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Assessment of fatigue as a late effect of therapy among survivors of childhood leukemia

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Assessment of fatigue as a late effect of therapy among survivors of childhood leukemia

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ASSESSMENT OF FATIGUE
AS A LATE EFFECT OF THERAPY
AMONG SURVIVORS OF CHILDHOOD LEUKEMIA
by
Kathleen Ann Meeske
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
EPIDEMIOLOGY
August 2003
Copyright 2003 Kathleen Ann Meeske
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UMI Number: 3116754
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UNIVERSITY O F SOUTHERN CALIFORNIA
THE GRADUATE SCHOOL
UNIVERSITY PARK
LOS ANGELES, CALIFORNIA 90089-1695
This dissertation, written by
c ■*-'-/ /-I - / 7'T c c Jr /<- c .
under the direction o f dissertation committee, and
approved by all its members, has been presented to and
accepted by the Director o f Graduate and Professional
Programs, in partial fulfillment of the requirements for the
degree of
DOCTOR OF PHILOSOPHY
Director
Date A u g u st 1 2 . 2Q03
Dissertation Committee
1 1
- U j l M - U a
hA
/s .
Chair
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DEDICATION
To my patients,
may all your dreams be realized.
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ACKNOWLEDGEMENTS
Leslie, thank you for sharing your wisdom and providing guidance over these
four years.
Brent and Julie, thank you for your patience and understanding.
Sarah and Pat, thank you for your ever-present support and encouragement.
Bill, thank you for always being at my side.
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TABLE OF CONTENTS
Page
Dedication ii
Acknowledgements iii
List of Tables vi
List of Figures vii
Abstract viii
I. Introduction 1
II. Background 3
A. Cancer Related Fatigue 3
B. Literature Review of Off-Treatment Fatigue in Cancer Patients 15
C. Overview of Acute Lymphoblastic Leukemia 29
D. Preliminary Study 45
III. Grant Application 53
A. Background and Rationale 53
B. Preliminary Study of Fatigue in Childhood Cancer Survivors 58
C. Study Design 59
D. Data Analysis 61
E. Sample Size Calculations 62
F. Budget 64
G. Cancer Control Potential 66
H. Justification for Funds 66
I. Plans for Subsequent Activity and Funding 67
IV. Manuscript: Fatigue in Long-Term Survivors of Childhood Leukemia 68
A. Introduction 68
B. Methods 69
C. Results 78
D. Discussion 99
E. Strengths and Limitations 110
iv
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Page
V. References H 2
VI. Appendices 129
A. Study Questionnaire 129
B. Medical Chart Abstraction Form 177
V
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LIST OF TABLES
Page
1. Key Points from Literature Review 26
2. Summary of Off-Treatment Cancer-Related Fatigue Studies 27
3. Possible Mechanisms of Fatigue Derived from Late Effects 42
4. Childrens Cancer Group Risk Group Classification for ALL 43
5. CCG ALL Protocols (1975-1995) 44
6. Demographic Data 46
7. Fatigue in Childhood Cancer Survivors, Pilot Data 47
8. Proposed Budget 64
9. Demographic, Diagnosis and Treatment Characteristics 79
of Eligible Subjects
10. Piper Fatigue Scores, Mean and Standard Deviations 83
11. Demographic Characteristics, Distribution and 85
Association with Fatigue and Depression
12. Disease and Treatment Factors, Distribution and 87
Association with Fatigue and Depression
13. Distribution of Fatigue and Depression by Self-Reported 90
Late Effects and Other Comorbidities
14. Multivariate Models for Fatigue and Depression 93
15. SF-36, Mean and Standard Deviations 97
vi
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LIST OF FIGURES
1. Conceptual Model Central Mechanisms of
Fatigue Following Cranial Irradiation
2. Pilot Data Compared to Breast Cancer
Patients on Treatment
3. Pilot Data Compared to Fatigue Scores in Breast
Cancer Patients
4. Pilot Data Compared to Fatigue in Breast
Cancer Patients
5. SF-36 Mean Scores, Fatigue and Non-Fatigue
Survivors Compared to US Norms
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ABSTRACT
Today, the five-year survival rate for childhood acute lymphoblastic
leukemia (ALL) exceeds 80%. Significant late effects, such as endocrine
abnormalities and cognitive impairments, have been well described in these cancer
survivors. Fatigue, a symptom reported by survivors of adult cancers many years
after completion of therapy, has never been studied in survivors of ALL. This study
estimated prevalence of fatigue, identified factors associated with fatigue and
explored the relationship between fatigue and quality of life in long-term survivors
of ALL.
Subjects were diagnosed with ALL at Childrens Hospital Los Angeles before
the age of 18 years between January 1, 1975 and December 31, 1995. One-hundred
sixty-one subjects who were disease-free, 18-41 years of age and off-treatment for an
average of 14 years (range 4-23) participated in a 45 minute telephone interview.
The Piper Fatigue Scale, Profile of Mood States, Rand SF-36 and the Symptom
Distress Scale were used to assess fatigue. Population norms were used for
comparison. Due to the high correlation between fatigue and depression,
multivariate logistic regression models were developed separately for fatigue and
depression.
Prevalence of fatigue among ALL survivors was 30%, consistent with the
rate reported for the general population. The final multivariate regression model for
fatigue included married (OR = 0.11, 95% Cl = 0.02-0.50); children (OR = 5.80,
95% Cl = 1.30-25.82); sleep disturbances (OR = 6.15, 95% Cl = 2.33-16.22); pain
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(OR = 5.56, 95% Cl = 2.13-14.48); obesity (OR=3.80, 95% Cl = 1.41-10.26);
cognitive impairments (OR=2.56, 95% Cl = 1.02-6.38); and exercise induced
symptoms (OR=2.98, 95 % Cl = 1.11-8.02) as significant factors. The final
multivariate model for depression included four of the seven factors associated with
fatigue: sleep disturbances, pain, obesity and cognitive impairment. Fatigue was
inversely related to quality of life with survivors who were both fatigued and
depressed reporting the poorest quality of life.
Prevalence of fatigue among survivors of ALL was within normal limits for
the general population. Survivors who report moderate levels of fatigue represent a
high-risk subgroup that needs further evaluation.
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I. INTRODUCTION
Over the past 30 years, remarkable strides have been made in the treatment of
childhood cancer. While childhood cancer was once a uniformly fatal disease, the
relative five year survival rate for children diagnosed under the age of 15 years is
now 75% (Ries, Kosary, Hankey, Miller and Edwards, 1997). This dramatic
improvement in survival has produced a growing population of cancer survivors.
Currently, there are more than 150,000 survivors of childhood cancer in the United
States. It is estimated that the prevalence of cancer survivors among young adults (15
to 45 years of age) will increase from one in 900 persons today to one in every 250
persons by the year 2010 (Bleyer, 1990).
As the number of long-term survivors of childhood cancer has grown,
attention has been directed increasingly toward treatment-related sequelae and their
effects on quality of life (Marina, 1997; Meadows, et al. 1991). In pediatric
oncology, a number of late complications of therapy such as second cancers,
hormone deficiencies, cardiac abnormalities and cognitive impairments are well
documented in the literature, while others have not yet been identified (Schwartz,
Constine, Hobbie and Ruccione, 1994).
Fatigue has recently been recognized as a potential late effect of therapy in
adult cancer survivors. Although the mechanisms have yet to be defined, many
adults (37%-78%) continue to experience significant fatigue long after the
completion of their cancer therapy (Stone, Richards, and Hardy, 1998). Several
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studies have found that the prevalence and intensity of fatigue is greater in survivors
of adult cancer than in the general population (Loge, Abrahamsen, Ekeberg and
Kaasa, 1999; Andrykowski, Curran and Lightner, 1998). This persistent fatigue
following cancer treatment has been shown to have a profound negative impact on
the adult patient’s quality of life (Bower, et al. 2000).
In our clinical program at Childrens Hospital Los Angeles (CHLA), we have
observed that long-term survivors of childhood cancer also report fatigue symptoms
that cause significant distress and interfere with normal functioning and quality of
life. To date, there are no publications in the literature on fatigue in long-term
survivors of childhood cancer. Fatigue has yet to be studied as a possible late effect
of therapy in survivors of childhood cancer. Because fatigue is not a recognized late
effect of therapy in pediatric oncology patients, fatigue symptoms often are ignored
and untreated.
This study will be the first to examine fatigue in long-term survivors of
childhood cancer, defined as patients who are free of disease and off-treatment for a
minimum of one year. The main objectives of this proposed study are (1) to estimate
the prevalence and severity of fatigue among adult survivors of childhood leukemia
(Acute Lymphoblastic Leukemia (ALL)) treated at CHLA from 1975-1995; (2) to
examine the relationship of fatigue to demographic and disease characteristics,
including treatment and late effects of therapy; (3) to explore the association between
fatigue and cranial irradiation; (4) to compare fatigue in survivors of ALL to fatigue
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in the general population (published norms) and adult cancer patients (literature); and
(5) to examine the association between fatigue and quality of life.
II. BACKGROUND
A. Cancer Related Fatigue
Only recently has the degree and magnitude of the problem of cancer related
fatigue (CRF) been recognized. With the more acute symptoms of nausea, vomiting
and pain now under better control, patients today identify fatigue as the most
distressing symptom associated with cancer and its treatment (World Health
Organization, 1990; Winningham, et al. 1994). In adults, CRF is described as the
most prevalent, under-recognized, untreated cancer side effect (Richardson, 1995).
Fatigue, similar to pain, is a subjective sensation that patients describe as a
feeling of tiredness, weakness, lack of energy, exhaustion, inability to concentrate,
lethargy and asthenia (Vogelzang, et al. 1997; Harpham, 1999; Glaus, 1998). CRF
differs from acute fatigue, a normal sensation that healthy people experience after a
period of exertion or insufficient sleep. CRF is a chronic symptom that persists
despite adequate rest and sleep and is “perceived as unusual, abnormal, or excessive
whole-body tiredness, disproportionate to or unrelated to activity or exertion” (Piper,
1993, p. 279). While acute fatigue serves a protective function, chronic fatigue
serves no known function. CRF has been shown to have a considerable impact on
quality of life, interfering with the individual’s ability to work, enjoy life, and
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interact with family and friends (Smets, et al. 1998; Ferrell, Grant, Dean, Funk B and
Ly, 1996). Recent data suggest that fatigue following cancer diagnosis and treatment
may not be an acute, temporary or time-limited symptom but rather a chronic
symptom resulting in significant disability (Nail and Winningham, 1995).
1. Definition of fatigue
There is no universally accepted definition of fatigue. It is a complex
phenomenon that has both physical and psychological components. CRF was
recently defined by The National Comprehensive Cancer Network as an “unusual,
persistent subjective sense of tiredness related to cancer or cancer treatment that
interferes with functioning.” (National Comprehensive Cancer Network, 2003, p 1.)
In 1999, CRF was accepted as a diagnosis in the International Classification of
Diseases, 10 Revision-Clinical Modification (Celia, Peterman, Passik, Jacobsen and
Breitbart, 1998).
2. Measurement of fatigue
There is no gold standard of measure of fatigue. Although a variety of valid
and reliable instruments have been developed to measure CRF in adult populations,
no instrument has yet been developed to measure fatigue in pediatric oncology
patients.
As a subjective phenomenon, fatigue requires measurement by self-report.
Although fatigue is multidimensional, its measurement is frequently restricted to
intensity, a single dimension (Piper, 1997; Irvine, Vincent, Bubela, Thompson and
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Graydon, 1991). Uni-dimensional scales are often (1) a single self report-item
incorporated into symptom distress/toxicity checklists or functional status scales (e.g.
Symptom Distress Scale (McCorkle and Young, 1978; Holmes, 1991)); (2) a multi
item subscale within a measure of health related quality of life or mood (e.g., Profile
of Moods, (McNair, Lorr and Droppleman, 1971); RAND SF-36 (Ware, Snow,
Kosinski and Gandek, 1993)). Several multi-dimensional instruments have been
designed to capture the multiple characteristics and manifestations of fatigue (e.g.,
Revised Piper Fatigue Scale (Piper et al. 1998); Fatigue Symptom Checklist
(Haylock and Hart, 1979); Multi-dimensional Fatigue Inventory (Smets, Garssen,
Bronke and deHaes, 1995); Functional Assessment of Cancer Therapy-Fatigue Scale
(FACT-F) (Celia, 1997)). The multi-dimensional measures are more sensitive and
effective than the unidimensional measures in detecting changes in fatigue over time
and group differences (i.e., differences between patient groups and differences
between patients and the general population).
A major challenge in defining fatigue is differentiating among causes,
indicators, and effects (e.g., fatigue, depression and cognitive impairment)
(Winningham, et al. 1994). Most studies find a positive correlation between
depression and fatigue (Glaus, 1998). Fatigue is a presenting symptom of depression.
While fatigue may also cause depression because of its negative impact on life, it can
occur in the absence of depression (Smets, Garrssen, Cull and deHaes, 1996; Visser
and Smets, 1998). In chronic fatigue it is very difficult to distinguish between cause
and effect (Grandjean, 1968). It is thought that fatigue and depression may be
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outcome indicators of one another. Because the directional relationship and
distinction between fatigue and depression are not clear, depression is an important
variable to consider in studies of fatigue in cancer patients (Glaus, 1998).
3. Prevalence of fatigue
Fatigue is a common problem in the general population with prevalence
estimates ranging from 23-30% for women and 13-20% for men (Shepard, Cooper,
Brown and Kalton, 1981). The prevalence of fatigue in cancer patients receiving
radiotherapy or chemotherapy is 70-100% (Irvine, Vincent, Bubela, Thompson and
Graydon, 1991; Winningham et al. 1994) and 37-68% for those patients who have
completed therapy and are cancer free.
The prevalence rate of fatigue is unknown in children who are being treated
or who have completed treatment for cancer. Since children differ from adults with
respect to their cancer diagnoses, treatments and late effects of therapy
(Hockenberry-Eaton, et al. 1998; Neglia and Nesbit, 1992; DeLat and Lampkin,
1992), one would expect CRF to differ among these cancer populations as well.
To compare fatigue in cancer patients to fatigue in the general population,
investigators have used published norms from questionnaires that have been
validated in healthy populations (Bower et al. 2000) or have included a cancer free
control group (Klee, Groenvold and Machin, 1997; Stone, Richards and Hardy,
1998). In this proposed study, norm-based instruments (POMS and RAND SF-36)
will provide comparison data. This methodology was used by Bower et al. (2000) in
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their study of fatigue in breast cancer survivors. In phase two of this study, we will
collect comparative data from cousins (age-matched and cancer free) of our long
term survivors.
4. Mechanisms (Theories) of fatigue
The etiology of CRF is complex and multi-factorial. Various theories have
been proposed to explain how fatigue occurs, yet the actual mechanisms that produce
fatigue are unknown.
a. Integrated fatigue model
Piper’s Integrated Fatigue Model (IFM) is the most frequently cited
theoretical framework on CRF (Piper, Lindsey and Dodd, 1987). This model
recognizes specific mechanisms of fatigue in the cancer population, such as, innate
host factors, symptom patterns, sleep wake patterns and disease patterns. These
mechanisms influence the subjective (perception) and objective (physiological,
behavioral and biochemical) indicators of fatigue. Since the correlation between
perception and most objective indictors is unknown, fatigue (like pain) is best
assessed by measuring the individual’s perception of fatigue. This framework
suggests that cancer related fatigue may be influenced by such factors as the patient’s
(1) age at diagnosis (innate host factors), (2) specific cancer diagnosis (disease
patterns), (3) specific treatment (treatment patterns) and (4) toxicities of therapy
(symptom patterns).
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The IFM was used to guide the development of The Piper Fatigue Scale, a
mutidimensional instrument that measures four subjective dimensions of fatigue in
cancer patients. In this study, we will use The Revised Piper Fatigue Scale, a shorter
form of the original scale, to measure fatigue (Piper, et al. 1989).
The etiology of fatigue for individuals receiving cancer therapy is likely to be
different than the fatigue experienced by those who are no longer receiving
treatment. For example, the acute toxicities of treatment (e.g., nausea and vomiting,
anemia) are likely to influence fatigue levels in patients receiving therapy, while late
toxicities (late effects) of therapy (e.g., endocrine dysfunction) are expected to play a
more dominant role in explaining CRF in individuals after treatment is completed.
Multiple late effects of therapy have been documented in our study population of
long-term survivors of childhood ALL.
b. Central-peripheral fatigue model
A neurophysiologic model of fatigue that has both central and peripheral
components has been proposed (Grandjean, 1968; Gibson and Edwards, 1985).
Impairment of the peripheral component results in impaired peripheral nerve
function, affecting neuro-muscular transmission at the motor end plate. Impairment
of the central component (psyche/brain and spinal cord) causes “lack of motivation,
impaired spinal cord transmission, and exhaustion or malfunction of the brain cells in
the hypothalamic region.” (Poteliakhoff, 1981, p. 91).
The central nervous system (CNS) component plays a significant role in the
perception and modulation of fatigue. In the literature on exercise, muscular fatigue
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makes itself evident as a sensation of weariness in the cortex. The central
mechanisms, perception of fatigue and motivation, have been found to have a
profound effect on performance endurance (Devis and Bailey, 1997). It is
hypothesized that exercise-induced alterations in the neurotransmitter function
produces a CNS fatigue that results in reduction of corticospinal impulses reaching
the motomeurons (Enoka and Stuart, 1992). Neurotransmitters identified as being
potentially involved in fatigue during prolonged exercise are serotonin (5-HT),
acetylcholine, dopamine and cytokines (Mannering and Deloria, 1986). In animal
studies, increases in brain 5-HT are associated with lethargy and loss of motor drive
(Jouvet, 1967), while an increase in brain dopaminergic activity increases endurance
performance (Chaouloff, Merino, Serrurier, Guezennec andElghozi, 1987).
Dopamine activity is also known to influence sleep and mood.
Since fatigue can be caused by disorders in neurotransmission, it has been
hypothesized that neurotoxic therapies, such as cranial irradiation and drugs that
cross the blood/brain barrier, may be more likely to produce fatigue than other
therapies (Piper, Lindsey and Dodd, 1987). Central mechanisms have been
implicated in the extreme fatigue observed in patients with cancer who are receiving
biotherapy or have recently completed cranial irradiation (Piper, et al.1989).
(1) CNS fatigue and biotherapy
Fatigue is common and often a dose- or treatment-limiting toxicity of
biotherapy, i.e., interferon, interleukein, and tumor necrosis factor. Biotherapy
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exposes patients with cancer to exogenous and endogenous cytokines. Fatigue
associated with biotherapy is believed to reflect CNS toxicity of the frontal lobe
(Quesada, Talpaz, Rios, Kurzrock and Gutterman, 1986). It has been proposed that
fatigue may arise from the direct effect of cytokines on the frontal lobes, from
neurotransmitter abnormalities, or from alterations in hypothalamic pituitary adrenal
axis (HPA neuro-endocrine abnormalities) (Besedovsky, del Rey, and Sorkin, 1985),
brain electrical activity (Krueger, Wlater, Dinarello, Wolff and Chedid, 1984), and/or
blood brain barrier permeability (Denicoff, et al. 1987). Similar mechanisms
(cytokine activation) have also been postulated in the significant fatigue observed in
patients with HIV (Grady, Anderson and Chase, 1998) and chronic fatigue syndrome
(Komaroff, 2000).
(2) CNS fatigue and cranial irradiation
Fatigue (somnolence syndrome) is a sub-acute side effect of cranial
irradiation that occurs in approximately 58% of patients four to eight weeks after
cranial irradiation (Littman, et al. 1984). Symptoms range from mild fatigue to
excessive sleep (up to 20 hours/day). Somnolence syndrome is transient and a self
limited event (Trautman, et al. 1988) that is thought to be the result of radiation-
induced transient disruption of myelination (Gertz, 1996). In adult patients,
depression has been described as a component of this syndrome and is thought to be
related to the dysregulation of neurotransmitters (dopamine and serotonin) (Proctor,
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Kemahan and Taylor, 1981). These findings suggest a centrally mediated mechanism
for both fatigue and depression.
(3) CNS fatigue, cranial irradiation and cognitive impairment
Many long-term survivors of childhood ALL who were treated with cranial
irradiation have cognitive impairments (Gamis and Nesbit, 1991). Fatigue and
cognitive impairment are linked, although the mechanisms underlying this
association are unclear. Impairment in cognitive functioning, such as decreased
attention and impaired perception and thinking, is associated with fatigue
(Yoshitake, 1978; Grandjean, 1968). Individuals with cancer who experience fatigue
also report cognitive impairments (difficulty thinking, forgetfulness, and loss of
concentration) (Rhodes, Watson, and Hanson, 1988; Oberst and James, 1985).
Directed attention requires mental effort and it is hypothesized that when mental
demands exceed available capacity, individuals are at risk for attentional fatigue that
results in a decreased capacity to concentrate or pay attention (Kaplan and Kaplan,
1982). Attentional fatigue is different than physical fatigue and has been described in
adult patients undergoing cancer therapy (Cimprich, 1992).
Cognitive impairment, specifically deficits in attention, is a well described
late-effect of cranial irradiation. Neuro-psychologists, while evaluating long-term
survivors of childhood ALL for cognitive deficits following cancer therapy, have
observed a fatigue effect that factors into the child’s poor school performance
(Brouwers, 1987; Lockwood, Bell and Colegrove, 1999). In these studies,
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investigators have been unable to distinguish between cause and effect, i.e., whether
fatigue causes poor performance or whether cognitive impairments produce fatigue.
In long-term survivors of ALL, many investigators have studied cognitive
functioning but none have evaluated its relationship to subjective fatigue.
(4) CNS fatigue in survivors of ALL
This central mechanism of fatigue is important to this proposed study because
all patients with ALL will have received some form of CNS therapy (i.e.,
chemotherapy with or without cranial irradiation). Further, significant and numerous
structural and functional late effects (neuro-toxicities) of CNS therapy have been
documented in long- term survivors of ALL, especially among those patients who
have received cranial irradiation. Structural late effects include cortical atrophy,
mineralizing microangiography and demyelination. Functional late effects include
neuro-endocrine abnormalities and cognitive impairments. In a recent study of long
term survivors of ALL, investigators report an association between mood
disturbances and cranial irradiation (Chen, et al. 1998). It is proposed that structural
changes, such as subcortical atrophy following cranial irradiation, can upset
neurotransmitter-neuroreceptor ratios and induce depression (Simpson, Baldwin,
Jackson and Bums, 1998). Although it has been suggested that there may be a
relationship between neuro-toxic cancer treatments and fatigue, little has been done
in this area. No one has examined the relationship between cranial irradiation and
fatigue in long-term survivors of cancer (Piper, Lindsey and Dodd, 1987; Meyers,
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2000). We are proposing in this study that cranial irradiation during childhood and
its related late effects may put this group of survivors at greater risk of fatigue (see
conceptual model, Figure 1).
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Figure 1.
Conceptual Model Central Mechanisms of Fatigue Following Cranial Irradiation
Structural late effects
of cranial irradiation
cortical atrophy
vascular damage
white matter destruction
Hypothalamus (HPA)
(neuro-endocrine disorders:
cortisol, growth hormone,
thyroid, sex hormones)
Dysfunction of FATIGUE
neurotransmitters ^ (limbic system:
dopamine, serotonin mood, motivation,
memory, sleep)
Neuro-cognitive impairments
Deficits in attention, sequencing, memory
B. Literature Review on Off-Treatment Fatigue in Cancer Patients
The majority of the studies on cancer-related fatigue focus on adult patients
who are currently receiving cancer therapy. Only a few studies have examined
fatigue in cancer patients who are disease-free and no longer receiving treatment
(off-treatment fatigue). These few published studies on off-treatment fatigue have
all been restricted to patients who had cancer while they were adults. Although no
study of fatigue in long-term survivors of childhood cancer has been published, an
abstract on fatigue in childhood cancer survivors was presented at the International
Society of Pediatric Oncology (2000). Thus, this literature review of off-treatment
fatigue will include publications that are limited to adult cancer patients and the
recent SIOP abstract on childhood cancer survivors.
In a study of long-term survivors of Hodgkin’s disease (n=403), Fobair et al.
(1986) found persistent energy loss in 37% of the survivors with a median follow-up
of nine years. Patients whose energy had not returned to normal were older and had
more advanced disease at diagnosis, had received combined-modality therapy
(chemotherapy and irradiation) and had higher depression scores (p=0.001). In a
sample of women who had received adjuvant chemotherapy for breast cancer, Knobt
(1986) found fatigue to be the most distressing physical symptom experienced by
women two to sixty months following treatment (n=28). Fatigue was also the most
frequently endorsed symptom in a larger sample of women (n=382) with breast
cancer two to ten years after the completion of therapy (Bergland, Bolund,
Fomander, Rutquist and Sjoden, 1991). Seventy-six percent of the women reported
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fatigue symptoms. Patients who had received radiotherapy reported decreased
stamina more frequently than did chemotherapy patients. For the women who had
received chemotherapy, fatigue increased as time after treatment increased. This
relationship was not present for the women who had received radiotherapy.
Similar results were found in a study of bone marrow transplant (BMT)
patients (n=200) who were at least 12 months post-BMT (mean 43 months).
Andrykowski, et al. (1995) found that the majority (78%) reported “feeling tired”.
Sixty-four percent indicated that their current energy level was lower than it was
before their cancer diagnosis. In this study, which used a 30 item symptom checklist
to evaluate symptom distress, cancer free BMT recipients endorsed “feeling tired”
most frequently and rated it as their most severe symptom.
These initial studies suggest that fatigue is a persistent and significant
problem for many adult cancer survivors long after completing curative therapy and
may be related to age at diagnosis, stage of disease at diagnosis, time since the last
treatment, specific cancer treatments and psychological factors such as depression.
These studies have significant limitations, however, including a crude, frequently
single-item, assessment of fatigue.
In the next series of off-treatment cancer-related fatigue studies, investigators
have used multiple measures (validated and reliability tested) to assess fatigue,
included comparison groups and adjusted for potential confounders, such as
depression, sleep disturbances and body mass index (BMI).
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1. Andrykowski Study
In a study by Andrykowski, Curran, and Lightner (1998), women with breast
cancer (BC) (n=88; mean 28 months post-treatment) and age-matched women
(n=88) with benign breast problems (BBP) completed a set of fatigue questionnaires
at initial assessment and a four month follow-up assessment. The investigators used
multiple instruments to measure fatigue: (1) Piper Fatigue Scale (multi-dimensional
measure, 35 items), (2) Medical Outcome Studies Short-Form General Health
Survey (MOS-36) vitality subscale (unidimensional scale, 4 items), (3) Chandler
Fatigue Scale (11 items, physical and mental dimensions of fatigue) and (4) two
single items (“feeling tired” and “feeling weak”). The Center for Epidemiological
Studies Depression Scale (CES-D) and Pittsburgh Sleep Quality Index were used to
evaluate depression and sleep, respectively.
The BC group reported statistically significant more fatigue, more weakness,
and less vitality than the BBP group at both assessment times (p<0.05). All analyses
were adjusted for body mass index (BMI) because groups differed on BMI and BMI
was related to fatigue. No relationship was found between fatigue and stage of
disease, type of treatment or time since treatment. In the BC group, income and
education were negatively related to fatigue.
While depression and sleep disturbances were correlated with fatigue
measures at both assessments (depression, r=.47-.68; sleep, r=.38-.51), the BC and
BBP groups did not differ as to depressive symptoms or on the global index of sleep
disturbance. However, the BC and BBP groups did differ significantly on the
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subscale measuring sleep quality, suggesting poorer sleep may be a possible
mechanism underlying off-treatment fatigue in cancer survivors.
2. Broeckel Study
In a comprehensive study by Broeckel, Jacobsen, Horton, Balducci and
Lyman (1998) fatigue reported by 61 women with breast cancer who had completed
chemotherapy (mean time off-treatment =1.3 years) was compared to fatigue
reported by 59 women with no history of cancer. Peer nomination procedures were
used to recruit an age matched non- cancer comparison group.
Participants completed several self-report fatigue measures: (1) Profile of
Mood States Fatigue subscale (unidimensional measure, 7 items), (2) Fatigue
Symptom Inventory (unidimensional, 12 items), (3) Multidimensional Fatigue
Symptom Inventory (multi-dimensional, 83 items), (4) Fatigue Catastrophizing Scale
(10 items, scale modified for this study). Sleep quality (Pittsburgh Sleep Quality
Index) and menopausal symptoms (Menopausal Symptom Checklist) were also
evaluated. A structured clinical interview (Structured Clinical Interview for
Diagnostic and Statistical Manual of Mental Disorder, DSM-IV) was administered
over the telephone to identify current and past psychiatric disorders (anxiety,
adjustment or mood disorders).
In this study, breast cancer survivors reported fatigue of greater severity,
duration and disruptiveness than non-cancer subjects (p<0.01). The average level of
fatigue experienced by women previously treated with adjuvant chemotherapy was
approximately 50% greater than that reported by the comparison group of women
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with no history of cancer. When compared with controls, breast cancer patients
reported that fatigue interfered to a greater extent with their overall quality of life
and their ability to work and concentrate.
Among former chemotherapy patients, fatigue severity was related to sleep
difficulties, menopausal symptoms, use of catastrophizing as a coping strategy, and
the presence of an anxiety, mood or adjustment disorder. None of the demographic
variables (age, marital status, ethnicity, education or employment status), medical/
treatment variables (menopausal status, disease stage, time since diagnosis, time
since treatment completion, length of chemotherapy treatment, type of surgery,
additional treatment with radiation therapy or current use of tamoxifen) or the
presence of a psychiatric disorder before cancer diagnosis was significantly related to
fatigue. Unfortunately, the authors of this study did not report or analyze data on
sleep disorders, menopausal symptoms, reliance on catastrophizing, and psychiatric
disorders (present and past) in the non-cancer group.
3. Loge Study
In a large Norwegian study of off-treatment fatigue, survivors of Hodgkin’s
Disease (HDS)(n=557) were compared to population based controls (n=2213)
(Loge, Abrahamsen, Ekeberg and Kaasa, 1999). The Hodgkin’s patients were treated
at a single institution between 1971-1991, with a mean time off-treatment of 12
years. Controls were randomly selected from the Norwegian National Register.
Subjects were mailed self-report questionnaires. The Fatigue Questionnaire (FQ) (11
items) was used to assess total fatigue (TF) (11 items), mental fatigue (MF) (4 items)
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and physical fatigue (PF) (7 items). Investigators used two scoring systems, Likert
(0,1,2,3) and dichotomized (0,0,1,1), to evaluate fatigue. Patients were defined as
fatigued (a case) when their total dichotomized fatigue score was greater than four.
The Hospital Anxiety and Depression Scale (HADS) was used to measure anxiety
and depression. Controls completed an additional questionnaire that assessed their
past and present health problems.
Fatigue levels for the HDS were highest among the oldest subjects (p for
trend <0.001) but did not differ by sex. In the univariate analyses, none of the
disease/treatment characteristics (years since diagnosis, disease severity, type of
primary treatment, relapse) were significantly associated with fatigue. In the logistic
regression analysis, age and stage of disease were significant predictors of fatigue.
Although measured, no results were reported for depression or anxiety.
The HDS had statistically significantly higher fatigue scores (TF, PF and
MF) than the controls (p<0.001). The HDS fatigue scores were nearly 20% higher
than the controls and equal to the fatigue reported by the subset of controls with the
poorest health. The number of patients with fatigue (cases) were nearly two times
greater in the HDS group than in controls. In multiple regression analysis, after
adjusting for age, sex, and education level, HDS status significantly predicted fatigue
(p<0.0001).
When investigators asked subjects how long their fatigue had lasted, 33% of
the controls and 10% of HDS reported that their fatigue had lasted for one week or
less while 61% of the HDS and 31% of the controls stated that their fatigue had
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lasted six months or longer (pcO.OOl). The authors of this large cross-sectional study
conclude that fatigue following curative therapy is not an acute, temporary or time-
limited symptom, but rather a chronic late effect of HD and treatment.
4. Smets Study
In contrast to the previous findings, Smets, et al. (1998) found that fatigue
levels in disease-free cancer patients who had been treated nine months earlier with
radiotherapy did not differ significantly from the general population. Controls were
randomly selected from telephone directories matched to the cancer patient’s
residential area. Fatigue was measured with the Multidimensional Fatigue Inventory
(MFI-20) and several additional questions specifically developed for this study. One
hundred and fifty-four cancer patients with a variety of malignancies were
interviewed; 48 controls were interviewed while an additional 93 controls completed
mailed questionnaires. No differences in fatigue scores were found between the two
samples while controlling for age and gender. Although fatigue scores did not differ
between the groups, differences were seen with the study specific questions on
fatigue. The patient group described its fatigue as more unpredictable and chronic
than the reference group.
In this study, none of the medical (diagnosis, prognosis, comorbidity) or
treatment-related variables (total radiation dose and fractionation) predicted fatigue
at follow-up. In the final multiple regression analysis, four variables (gender r = .23,
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pretreatment fatigue r=. 54, functional disability r = .09 and pain r =.02) significantly
predicted fatigue at nine month follow-up. Women reported more fatigue than men.
The authors of this study suggest that a response shift may partially explain
their unexpected findings, i.e., no significant difference in fatigue scores between
disease-free patients and the reference group. They propose that the patients’
experience of fatigue during radiotherapy may have changed their standard of
measurement, complicating the interpretation of comparison data. It is possible that a
response shift may have played a significant role in this study because the patients’
time off-treatment was relatively short (nine months) when compared to the other
studies that have examined off-treatment fatigue. A response shift is likely to be
greater and more problematic when follow-up is of short duration.
The patients in this study were diagnosed and treated for a variety of cancers,
while all other studies on off-treatment fatigue have limited their samples to one
cancer diagnosis. The heterogeneity of this study population may also partially
explain why these results differ from those studies that have used a single cancer
diagnosis.
5. Bower Study
In a large survey study, Bower, et al. (2000) studied off-treatment fatigue in
breast cancer survivors (BCS)(n=1957). Women were eligible for this study if they
had been diagnosed with early stage breast cancer (stage 0,1, I I ), were one to five
years after initial diagnosis, currently disease-free and not receiving any cancer
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therapy other than tamoxifen. “More than 50% of the women” who responded to the
initial invitation letter participated in the study. The RAND SF-36 was used to
measure fatigue and quality of life. Valid and reliable measures were used to assess
sleep, depression and menopausal symptoms. Demographic and medical data were
collected by self report. No chart data was collected.
Fatigue scores in BCS were compared to those observed in two reference
groups: national norms for age-matched women in the general population (n=1053)
(Ware, Snow, Kosinski and Gandek, 1993) and baseline data from women at high
risk for breast cancer (n=9,749) (Ganz, Day, Ware, 1995). Mean fatigue scores for
the BCS group fell well within the normal range, slightly higher than those reported
by the group at high risk for breast cancer.
Investigators identified a subset of BCS who reported more severe and
persistent fatigue. When BCS were classified as fatigued at or below the fatigue
midpoint score of 50, 684 were classified as fatigued and 1269 were classified as
non-fatigued. Women in the fatigued group were slightly younger, had lower
incomes^ were less likely to be married and had higher rates of non-cancer related
medical problems (arthritis, headache and heart disease). Fatigue was greater in
women who had received chemotherapy and lowest among those who had received
radiation or surgery alone. After one year, this association with treatment was no
longer significant. A pattern of fatigue was observed in these patients with energy
levels being relatively low at one year, increasing during the second year and then
remaining relatively stable over the next three years. Women who were fatigued
23
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reported more pain, menopausal symptoms, sleep disturbances and depression. In
multivariate analyses, depression and pain emerged as the strongest predictors of
fatigue after controlling for age, arthritis, and high blood pressure. Individuals who
were more fatigued rated their quality of life at a lower level.
The investigators examined the complex relationship between fatigue and
depression. Some patients were fatigued and not depressed, while others were
depressed and not fatigued. Fatigued/not depressed patients had greater impairment
in physical health and poorer general health, while women in the depressed/
nonfatigued group demonstrated greater impairment in mental health, emotional
well-being and poorer social functioning. These investigators found that fatigue,
although closely related to depression, was not equivalent to depression.
Although the investigators conclude that BCS in this study are not more
fatigued than age-matched group women from the general population, this study has
some significant limitations. Eligibility was limited to women diagnosed with early
stage breast cancer. This study excludes BCS who were diagnosed with more
advanced disease and who received more intensive therapies, i.e., a group that may
be more likely to experience fatigue following the completion of therapy. These
findings cannot be generalized to the total population of BCS. Furthermore, a large
percentage of eligible BCS did not participate in the study. It is possible, although
unknown, that those who were most fatigued did not have the energy to participate in
the study. As a result, fatigue in this population of cancer survivors may be
underestimated.
24
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6. Langeveld Study
Lange veld et al. (2000) presented an abstract on fatigue and depression in
long-term survivors of childhood cancer. Subjects treated in the Netherlands (n=471,
mean age 24 years, average time off therapy was 15 years, treated in the
Netherlands) and an age-matched reference group (n=1087) with no history of cancer
completed the Multidimensional Fatigue Inventory and the CES-D. No significant
group differences were found for fatigue or depression. In both groups, female
gender and depression were identified as predictive factors in explaining fatigue. In
the cancer survivor group, reduction in activity and mental fatigue were partly
explained by radiotherapy treatment (adjusted odds ratios respectively were 1.79 and
2.02). Site of radiotherapy was not reported. Depression was partially explained by
the duration of treatment (odds ratio: 1.85). This study, the first to report on fatigue
in childhood cancer survivors, found no significant group difference in fatigue or
depression when cancer survivors were compared to healthy controls. As with
Smets’ study (1998), the patients in this study were diagnosed and treated for a
variety of cancers. This heterogeneity of the study population may partially explain
the absence of group differences. Although this study finds some relationship
between radiotherapy and fatigue, radiotherapy in this population is not limited to the
CNS and thus does not test the relationship between cranial irradiation and fatigue.
Table 1 and 2 provide a summary of the literature review on off-treatment
fatigue in cancer patients.
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Table 1. K ey Points from Literature R eview
1. Studies suggest that many cancer patients experience significant fatigue many years after the
completion of cancer therapy
2. Some but not all studies find fatigue in cancer patients following curative therapy greater than
fatigue in the general population
1. Investigators have used published norms and controls when comparing fatigue in cancer
survivors to fatigue in the general population
2. Valid and reliable instruments have been developed specifically to measure fatigue in adult
cancer patients
3. Possible covariates of fatigue in survivors of adult cancers include:
Demographic factors: age, income, education, gender, body mass index
Disease factors: stage of disease, type of treatment (no study has included a cancer
population that has received CNS therapy)
Sleep
Pain
Other medical conditions including cardiac, arthritis and functional disability
Menopause symptoms
Psychiatric disorders
4. In most studies, length of therapy is not related to off-treatment fatigue, suggesting that fatigue
following cancer is a chronic condition
5. Depression is closely related to off-treatment fatigue but is not equivalent to fatigue.
6. Fatigue is negatively related to quality of life (QOL)
7. Fatigue in survivors of leukemia (adult or childhood) has never been studied
8. Fatigue in cancer survivors who have received CNS therapy has never been studied
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Table 2. Summary of Off-treatment Cancer-Related Fatigue Studies
Reference/sample Fatigue Measure(s)
Knobt (19861
Breast cancer (n=28)
Off therapy (mean=28 mos)
Fobair. etal. (1986)
Hodgkin’s disease (n=403)
Off-treatment (median=9 yrs)
Symptom Distress Scale
2 questions developed
by investigators
Bereland. etal. (19911
Breast cancer (n=382) single item developed
Off-treatment (2-10 yrs) by investigators
Andrykowski. etal. (1995)
BMT (200)
Off-treatment (mean=43 mos)
Andrykowski. et al. (1998)
Breast cancer (BC) (n=88)
Off-treatment (mean=28 mos)
Comparison Grp (n=88)
Broeckel. etal. (1998)
Breast cancer (BC) (n=61)
Off-treatment (mean=1.3 yrs)
Comparison Grp (n=599)
Symptom Experience
Report
Piper Fatigue Scale
SF-36, vitality subscale
Chandler Fatigue Scale
Two items developed by
investigators
POMS
Fatigue Symptom Inventory
Muliti-Dimensional Fatigue
Fatigue Catastrophizing Scale
Symptom Inventory
to
Findings
(1) fatigue, most distressing
symptom
(1) 37%, energy had not return
to normal
(1) fatigue, most frequently endorsed
symptom
(1) 78% feeling tired
(2) 64% energy less than before BMT
(1) fatigue, BC >BBP
(2) BMI= covariate
(3) in BC patients, negative
correlation between fatigue
and income, education
(1) fatigue, BC>control
(2) (BC) fatigue related to sleep
difficulties, menopausal symptoms, catastrophizing
coping strategies, and psychiatric disorders
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Table 2. Summary of Off-treatment Cancer-Related Fatigue Studies (continued)
Reference/sample Fatigue Measure(s) Findings
Loge et al. (1999)
Hodgkin’s (HDS) (n=557)
Off-treatment (mean=12 yrs)
Comparison Grp (n=2,213)
Smets et al. (1998)
Cancer patients
following XRT (n=145)
Comparison Grp (n=141)
Bower, et al. (2000)
Breast Cancer (n=1,957)
Time since dx (mean=2.9 yrs)
Comparison Grps:
(1) (n=l,053)
(2) (n=9,749)
Langeveld. et al. (2000)
Childhood Cancer (n= 417)
Comparison Grp (n=1087)
Norwegian Fatigue Questionnaire (1) fatigue, HDS>controls
(2) predictors of fatigue (HDS) =age and stage of disease
Multidimensional Fatigue
Inventory
SF-36, vitality subscale
Multidimensional Fatigue
Inventory
(1) fatigue, no group difference
(2) predictors of fatigue=gender and
functional disability and pain before XRT
(1) fatigue, no group difference
(2) predictors of fatigue (BC)= depression, pain and sleep
disturbance
(1) fatigue and depression, no group differences
(2) predictors of fatigue=gender and radiotherapy
to
oo
C. Overview of Acute Lymphoblastic Leukemia (ALL)
This study will assess subjective fatigue in individuals who were diagnosed
with ALL at CHLA between the years 1975-1995, before the age of 18 years and
who at the time of recruitment are 18 years or older, free of disease and off cancer
treatment for a minimum of one year. Eligibility for this study is restricted to ALL,
one specific cancer diagnosis, because fatigue, like other late effects, is thought to be
disease and treatment specific (Cleeland and Wang, 1999). The etiology and
prevalence of fatigue in long-term survivors of ALL are expected to be different
from those observed in long-term survivors of other childhood cancers. Because
ALL is the most common malignancy of childhood and survival rates are high, will
provide a relatively large population of long term survivors compared to other
childhood cancers.
Leukemia is the most common malignancy in children less than 15 years of
age. ALL represents 75% of these cases. Approximately 1500 new cases of ALL
occur in the United States each year (Smith, Ries, Gurney and Ross, 1999). Peak
incidence of ALL occurs between the ages of 2 and 3 years. Incidence is greater in
males than in females and is almost twice as common in white children than in black
children (Sather, 1986).
ALL is characterized by an uncontrolled proliferation of immature lymphoid
cells. Presenting signs and symptoms result from either bone marrow failure or the
infiltration of extramedullary sites by leukemia cells. The most common symptoms
at diagnosis are pallor, fever, bleeding, and bone and joint pain (Simone, Verzosa
and Rudy, 1975).
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1. Treatment of ALL
CHLA is a member institution of the national pediatric cooperative group,
Childrens Cancer Group (CCG). The vast majority of children diagnosed with ALL
at CHLA are entered in CCG clinical trials and treated according to a standard
protocol. If there is no open clinical trial at the time of a child’s diagnosis, that child
usually receives “standard” CCG therapy. Since the subjects in this study were
diagnosed from 1975 to 1995, the following discussion of treatment of ALL is
specific to CCG studies open during that time period.
At diagnosis, patients with ALL are stratified into prognostic groups
according to clinical and laboratory features, i.e., gender, age at diagnosis, initial
white blood cell count (WBC), platelet count, and FAB morphology. In CCG,
therapy is tailored to the child’s risk of disease recurrence. High risk patients receive
intensive therapy while low risk patients receive minimal therapy. This tailored
approach has improved survival while decreasing toxicities and late effects for the
low risk patients.
ALL therapy is divided into specific phases: remission induction,
intensification-consolidation, prevention of overt CNS disease and maintenance of
remission. Combination chemotherapy is the primary therapeutic modality.
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a. Central Nervous System (CNS) Preventive Therapy
CNS preventive therapy is based on the assumption that undetectable CNS
leukemia is present in most patients at the time of diagnosis (Pavlovsky, et al. 1983).
In the 1960s, when the CNS was left untreated, CNS relapse rates after remission
induction exceeded 75% (Evans, Gilbert and Zandstra, 1970). The CNS acts as a
sanctuary site in which leukemia cells reside, protected by the blood-brain barrier
from the systemically administered chemotherapy. Early studies found that when
therapy was directed specifically at the CNS (2400 cGy of cranial irradiation and
intrathecal (IT) methotrexate) CNS relapses were reduced to less than 10% (Aur, et
al. 1971). Although effective in reducing relapse and mortality, the long-term
sequelae of cranial irradiation was significant. Documented adverse late effects of
cranial irradiation include brain abnormalities on CT scans, neuro-psychologic and
neuro-endocrine impairments and second cancers.
To avoid the long-term sequelae of cranial irradiation, CCG protocols
(therapeutic approach) over time have attempted to eliminate cranial irradiation,
replacing it with varying doses and schedules of IT/systemic chemotherapy.
b. Length of therapy
In CCG protocols, maintenance therapy for ALL (combination
chemotherapy) continues for two to three years. In the more recent protocols, length
of therapy is longer for males than females. Males are given an additional year of
therapy because of their higher incidence of late relapses.
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c. Relapse therapy
The most common sites of relapse are the bone marrow, CNS and testes
(Poplack, 1993). Relapsed patients receive aggressive reinduction chemotherapy and
an additional two to three years of intensive systemic chemotherapy or bone marrow
transplant. Patients with a CNS or testicular relapse require local treatment in
addition to systemic therapy. Testicular disease is treated with bilateral testicular
radiotherapy (2400cGy) (Meister and Meadows, 1993). A variety of therapeutic
approaches have been used to treat CNS disease; most include cranio-spinal
irradiation (2400 cGy cranial and 1200-1800 cGy spinal) with maintenance IT
chemotherapy. A number of patients who relapse will achieve long term remission
and be cured of their disease. Such patients will be eligible for this study.
2. Survival
Over the past 40 years, prognosis has dramatically improved for children
with ALL. Once a uniformly fatal disease, long-term survival rates for childhood
ALL are now approaching 80% (Pui, 1995). As the number of childhood cancer
survivors grows (an estimated 1200 new survivors/year), attention is increasingly
being directed to the evaluation, management and prevention of late effects of
treatment.
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3. Late effects of ALL therapy
Late effects are delayed toxicities that occur months to years after the
successful completion of cancer therapy. They tend to be chronic (irreversible) and
progressive.
a. Central nervous system (CNS)
With childhood leukemia, the late effects that affect the CNS have been of
greatest concern. CNS preventive therapy has been associated with abnormal CT
brain scans, neuro-psychological deficits, second tumors and neuro-endocrine
abnormalities (Poplack, 1993).
Although the exact physiological mechanisms are not fully understood,
cranial irradiation and methotrexate have been identified as the major causes of
delayed CNS damage. Computed tomography (CT) brain scan abnormalities
including ventricular dilatation and widening of the subarachnoid spaces (cerebral
cortical atrophy), decreased attenuation coefficient (white matter hypodensity
indicating localized edema, cytokine expression and demyelination), and
intracerebral calcifications (mineralizing microangiography) have been detected in
ALL patients following treatment with cranial irradiation and IT chemotherapy
(Bleyer and Griffin, 1980; Fletcher and Copeland, 1988; Hopewell, 1998). The basal
ganglia (caudate) is the site of most intracerebral calcifications (Brouwers, Riccardi,
Fedio and Poplack, 1985). Peylan-Ramu, Poplack, Pizzo, Adomato, and Di Chiro
(1978) reported a 53% prevalence rate of intracranial abnormalities in CT scans of
33
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32 children with ALL who had received combination chemotherapy and cranial
irradiation.
The incidence of CT abnormalities correlates with the intensity of CNS
therapy, with the highest incidence among patients who have received 2400 cGy
irradiation plus maintenance IT methotrexate therapy. IT methotrexate is
synergistically neurotoxic when combined with cranial irradiation (Bleyer, 1998). It
is postulated that cranial irradiation breaks down the blood brain barrier, allowing for
both greater penetration and slower clearance of methotrexate, i.e., increasing
toxicity (Bleyer, 1981). CT abnormalities have been observed in asymptomatic
patients and may first appear as late as seven years after CNS irradiation.
(1) Neuro-psychologic (NP)
Cranial irradiation is associated with cognitive dysfunction. Impairments in
memory, attention, academic achievement, intelligence (a decline of 10-15 IQ
points) and increased distractibility have been documented in long term survivors of
ALL who have received cranial irradiation (Copeland, et al. 1985; Mulhern,
Wasserman, Fairclough and Ochs, 1988). Non-verbal skills are most vulnerable.
Although correlations between CT abnormalities and NP function are not
consistently high, studies have documented a significant association between
cerebral calcifications in the basal ganglia and deficits in attention, concentration and
distractability (Mulhern, et al. 1992). It has been postulated that these impairments,
which originate in the frontal lobe, may be the result of neurotransmitter
dysfunctions (Brouwers, Riccardi, Fedioi and Poplack, 1985).
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In a sample of 18 children who had received 2400 cGy of cranial irradiation,
72% required tutoring or special education placement (Peckham, Meadows, Bartel
and Marrero, 1988). In a similar study, 47% (n=15) required special educational
services following 1800 cGy of cranial irradiation (Peckham, 1991). This dose
dependent relationship is not a consistent finding (Butler, Hill, Steinherz, Meyers
and Finlay, 1994). Females and children who were treated under the age of six are at
greatest risk of cognitive impairment (Waber, et al. 1990; Butler and Copeland,
1993). Neurocognitive deficits take two to three years to manifest themselves
(Meadows et al. 1981).
Results have been mixed concerning NP late effects of IT methotrexate in the
absence of cranial radiation. Most studies do not find an association between IT
methotrexate (with or without systemic methotrexate) and NP deficits if cranial
irradiation is not also administered (Copeland, Moore, Francis, Jaffe and Culbert,
1996) In the few studies that do report an association, cognitive deficits tend to be
mild, limited to specific areas of cognitive functioning and appear to be dose related
(Lockwood, Bell and Colegrove,1999; Butler, Hill, Steinherz, Meyers and Finlay,
1994).
(2) Neuro-endocrine
Neuro-endocrine abnormalities, primarily involving the hypothalamic
pituitary axis (HPA), have been documented in children who have received cranial
irradiation. The doses of radiation delivered in the leukemia protocols
35
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(1800-2400 cGy) affect the hypothalamus, whereas higher doses (3-4,000 cGy)
impair the pituitary gland (Shalet, Beardwell, Pearson and Jones, 1976). Growth
hormone deficiency (GHD) resulting in short stature is the most common neuro
endocrine disturbance seen in children with ALL who have received 18 cGy or more
of cranial irradiation (Sklar, 1994). Rappaport and Brauner (1989) found that 56%
of the children with ALL who had received 24 cGy cranial irradiation had decreased
growth hormone output measured by response to provocative stimuli or by analysis
of basal pulsatile growth hormone secretion. GHD is directly related to radiation
dose with the incidence of problems increasing over time. Females and younger
children are at greater risk. Growth hormone replacement therapy is recommended
for children with documented GHD.
(3) Precocious puberty
Precocious puberty has been observed in patients who have had cranial
irradiation (Leiper, Stanhope, Ketching and Chessells, 1987). These patients exhibit
secondary sex characteristics at an early age and if untreated experience halted
growth from premature fusion of epiphyses. This phenomena is directly related to
radiation dose. Younger children and females appear to be at greatest risk. Subjects
who experience precocious puberty are often GHD (Sklar, 1994). Definition of
precocious puberty is physical signs of puberty before age 8 years (female) and
before age 9 years (males).
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(4) Obesity
Obesity is observed often in children successfully treated for ALL. It has
been suggested although not confirmed that this phenomenon may be related to
cranial irradiation. It is postulated that subtle GH deficiencies secondary to cranial
irradiation may lead to alterations in body composition (decrease in muscle mass and
increase in adipose tissue), which in turn effect exercise capacity and physical
activity level (Warner, Bell, Webb and Gregory, 1998). Jenny, et al. (1995) found
that 42% of children previously treated for ALL reported some degree of exercise
intolerance.
(5) Thyroid dysfunction
Thyroid dysfunction may result from irradiation of the hypothalamic-
pituitary axis or thyroid gland, i.e., cranial or cranio-spinal irradiation. Robison et al.
(1985) found that 10% of 175 ALL survivors treated with 1800-2400 cGy cranial or
cranial-spinal irradiation at 7 years follow-up had thyroid abnormalities, which
included primary hypothyroidism, compensated hypothyroidism, thyroid adenoma,
and one case of carcinoma. No significant association was found between radiation
dose, duration of therapy, or age at the time of irradiation. Thyroid hypofunction is
usually asymptomatic and manifested by elevated thyroid-stimulating hormone
(TSH), decreased T4 levels, or thyroid enlargement (Constine and Schwartz, 1994).
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(6) Gonadotropin deficiency
Although the vast majority of boys and girls treated for ALL maintain normal
gonadal function, primary gonadal damage (elevated FSH) has been documented in
both sexes when exposed to high doses of Cytoxan. Early menopause has been
documented in females who were treated with alkylating agents during adolescence
(Byrne, et al. 1992).
Boys who have had testicular disease and have received irradiation to the
testes (2400 cGy) have a high rate of testicular endocrine failure (elevated FSH, low
testosterone) and require androgen replacement therapy (Sklar, et al. 1990).
b. Second cancers
Second cancers have been reported in survivors of childhood ALL. A
retrospective cohort study of 9720 children who were treated for ALL between 1972
and 1988, with a median follow-up of 16 years, found 43 second cancers, seven
times more than among age-matched controls (Neglia, et al. 1991). Incidence of
brain tumors was 22 times higher than among age matched controls. All brain
tumors arose in children who were less than five years of age at diagnosis and treated
with cranial irradiation. Secondary leukemias have been linked to intensive
exposure to epipodophyllotoxins (Pui, et al. 1991).
c. Cardiac
Late cardiac effects may occur months or years after treatment with
anthracyclines and can range from minor electrocardiogram (ECG) abnormalities to
38
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congestive heart failure. (Lipshultz, et al. 1991) found abnormal echocardiograms
(Echo) in 57% of 115 ALL survivors who had received cumulative doses of
anthracyclines in the range of 228 mg to 550 mg/m2 at a median follow-up time of
6.4 years. The most common findings were decreased contractility and increased
ventricular afterload. Eleven patients (10%) had congestive heart failure within the
first year of treatment. No late cardiac events were found. Symptoms of fatigue,
shortness of breath, palpitation, and syncope correlated poorly with measures of
ventricular function. Reduced exercise tolerance was common (43%), although it did
not predict cardiac abnormalities.
Sorensen, Levitt, Bull, Chessells and Sullivan (1997) found cardiac
abnormalities to be less frequent and less severe in ALL survivors (n=120) who had
received low to moderate cumulative doses of anthracycline (90mg/m2 to
270mg/m2). With a median follow-up of 6.2 years, 23% had abnormal studies. All
patients were asymptomatic.
Prevalence and severity of cardiac abnormalities increase with higher
cumulative doses of anthracyclines, younger age at diagnosis (less than 4 years),
longer follow-up and mediastinal irradiation. (Steinherz, Steinherz and Tan, 1995;
Lipshultz, et al.1995) Some studies identify female gender as a risk factor (Silber,
Jakacki, Larsen, Goldwein and Barber, 1993; Steinherz, Steinherz, Tan, Heller and
Murphy, 1991).
Symptoms of cardiomyopathy include palpitations, shortness of breath and
fatigue. Patients are often asymptomatic in the early stages of cardiomyopathy
39
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(Truesdell, Schwartz, Clark and Constine, 1994). Cardiac follow-up after
anthracycline therapy is not standardized. Echos are used to evaluate cardiac
function, although the frequency of these evaluations varies greatly between patients.
Symptomatic patients are managed with cardiac medications, such as digitalis,
diuretics, vasodilator and angiotensin-converting enzyme inhibitors (Bricker, Green
andD ’angio, 1993).
In CCG leukemia studies (1975-1995), total anthracycline doses range from
0-500mg/m2, with high risk patients receiving higher doses.
d. Hepatitis C infection
Patients with ALL who received blood products before July 1992 are at risk
of Hepatitis C (Centers for Disease Control and Prevention, 1998). Follow-up studies
of childhood cancer survivors have found a 15% prevalence rate of Hepatits C in
patients who received blood transfusions during therapy (Oeffinger, Eshelman,
Tomlinson, Buchanan and Foster, 2000). Although the National Center for Disease
Control and Prevention recommend screening all individuals who have received
blood products before July 1992, the percent of cancer survivors at CHLA who have
been tested for Hepatitis C is unknown. Patients with chronic Hepatitis C infection
are often asymptomatic.
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e. Psychological
Psychological late effects of cancer are less clear than medical effects. In
general, studies of long-term survivors of childhood cancer show that most adjust
well but that a significant subset continue to have ongoing difficulties. Lansky, List
and Ritter-Sterr (1986) found a higher incidence of treated adult depression in cancer
survivors compared to the general population. Cancer treatment intensity and cranial
irradiation have recently been identified as possible risk factors for negative mood in
long term survivors of ALL (Chen, et al. 1998). It is proposed that cranial irradiation
may dysregulate neurotransmitter systems (e.g., serotonin, norepinephrine) which are
linked to the onset of depression. Neuro-chemical changes secondary to neurotoxic
cancer therapy are just beginning to be explored.
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Table 3. Possible Mechanisms of Fatigue Derived from Late Effects_________________
*CNS-neurotransmitters: cortisol levels affects fatigue (Poleliakhoff, 1981).
*CNS-Neuropsvch: cognitive impairment and fatigue are interrelated (Yoshitake, 1978;
Grandjean, 1968).
*CNS-Neuroendocrine: BMI and fatigue are positively correlated in women with breast
cancer (Andrykowski, Curran and Lightner, 1998).
*CNS-Neuroendocrine: GHD is correlated with fatigue (Birmaher, et al., 2000) and
depression (Birmaher, 2000) and sleep pathology (Darko, 1997).
*CNS: precocious puberty (related to GHD) may be a subtle symptom of CNS damage
(Leiper, Stanhope, Retching and Chessells, 1987).
*Endrocine: fatigue is a symptom of hypothyroidism (Constine and Schwartz,
1994).
*Endocrine: sex hormones (estrogen and testosterone) affect fatigue
Hepatitis C: fatigue is a symptom of hepatitis (Cuthbert, 1994).
Carciac: fatigue is a symptom of cardiac failure (Truesdell, Schwartz, Clark and
Constine, 1994).
*Psvcholoaic: fatigue and depression are inter-related (Bower, et al. 2000).
Pain: pain is positively correlated with fatigue (Smets, et al. 1998; Bower, et al. 2000).
*late effects associated with cranial irradiation therapy
42
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Table 4. Childrens Cancer Group Risk Group Classification for ALL*
Low Risk
WBC, 10,000
Age 2-9 years inclusive
All girls, boys with platelets >100,000
Moderate Risk
Age 2-9 years inclusive with WBC=10,000-49,999; or
Age 2-9 inclusive with WBC<10,000 and male
(Platelets<100,000)
12-23 months with WBC<50,000
High Risk
Age>=10years, or
WBC>=50,000 (and>lyr but <=20years)
Must not meet Lymphoma Syndrome Criteria)
High Risk, Lymphoma Syndrome
Age>=l-20 years, with one characteristic from each column:
WBC>=50,000, or Massive lymphadenopathy, or
Hgb>=10gm/dl, or Massive splenomegaly, or
T cell ALL Large mediastinal mass
High Risk, infants
________ A gecl year at diagnosis_____________________________________________
*risk groups are not consistent across all CCG treatment protocols; slight variations
in criteria have been made over the years
43
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Table 5. CCG ALL Protocols (1975-1995)
CCG Protocol (yrs) CNS Preventive Therapy Length of Therapy Total Anthracycline Dose
LOW RISK
101/143 Randomized to 3 yrs or 5 yrs
161 (9/77) IT MTX +/- 1800 cGy cr XRT Randomized to 2 yrs or 3 yrs none
104 (4/83) IT MTX 3 yrs (males), 2 yrs (females) none
1881 (12/88) IT MTX 3 yrs (males), 2 yrs (females) 75mg/m2
\ \ l K \<«r RISK ♦ * *
162 A (3/78) 1800 cGy Cr XRT and IT MTX 3 yrs 0-360mg/m2
105 (8/88) IT MTX +/- 1800 cGyCr XRT 3 yrs (males), 2 yrs (females) 175mg/m2
1891 (1/90) IT MTX 3 yrs (males), 2 yrs (females) 175mg/m2
11K.II RISK
163 (8/77) 1800 cGy Cr XRT and IT MTX 3 yrs. 360mg/m2
192P (4/81) 1800 cGy Cr XRT and IT MTX 3 yrs. 350-500mg/m2
193 (7/81) 1800 cGy Cr XRT and IT MTX 3 yrs. 175mg/m2
106 (2/83) 1800 cGy Cr XRT and IT MTX 3 yrs (males), 2 yrs (females) 175mg/m2
1882 (12/90) IT MTX +/- 1800 cGy Cr XRT 3 yrs (males), 2 yrs (females) 175-250mg/m2
HIGH RISK 1M WIN
107 (1/84) IT MTX and Ara-C with high dose
systemic therapy
3 yrs (males), 2 yrs (females) 125mg/m2
1883 (12/88) IT MTX and IT Ara-C with high dose
systemic therapy
3 yrs (males), 2 yrs (females) 87mg/m2
HIGH RISK n M P I I O M \
123(11/88) 11 M I ’X +/- 1800 cGy Cr XRT 3 yrs (males), 2 yrs (females) 350-500mg/m2
1901 (12/90) 1800 cGy Cr XRT and IT MTX 2 yrs 360-420mg/m2
4^
«£*•
III. PRELIM INARY STUDY
Although some investigators have examined off-treatment fatigue in adult
cancer patients, none has assessed off-treatment fatigue in childhood cancer
survivors. To obtain some preliminary data on off-treatment fatigue in long term
survivors of childhood cancer, a pilot study was conducted with young adult
survivors of childhood cancer at Childrens Hospital Los Angeles (CHLA). Sixteen
long-term cancer survivors who were being seen in the Long-term Information,
Follow-up and Evaluation (LIFE) clinic for their annual evaluation were asked to
complete the Revised-Piper Fatigue Scale (R-PFS), a valid and reliable tool for the
measurement of perceived fatigue in patients with cancer (Piper, et al. 1998). The R-
PFS contains 22 items on a 0 to 10 scale that measures the following four dimensions
of subjective fatigue: (1) behavioral, 6 items relating to the distress and degree of
disruption in the activities of daily living (e.g., work, social; (2) sensory, 5 items
relating to the physical symptoms of fatigue (e.g., strong/weak, refreshed/tired));
(3) cognitive/mood, 6 items, relating to mental and mood states (e.g., concentration,
memory); (4) affective/emotional, 5 items relating to the emotional meaning
attributed to fatigue (e.g., normal/abnormal, positive/negative). Mean subscale
scores were calculated. A total fatigue score was calculated by adding the mean
subscale scores and dividing by four. Higher scores indicate greater fatigue. Fatigue
scores were also categorized as per published cut-points (Berger, 1998), mild (0-
2.49), moderate (2.5-5.99) and severe (6-10).
45
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Fifty percent of the patients in the pilot study had a diagnosis of ALL. The
remaining patients were diagnosed with a variety of childhood cancers, including
Lymphoma, Hodgkin’s, and Wilms’ Tumor. Nine of the patients had received cranial
irradiation (1800 cGy, n = 4; 2400 cGy or greater, n=5). Mean time off therapy was
12.7 years with 75% of the patients being off-treatment for more than 8 years.
Additional demographic data is summarized in Table 6.
Table 6. Demographic data
Gender* Time off
Theraov
Years (sd)
(range)
Age at
Diagnosis
Years (sd)
(range)
Current Age
Years (sd)
(range)
Full Sample (n-16) 6M/10F 12.6 (5.82) 8.6 (5.27) 25.3 (4.38)
(4-25) (1-16) (18-33)
CrR* (n=9) 4M/5F 11.6 (3.72) 8.7 (4.24) 25.8 (4.24)
No CrR** (n=7) 2M/5F 14.0 (8.53)± 8.5 (6.60) 24.7 (4.55)
CrR = cranial irradiation
No CrR = no cranial irradiation
p<0.05 (t-test)
M=male, F=female
The results of the pilot study are presented in Table 7. With the total fatigue
score, 69% of the sample (11/16) reported moderate or severe levels of fatigue with
an average time off-treatment of 12.5 years. When the specific fatigue subscales are
examined, the percentage of individuals who reported moderate or severe levels of
fatigue range from 81% (sensory subscale) to 50% (behavior subscale). When
survivors who had received cranial irradiation were compared to those who had not
received cranial irradiation, group differences were significant for the cognitive
46
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subscale (p<0.05) and approached significance for the total fatigue score (p=0.07).
Fatigue mean scores were consistently higher in the group of survivors who had
received cranial irradiation.
Table 7.
Fatigue in Childhood Cancer Survivors, Pilot Data
Pilot Sample CrR* No CrR** p-value***
(n=16) (n=9) (n=7)
Total Fatigue Score
Mild 5 (31%) 1 (11%) 4(57% ) 0.07
Moderate 7 (44%) 6(67%) 1 (14%)
Severe 4 (25%) 2 (22%) 2 (29%)
Behavior
Mild 8 (50%) 3 (33%) 5 (71%) 0.12
Moderate 4 (25%) 4 (44%) 0 ( 0%)
Severe 4 (25%) 2 (22%) 2 (29%)
Sensory
Mild 3 (19%) 1 (11%) 2(29% ) 0.11
Moderate 6 (37%) 2 (22%) 4 (57%)
Severe 7 (44%) 6 (67%) 1 (14%)
Cognitive
Mild 5(31% ) 1 (11%) 4(57% ) 0.01
Moderate 9 (56%) 8 (89%) 1 (14%)
Severe 2 (13%) 0 ( 0%) 2 (29%)
Affective
Mild 6 (37%) 3 (33%) 3 (43%) 0.89
Moderate 7 (44%) 4 (44%) 3 (43%)
Severe 3 (19%) 2 (22%) 1 (14%)
Fatigue Pilot Sample CrR (n=9) No CrR (n=7) p-value
mean (sd) mean (sd) mean (sd)
Total Fatigue 3.96 (2.53) 4.36 (2.08) 3.44 (3.05) 0.44
Sensory 4.90 (2.57) 5.76 (2.38) 3.80 (2.55) 0.14
Affective 3.95 (3.11) 4.31 (3.38) 3.49 (2.92) 0.62
Cognitive 3.73 (2.55) 3.94(1.73) 3.45 (3.48) 0.72
Behavior 3.23 (3.30) 3.65 (2.79) 2.69 (3.89) 0.57
mild fatigue = 0-2.49; moderate fatigue = 2.5-5.99; severe fatigue = 6-10
* CrR = cranial irradiation; ** No CrR = no cranial irradiation; *** chi-square test
While no participant in this pilot study had received therapy in the past 4
years, time off therapy was significantly shorter for those survivors who had received
cranial irradiation (11.6 years) compared to those who had not received cranial
47
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irradiation (14 years). Cranial irradiation treatment groups did not differ significantly
in respect to gender, age at diagnosis and current age (Table 6).
We compare our pilot data to published data for adult cancer patients to put
our pilot study findings in the context of what is known about cancer-related fatigue.
In Figure 2, we compare our pilot fatigue scores to fatigue scores from 65 women
receiving adjuvant breast cancer treatment, where fatigue was measured 48 hours
after (Time 1) and at treatment cycle midpoints (Time 2) for three cycles (Berger,
1998). Berger found that fatigue scores were greater at Time 1 than Time 2 with
subjects reporting approximately the same intensity of fatigue with each treatment.
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Figure 2.
Pilot Data Compared to Breast
Cancer Patients OnTreatment*
o
6
6 7 -r— —
■Time 1
■Time 2
□ Pilot
□ CrR Pilot
■ No CrR Pilot
*Berger, 1998
Time 1: 48 hours after chemotherapy treatments, average score of three treatments (n=65)
Time 2: Mid-point between chemotherapy treatments, average score over three treatments (n=65)
Pilot: n=16
CrR Pilot: Pilot data, cranial irradiation (n= 9)
No CrR Pilot: Pilot data, no cranial irradiation (n-7)
Pilot fatigue scores, which were collected from individuals with an average
time off-treatment of 12.7 years, are all greater than the fatigue scores reported by
breast cancer patients at mid-cycle (Time 2). In the sensory subscale, we find the
pilot mean score to be greater than the mean score reported by patients who had
received chemotherapy 48 hours earlier. In the group that had received cranial
irradiation, affective fatigue score is almost equal to that reported by breast cancer
patients at Time 1. Thus, individuals who completed treatment for childhood cancer
nearly 13 years ago exhibit similar fatigue compared with women undergoing
chemotherapy for breast cancer, suggesting persistent long-term effects from these
treatments.
49
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Woo and colleagues (1998) studied fatigue levels in women who had
received treatment for breast cancer within the past 18 months (n=322) by type of
cancer therapy ((1) radiation (R), (2) chemotherapy (C), (3) hormone therapy (H), (4)
chemotherapy and radiation therapy (CR), (5) chemotherapy, radiation, and hormone
therapy (CRH), and radiation and hormone therapy (RH). Time since initial
diagnosis was 17 days to 27 years. This study consisted of a mixed sample of on and
off-treatment patients. Women who had received combination therapy (RHC) had
the highest fatigue scores while those who received only radiation therapy had the
lowest fatigue scores. When the fatigue scores from our pilot data are compared to
the fatigue scores from Woo’s study we find the pilot fatigue score to be higher than
the score of four of the treatment groups (R, H, CH, CR) (Figure 3). The cognitive
fatigue score in our pilot group was most similar to the CRH group, i.e., the group of
breast cancer patients who had received the most intensive therapy and who reported
the highest level of cognitive fatigue.
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Figure 3.
Pilot Data Compared to Fatigue
Scores in Breast Cancer Patients*
H Total fatigue
■ cognitive
o
* Woo, Dibble, Piper, Keating, and Weiss, 1998
CRH= Chemotherapy, Radiation and Hormone therapy; C=Chemotherapy; HR= Hormone and
Radiation therapy; CR=Chemotherapy and Radiation therapy; C=Chemotherapy; H=Hormone
therapy; R = Radiation therapy________________________________________________________
Pilot = Pilot data (n=16)
CrR Pilot = Pilot data, cranial irradiation (n=9)
No CrR Pilot = Pilot data, no cranial irradiation (n=7)
In another comparative study, we found that our pediatric cancer survivors
who had received cranial irradiation reported higher levels of fatigue on all five
dimensions (total, behavior, cognitive, affective and sensory) than breast cancer
patients (n=382) (Figure 4) (Piper et al. 1998). The breast cancer patients in this
study had completed their primary cancer treatment, with the majority currently
receiving tamoxifen therapy. Time from primary cancer treatment was not reported.
51
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Figure 4.
Pilot Data Compared to Fatigue in
Breast Cancer Patients*
6 - h b r s t c a
■ Pilot
□ CrR Pilot
□ No CrR Pilot
of*
S S f J
*Piper, et al. 1998
BRST CA = Breast cancer patients (n=382)
Pilot = Pilot data (n=16)
CrR Pilot = Pilot data, cranial irradiation (n=9)
No CrR Pilot = Pilot data, no cranial irradiation (n=7)
In summary, in our pilot study we found that childhood cancer survivors who
have been off therapy for an average of 12.7 years reported moderate to severe levels
of fatigue. The fatigue scores of these childhood cancer survivors are in the range of
scores reported by adult cancer patients who are on active treatment or completed
treatment 18 months earlier. Our pilot study also found that fatigue is greater among
those patients treated with cranial irradiation.
52
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III. GRANT PROPOSAL
A. Background and Rationale
Over the past 40 years, prognosis has dramatically improved for children with
acute lymphoblastic leukemia (ALL). Childhood ALL was once a uniformly fatal
disease but long-term survival rates are now approaching 80% (Pui, 1995). As the
number of survivors of childhood cancer grows, attention is being increasingly
directed toward the evaluation, management and prevention of late effects of
treatment (Meister and Meadows, 1993). A number of late complications of therapy
in long-term survivors of childhood ALL, such as cognitive impairments and
endocrine abnormalities, are well documented in the literature. Others, such as
fatigue, have not yet been identified or studied (Schwartz, Constine, Hobbie and
Ruccione, 1994).
In 1994, Childrens Hospital Los Angeles (CHLA) established the LIFE
(Long-term Information, Follow-up and Evaluation) program to provide ongoing
surveillance, education and support to long-term survivors of childhood cancer. In
the LIFE clinic, we find our patients frequently report fatigue symptoms that cause
significant distress and interfere with activities of daily living (social, work and
school). Patients also report that clinicians often dismiss these symptoms and
indicate that they are unrelated to the patient’s past cancer therapy. Although
chronic fatigue following cancer therapy has been recently described in survivors of
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adult cancers, this is the first study to investigate fatigue as a late effect of therapy in
survivors of childhood leukemia.
The aims of this study are to (1) estimate the prevalence and severity of
fatigue among adult survivors of childhood ALL, (2) examine the relationship of
fatigue to demographic and disease characteristics, including treatment and late
effects of therapy, (3) explore the association between fatigue and cranial irradiation
and (4) compare fatigue in survivors of childhood ALL to published norms on
fatigue in the general population and in adult cancer patients.
1. Fatigue in survivors of adult cancer
Cancer related fatigue (CRF) is defined as “an unusual, persistent, subjective
sense of tiredness related to cancer or cancer treatment that interferes with usual
functioning” (National Comprehensive Cancer Network, 2003, p. 1). It has recently
been accepted as a diagnosis in the International Classification of Disease 10th
Revision-Clinical Modification (Celia, Peterman, Passik, Jacobsen and Breitbart,
1998). Although the mechanisms that precipitate or sustain fatigue are unknown,
fatigue is a nearly universal symptom in patients undergoing cancer therapy.
Further, 37%-78% of the adult patients continue to experience significant fatigue
long after the completion of therapy (Stone, Richards and Hardy, 1998). In the
majority of the studies, investigators have found the prevalence and intensity of
fatigue to be greater in survivors of adult cancers than in the general population
(Loge, Abrahamsen, Ekeberg and Kaasa, 1999; Andrykowski, Curran and Lightner,
54
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1998). In adult cancer survivors, fatigue has been found to be related to type of
treatment, stage of disease, sleep disorders, depression, pain, body mass index,
menopause, income, education, functional disability, age and gender (Winningham
and Barton-Burke, 2000). In most studies, fatigue has not been related to time off
treatment. This persistent fatigue following cancer treatment has been found to have
a profound negative impact on the cancer survivor’s quality of life, affecting
relationships, role performance and income potential (Bower, et al. 2000).
2. Fatigue in survivors of ALL
The epidemiology of fatigue in long-term survivors of childhood ALL is
unknown. Since children differ from adults with respect to their cancer diagnoses,
treatments and late effects of therapy (Hockenberry-Eaton, et al. 1998; Neglia and
Nesbit, 1992; DeLat and Lampkin, 1992), these cancer populations would be
expected to differ with respect to fatigue following cancer therapy. In contrast to
patients on active therapy, where fatigue is often related to the acute toxicities of
treatment (e.g., anemia, vomiting), fatigue in long term survivors is more likely to be
related to the late toxicities (late effects) of therapy such as endocrine dysfunction.
3. CNS late effects and cranial irradiation in survivors of ALL
In survivors of childhood ALL, many of the late effects that have been
identified are directly related to cranial irradiation. Because the central nervous
system (CNS) is a sanctuary site for leukemia cells, all children diagnosed with ALL
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receive some form of CNS therapy. CNS therapy generally consists of intrathecal
chemotherapy (i.e., chemotherapy (methotrexate) given directly into spinal fluid)
with or without cranial irradiation. Documented late effects following cranial
irradiation are (1) structural changes in the brain: cerebral cortical atrophy; white
matter hypodensity indicating localized edema, cytokine expression, and
demyelination; and intracerebral calcifications (Hopewell, 1998); (2) neuro
endocrine abnormalities, primarily involving the hypothalamic pituitary axis (HPA):
growth hormone deficiency; obesity; thyroid dysfunction; precocious puberty; and
gonadotropin deficiency (Schwartz, Constine, Hobbie and Ruccione,1994) and (3)
neuro-cognitive impairments: attention and memory deficits (Copeland, et al. 1985).
We propose in this study that cranial irradiation during childhood and its
related late effects may put this group of ALL survivors at greater risk of fatigue.
Although a relationship between neuro-toxic therapies and fatigue has been proposed
by others (Piper, Lindsey and Dodd, 1987; Meyers, 2000), this association between
cranial irradiation and fatigue has never been studied.
4. CNS and fatigue
The CNS plays a significant role in the perception and modulation of fatigue
(Grandjean, 1968). Impairment of the central component (psyche/brain and spinal
cord) causes “lack of motivation, impaired spinal cord transmission, and exhaustion
or malfunction of the brain cells in the hypothalamic region.” (Poteliakhoff, 1981, p.
91). Central mediated mechanisms have been implicated in the extreme fatigue
present in multiple sclerosis, chronic fatigue syndrome, HIV, cancer patients
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receiving biotherapy and cancer patients immediately after cranial irradiation (note:
somnolence syndrome is a transient event, an acute toxicity). Proposed mechanisms
include cytokine activation, demyelination, neurotransmitter dysfunction (serotonin
(5-HT), acetylcholine) and HPA neuro-endocrine abnormalities (Trautman, et al.
1988; Enoka and Stuart, 1992; Gibson and Edwards, 1985).
5. Cognitive impairment, fatigue and cranial irradiation
Cognitive impairment is a late effect of cranial irradiation. Fatigue and
cognitive impairment are linked, although the mechanisms underlying this
association are unclear. Directed attention requires mental effort; when mental
demands exceed available capacity, individuals are at risk for attentional fatigue that
results in decreased capacity to concentrate or pay attention (Kaplan & Kaplan,
1982). Neuro-psychologists, while evaluating survivors of ALL for cognitive
deficits, have observed a fatigue effect that factors into the child’s poor school
performance (Brouwers, 1987; Lockwood, Bell and Colegrove, 1999). In these
studies, investigators have been unable to determine if fatigue is causing poor
performance or if cognitive impairments are producing fatigue.
6. Depression and fatigue and cranial irradiation
Fatigue is a complex phenomenon that has both physical and psychological
components. Most studies find a positive correlation between depression and
chronic fatigue (Glaus, 1998). It is thought that fatigue and depression may be
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outcome indicators of one another. A recent study of long-term survivors of ALL
investigators found an association between depression and cranial irradiation,
supporting a centrally mediated mechanism (neurotransmitter dysfunction) for
depression and fatigue (Chen, et al. 1998).
B. Preliminary Study of Fatigue in Childhood Cancer Survivors
To obtain some initial data on fatigue in long-term survivors of childhood
cancer, we asked sixteen young adult survivors of childhood cancer in the LIFE
clinic to complete the Revised-Piper Fatigue Scale (R-PFS), a valid and reliable
questionnaire often used to measure subjective fatigue in patients with cancer (Piper,
et al. 1989). The R-PFS contains 22 items (0 to 10 scale) and measures the
following dimensions: (1) Behavior: (relates to the distress and degree of disruption
in the activities of daily living (e.g., work, social)); (2) Sensory: (relates to the
physical symptoms of fatigue (e.g., strong/weak, refreshed/tired)); (3)
Cognitive/Mood: (relates to mental and mood states (e.g., concentration, memory));
(4) Affective/Emotional: (relates to the emotional meaning attributed to fatigue (e.g.,
normal/abnormal, positive/negative); and (5) Total Fatigue (global score).
The patients (10 females and 6 males) were 18 to 33 years of age and
diagnosed with a variety of childhood cancers at a mean age of 8.6 years (standard
deviation = 5.27). Average time off therapy was 12.7 years (range 4-25 years).
When fatigue scores were categorized as mild (0-2.49), moderate (2.5-5.99)
and severe (6-10) (Berger, 1998), 69% of the patients (11/16) reported moderate or
58
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severe levels of fatigue (total score). When the subscales were examined, the
percentage of individuals who reported moderate or severe levels of fatigue ranged
from 81% (sensory subscale) to 50% (behavior subscale). When survivors who had
received cranial irradiation (n=9) were compared to those who had not received
cranial irradiation, group differences were significant for the cognitive subscale
(p<0.05) and approached significance for the total fatigue score (p=0.07), and
sensory (p=0.11) and behavior subscales (p=0.12). Fatigue mean scores were
consistently higher in the group of survivors who had received cranial irradiation.
These initial findings suggest significant fatigue in this population of survivors and
that those who received cranial irradiation may be at greater risk of fatigue.
C. Study Design
1. Subjects
The proposed study is a descriptive cross-sectional study. Eligible patients
will be those who were diagnosed at CHLA with ALL between January 1, 1975 and
December 31, 1995 before age 18 years. All eligible patients will be disease free, off
cancer therapy for a minimum of one year, English speaking and at least 18 years old
at the time of interview. The Cancer Registry at CHLA will provide a list of potential
subjects.
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6. Procedure
After receiving physician permission, we will send the subject a letter
introducing the study. In a follow-up telephone call we will answer questions and
schedule a telephone interview with those patients who agree to participate. We will
obtain oral consent before the interview and ask patients to sign and return by mail a
consent for release of medical records. For patients without good contact information
(estimated n=243), we will use mortality and vital statistics tracing services located
in the Department of Preventive Medicine at University of Southern California
(USC).
We will collect data by telephone interview and review of medical records.
We will ask the patient to provide the following demographic information: gender,
date of birth, educational attainment, employment status, marital status, income,
living arrangement, number of dependents in the home, current height and weight,
ethnicity and parental information (mother and father’s educational level, ethnicity
and income). We will use four well-established self-report instruments to measure
fatigue: the Revised Piper Fatigue Scale (R-PFS) (Piper, et al. 1989; Profile of Mood
States, fatigue-inertia subscale (POMS) (McNair, Lorr and Droppleman, 1971);
RAND SF-36 vitality subscale (Ware, Snow, Kosinski and Gandek, 1993); and
Symptom Distress Scale (McCorkle and Young, 1978). Norms for the general
population (age/gender adjusted) are available for the RAND SF-36 and POMS. We
will use the RAND SF-36 pain subscale to assess pain, the Center for Epidemiologic
Studies’ Depression Scale (CES-D) (Radloff, 1977) to measure depression, the
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Pittsburgh Sleep Quality Index (PSQI) (Bussye, Reynolds, Monk, Berman and
Kupfer, 1988) to measure sleep quality and selected questions from the Children’s
Cancer Survivor Study Questionnaire to measure medical late effects of ALL
treatment.
We will abstract the following information from the patient’s medical record:
diagnosis, date of diagnosis, date of last treatment, therapy [(1) cranial irradiation
(dose, site and dates of treatment); (2) IT chemotherapy (drugs, total dose and
number of treatments); (3) IV methotrexate (total dose); (4) anthracycline (total
dose)]. We will obtain medical records from storage for those patients who are no
longer actively followed at CHLA. We will submit the study protocol to CHLA’s
Internal Review Board for approval.
D. Data Analysis
Descriptive statistics (means, standard deviations, frequencies, plots, tables and
histograms) will be calculated for all variables. We will examine the interrelationship
between variables using standard statistical procedures depending on the type of
variables that are involved. These will include Student’s t-test, Pearson and other
correlation coefficients, chi-square tests and analysis of variance. We will calculate
chronbach’s alpha coefficients for individual fatigue scales and subscales to test
internal consistency. A Pearson correlation matrix of fatigue variables will be used
to examine the interrelationships between fatigue measures. One sample t-test will
be used to compare the patient’s POMS fatigue-inertia and SF-36 vitality subscale
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scores to published norms, (i.e., to test the null hypothesis that the ALL sample mean
is equal to the published norm).
The R-PFS total fatigue score is the primary outcome variable for regression
analyses. We will use linear regression models to identify covariates of fatigue. We
will consider demographic and disease/treatment variables in the model. A backward
elimination procedure will be used and iterations will continue until only those
variables that significantly predict the dependent variable remain. Since it is
biologically possible that some comorbid conditions such as depression may be an
intermediate step in the causal pathway of fatigue, we will run regression models
with and without these variables.
We will use logistic regression analysis to determine the risk of being classified
as fatigued. Fatigue is defined as a R-PFS (total) score of 3 or greater (Berger, 1998).
Cranial irradiation, a dichotomous variable, will be entered in the regression model
as the independent variable. Confounding variables will be added as covariates. All
statistical testing will use an alpha of 0.05 (two-sided)
E. Sample Size Calculations
The CHLA Cancer Registry has identified 389 potential subjects. After
projecting the number of patients who may be ineligible, lost to follow-up or refuse
to participate, we estimate a sample size of 232. A sample of 232 (116/treatment
group ((+) or (-) cranial irradiation) provides sufficient statistical power (>.80) to
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detect mean group differences (0.92) in total fatigue (PFS) (alpha - 0.05, two sided)
(POWER Epicenter Software).
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F. Budget
Table 8. Proposed Budget
PERSONNEL
Meeske, Kathleen Salaries
Fringe Benefits
TOTAL:
TRAVEL
One scientific meeting per year
Transfer fee for pulling medical charts out of storage
Year 1: ($2.50 x 78 = $195; $12.50 x 2 = $25)
Year 2: ($2.50 x 38 = $95; $12.50 x 2 = $25)
Fee for tracking patients lost to follow-up
Year 1: ($2.00 x 78 = $156) Year 2: ($2.00 x 38= $76)
TOTAL:
OTHER COSTS: Telephone Costs
One Hour Interviews == outside California
Year 1: (52 interviews x $.30/min x 60 minutes = $936)
Year 2: (25 interviews x $.30/min x 60 minutes = $450)
One Hour Interviews == outside local calling area
Year 1: (52 interviews x $.15/min x 60 minutes = $468)
Year 2: (25 interviews x $.15/min x 60 minutes = $225)
One Hour Interviews == local calling area
Year 1: (52 interviews x $.05 x 60 minutes = $156)
Year 2: (25 interviews x $.05/min x 60 minutes = $75)
Recruitment Telephone Calls
Year 1: (12 months x $30/month = $360)
Year 2: (3 months x $30/month = $90)
Tracking Telephone Calls
Year 1: (12 months x $30/month = $360)
g Year 2: (3 months x $30/month = $90)
YEAR 1 YEAR 2 TOTAL
$ 3,536 $ 3,536
587 587
$ 4,123 $ 4,123 $ 8,246
$ 1,000 $ 1,000
$ 220
$ 120
$ 156 $ 76
$ 376 $ 196 $ 572
$ 936
$ 450
$ 468
$ 225
$ 156
$ 75
$ 360
$ 90
$ 360
$ 90
TOTAL:
Table 8. Proposed Budget (continued)____________________
Mailing Costs
Letter of invitation to subject
Year 1: (1 st & 2nd mailings: 442 x $.33 = $146)
Year 2: (3rd mailing: 221 x $.33 = $73)
Confirmation Letter
Y earl: (251 letters x $.77 = $194)
Year 2: (124 letters x $.77 = $96)
Return Envelope (release of med.records form)
Yearl: (250 x $.33 = $83) Year2: (125x $.33 = $42)
Thank you Letter
Yearl: (250x $.33 = $83) Year2: (125x $.33 = $42)
TOTAL:
Photocopying
Letter of Invitation (663 x 1 pg x $.04 = $27)
Confirmation Letter (275: includes Medical Release
1 pg; Response Cards 7 pgs; Consent Form
4 pgs) (275x12x$.04 = $132)
Thank you letters (232 x $.04 = $14)
Questionnaire (232 x 40 pg x $.04 = $372)
TOTAL:
Supplies
SAS Renewal ($150/year x 2 years)
General Office Supplies ($84/yr x 2)
Questionnaire Envelopes/Stationary
Printer Toner cartridge (4 x $87)
Computer Diskettes
Folders and Notebooks
TOTAL:
TOTAL DIRECT COSTS:
$ 2,280 $ 930 $ 3,210
$ 146
$ 194
$ 83
$ 83
$ 506
. $ 27
$ 132
$ 10
$ 372
$ 541
$ 150
$ 84
$ 168
$ 174
$ 100
$ 100
$ 776
$ 9,602
$ 73
$ 96
$ 42
$ 42
$ 253
$ 150
$ 84
$ 174
$ 100
$ 100
$ 608
$ 7,110
$ 759
$ 541
$ 1,384
$ 16,712
G. Cancer Control Potential
In this study, we will assess fatigue as a possible late effect of cancer therapy
in long term survivors of childhood ALL. In adults, chronic fatigue following cancer
therapy has been identified as a major obstacle to normal functioning (disability) and
good quality of life. Since fatigue has not yet been recognized in the literature as a
late effect of therapy in pediatric oncology, this symptom frequently is ignored and
untreated in this population of cancer survivors. With approximately 70% of children
now surviving a diagnosis of cancer (currently 220,000 childhood cancer survivors
in the United States), it is important to identify and study systematically late effects
such as fatigue that disrupt well being and the quality of survival. Recognition of
fatigue as a possible late effect of cancer therapy in survivors of childhood ALL will
encourage research and better assessment, clinical intervention and management.
H. Justification for Funds
Although fatigue has been recognized recently as a late effect of cancer
therapy in survivors of adult cancers, this is the first study to examine fatigue as a
possible late effect of cancer therapy in long-term survivors of childhood cancer
(ALL). It is also the first study to examine the relationship between cranial
irradiation (neurotoxic therapy) and fatigue in survivors of cancer. If the data from
this study are consistent with that published regarding adult cancer patients, fatigue
will be identified as a new potential late effect of therapy in pediatric oncology
patients and will require further study. This study is also important because it
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establishes for the LIFE program at CHLA its first cohort of long-term survivors.
With this cohort, we will be able to monitor survivors of childhood ALL for
additional late effects that may emerge during adult life.
I. Plans for Subsequent Activity and Funding
This initial study will provide valuable information on the prevalence and
risk factors of fatigue in survivors of ALL. If we find that survivors of ALL
experience significant fatigue, we will propose to the Pediatric Oncology Group to
expand this study to include survivors of all childhood cancers (Hodgkin’s, Wilms
tumor) and to conduct it within the cooperative group setting. If we find a positive
relationship between cranial irradiation and fatigue, we will consult with neuro
biologists, neuro-psychologists, neuro-physiologists and neuro-radiologists to
identify objective measures/bio-markers (functional MRI, endocrine studies),
delineate the specific central pathways of fatigue. Our long-term objective is to
design future studies to clarify the central mechanisms (conceptual model) of fatigue
following cancer therapy.
At the completion of this study, we will apply for a grant through the
American Cancer Society and The National Cancer Institute’s Office of Cancer
Survivors. Both organizations fund research projects centered on the psychosocial
and behavioral aspects of cancer survivorship.
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IV. FATIGUE IN LONG-TERM SURVIVORS OF CHILDHOOD
LEUKEMIA
A. Introduction
Over the past 30 years, remarkable strides have been made in the treatment of
childhood cancer. While childhood cancer was once a uniformly fatal disease, the
relative five year survival rate for children diagnosed under the age of 15 years is
now 75% (Ries, Kosary, Hankey, Miller and Edwards, 1997). This dramatic
improvement in survival has produced a growing population of cancer survivors.
Currently, there are more than 150,000 survivors of childhood cancer in the United
States. It is estimated that the prevalence of cancer survivors among young adults (15
to 45 years of age) will increase from one in 900 persons today to one in every 250
persons by the year 2010 (Bleyer, 1990).
As the number of long-term survivors of childhood cancer has grown,
attention has been directed increasingly toward treatment-related sequelae and their
effects on quality of life (Marina, 1997; Meadows, et al. 991). In pediatric oncology,
a number of late complications of therapy such as second cancers, hormone
deficiencies, cardiac abnormalities and cognitive impairments are well documented
in the literature, while others, such as fatigue have received limited attention and
exploration (Schwartz, Constine, Hobbie and Ruccione, 1994).
Fatigue has recently been recognized as a potential late effect of therapy in
adult cancer survivors. Although the mechanisms are often unknown, many adults
continue to experience significant fatigue long after the completion of their cancer
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therapy (Stone, Richards, and Hardy, 1998). Several studies have found that the
prevalence and intensity of fatigue is greater in survivors of adult cancer than in the
general population (Loge, Abrahamsen, Ekeberg and Kaasa, 1999; Andrykowski,
Curran and Lightner, 1998). Persistent fatigue following cancer treatment has been
shown to have a profound negative impact on the adult patient’s quality of life
(Bower, et al. 2000).
While information on off-treatment fatigue among survivors of childhood
cancer survivors is limited (Chen et al. 1998; Langeveld, Grootenhuis, Voute, de
Haan and van den Bos, 2003), we find that many survivors in our long-term follow-
up clinic report fatigue symptoms that cause significant distress, interfere with
normal functioning and affect quality of life. Since fatigue in survivors of childhood
cancer has received little attention and is poorly understood, this is a symptom that
continues to be under-recognized and under-treated.
The objectives of this study were to estimate the prevalence of fatigue among
adult survivors of ALL, to identify significant co-variates of fatigue and to examine
the association between fatigue and quality of life.
B. Methods
1. Subjects
Eligible subjects were individuals diagnosed with Acute Lymphoblastic
Leukemia (ALL) at Childrens Hospital Los Angeles (CHLA) before age 18 years
and between January 1, 1975 and December 31, 1995. Study subjects were required
to be disease-free, off-treatment for a minimum of one year, English speaking and at
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least 18 years old at the time of their interview. Patients with Down syndrome were
ineligible.
2. Procedure
We sent a letter of invitation to eligible subjects after receiving their
physicians’ assent to make contact. We made a follow-up telephone call to review
the study’s purpose and procedure and to answer questions. We mailed a consent
form, a postage-paid return envelope and an answer booklet to individuals who
agreed to be interviewed. We scheduled an interview after we received the subject’s
signed consent. We used established tracing methods available through the Los
Angeles County Cancer Surveillance Program at University of Southern California
(USC) to locate survivors for whom we did not have a current address or telephone
number.
Subjects participated in a 30-45 minute telephone interview in which we
collected demographic information, assessed fatigue, depression, sleep quality and
quality of life and screened for late effects related to cancer therapy and medical
comorbidities. During the interview, subjects used an answer booklet in which all
response choices were listed. This visual aid was most helpful to subjects with
cognitive impairments. Interviews were conducted by experienced interviewers blind
to the subject’s treatment history. Study procedures were approved by the CHLA
Clinical Investigations Committee and the USC Research Committee, in accordance
with assurances approved by the U.S. Department of Health and Human Services.
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3. Data collection
a. Demographic data
We collected information on each subject’s ethnicity, educational level,
current employment or school status, marital status, income, number of children in
the home, and current height and weight.
b. Fatigue measures
We used four well-established instruments to measure fatigue, the Revised
Piper Fatigue Scale (R-PFS), Profile of Mood States fatigue-inertia subscale
(POMS), RAND SF-36 vitality subscale and Symptom Distress Scale. We used the
R-PFS as our primary measure of fatigue.
(11 The Revised-Piper Fatigue Scale (R-PFS) (Piper, et al.1989). The R-PFS
measures four dimensions of subjective fatigue and provides an overall total fatigue
score. The behavioral subscale measures the distress and degree of disruption in
activities of daily living; the sensory subscale measures the physical symptoms of
fatigue; the cognitive/mood subscale measures mental and mood states; and the
affective/emotional subscale measures emotional meaning attributed to fatigue. The
R-PFS contains 22 items, each scored from 0 -10, with higher scores indicating
greater fatigue. In this study, we used an adapted version of the R-PFS which asked
survivors to rate their fatigue over the past month rather than over the past week. We
thought this time frame would be a better indicator of fatigue in this survivor
population. The R-PFS has demonstrated high reliability when used with adult
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cancer patients (Woo, Dibble, Piper, Keating and Weiss, 1998; Wu and McSweeney,
2001; Berger and Higginbotham, 2000). Moderate correlations between the R-PFS
and the Fatigue Symptom Checklist and the POMS fatigue-inertia subscale have
been reported (Wu and McSweeney, 2001).
(2) The RAND SF-36 Item Health Status Survey, Short Form-36, vitality
subscale (RAND SF-36) (Ware, et al. 1993). The SF-36 is a generic quality of life
instrument which measures perceived health status over the previous four weeks.
The SF-36 assesses eight domains: physical functioning, bodily pain, role limitations
due to physical health problems, role limitations due to personal or emotional health,
general mental health, social functioning, vitality and general health perception
(Hays, Sherboume and Mazel, 1993). The SF-36 summary measures, physical
component summary scale (PCS) and mental component summary scale (MCS),
represent physical and mental health. SF-36 subscales are scored on a scale of 0 to
100 with higher scores representing higher levels of functioning and health. The
RAND SF-36 has received extensive reliability and validity testing (McHomey,
Ware and Raczek, 1993; Ware and Sherbourne, 1992). General population norms are
available for males and females 18 years and older. The vitality subscale is a four
item uni-dimensional measure of fatigue. With breast cancer patients, the vitality
subscale correlates highly with the Piper Fatigue Scale and discriminates between
breast cancer survivors and women with benign breast problems (Andrykowski,
Curran and Lightner, 1998). In this study, in addition to the vitality subscale, we
have used the full SF-36 to measure quality of life and the pain subscale to determine
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survivors’ pain status. In this study, the Cronbach alpha coefficient for the SF-36 and
vitality subscale were 0.95 and 0.85 respectively.
(3) The Profile of Moods Scale (POMS) fatigue-inertia subscale (McNair,
Lorr and Dropplemen, 1971). The POMS_fatigue-inertia subscale (seven items)
measures a mood of weariness, inertia and low energy level. Subjects indicate on a
five point scale (0, not at all; 4, extremely well) how well each adjective describes
how they have been feeling during the past seven days. The higher the score the
greater the fatigue. Norms have been derived from adults diagnosed with chronic
fatigue syndrome, college students and psychiatric outpatients. POMS has been used
in numerous studies of adults to measure cancer-related fatigue. In this study,
internal consistency for the fatigue-inertia subscale was 0.93.
(4) Symptom Distress Scale (McCorkle and Young, 1978). The Symptom
Distress Scale has been used widely with adult cancer patients to assess distress
associated with cancer-related symptoms. Single symptom items include nausea,
mood, appetite, insomnia, pain, mobility, fatigue, bowel pattern, concentration and
appearance. Subjects indicate their level of distress for the current day on a scale of 1
to 5. Higher numbers indicate a higher level of distress. Cronbach alpha coefficient
for this study population was 0.83.
c. Depression
We used The Center for Epidemiological Studies Depression Scale (CES-D)
(Beeber, Shea and McCorkle, 1998; Radloff, 1977), a 20 item scale, to assess
symptoms of depression. Respondents report how often they experience a variety of
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symptoms during the previous week on a four point ordinal scale ranging from
‘rarely or none of the time’ (less than one day) to ‘most or all of the time’ (5-7days).
Higher scores indicate higher levels of depressive symptomatology with scores
greater than 15 indicating potentially significant levels of depression (Weissman,
Sholomskas, Pottenger, Prusoff and Locke, 1977). The CES-D has demonstrated
validity as a screening tool for detecting groups at high risk for depression (Radloff,
1977). The scale has high internal consistency, adequate test-retest repeatability and
good construct validity in both clinical and community samples (Sayetta and
Johnson, 1980; Weissman, et al., 1977). In this study, the cronbach alpha coefficient
was 0.93.
d. Sleep quality
We used the Pittsburgh Sleep Quality Index (PSQI), a 19 item instrument, to
measure subjective sleep quality over the past month (Buysse, Reynolds, Monk,
Berman and Kupfer, 1989. The PSQI assesses seven sleep quality components: sleep
quality, sleep latency, sleep duration, sleep efficiency, sleep disturbance, use of sleep
medication and daytime sleep dysfunction. A total sleep quality score is computed
by summing the seven component scores. Higher scores indicate poorer sleep
quality. A total sleep quality score above 5 has a diagnostic sensitivity of 89.6% and
specificity of 86.5% in differentiating good from poor sleepers. The Cronbach alpha
coefficient for this index was 0.76.
e. Medical late effects and other comorbidities
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We developed questions specifically for this study to screen for late effects
associated with leukemia and its treatment and other medical comorbidities that may
be associated with fatigue. Questions were adapted from two large epidemiologic
survey questionnaires (Robison, et al. 2002, Bernstein, et al. 2002) and developed in
consultation with an epidemiologist, a pediatric neuro-psychologist, a pediatric
oncologist, a neurologist, an endocrinologist and specialists at CHLA (LIFE medical
team) in long-term follow-up of childhood cancer survivors. For most conditions, we
used the following format:
“Have you ever been told by a doctor or other health care professional that
you have or have had (name of medical condition)? How old were you when you
were first diagnosed with this condition? Are you currently taking prescription
medication for this condition? Are any of your current activities limited by this
condition? (no; yes, limited a little; yes, limited a lot)”. Three questions were
developed to assess further cognitive functioning, including memory problems,
concentration problems and use of special education services,
f. Disease and treatment information
A nurse researcher abstracted from each subject’s medical record,
date of birth, date of diagnosis, protocol number, total dose of anthracycline, dates
and total dose of cranial irradiation, relapse and bone marrow transplant history and
date of last cancer treatment.
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4. Statistical analyses
Standard procedures were used to compute scale and subscale scores on the CES-
Depression, Piper Fatigue Scale, POMS, SF-36, and Pittsburgh Sleep Index. Body
mass index (BMI) was estimated using Quetelet’s index (weight in kilograms
divided by height in meters squared). Individuals with BMI scores of 30 or more
were categorized as obese (World Health Organization, 1997).
Since we used therapeutic risk groups (low, moderate and high risk treatment
protocols) as a surrogate measure of treatment intensity, we moved patients who had
relapsed into the high risk group because of their exposure to additional treatment.
Subjects were classified as cognitively impaired if they had ever been diagnosed
with a learning disability, received special education/resource services or reported
having significant memory or concentration problems (defined as 3 or more on a 0-4
scale). We analyzed cranial irradiation as a dichotomous (yes/no) and categorical
variable (no radiation, < 1800 cGy, and > 1800 cGy). We categorized subjects as
having sleep problems when PSQI total sleep quality scores were 5 or more. We
created an ordinal co-morbidity variable (0, 1,2 or more) to represent data collected
from the open-ended question “are there any other medical conditions that affect
your current health status?” We classified survivors as having pain when SF-36
pain subscale scores fell below 72, the 25th percentile for the general population
(Ware, et al. 1993).
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Descriptive statistics (means, standard deviations, frequencies) were
calculated for all variables. Chi-square tests were used to explore differences
between participants and non-participants. Cronbach’s alpha coefficients were
calculated for individual measures and subscales to test internal consistency. A
correlation matrix of fatigue variables, including the Piper Fatigue total fatigue score
and subscale scores, POMS inertia subscale, SF-vitality subscale, and the Symptom
Distress single fatigue item, was created to establish validity of the Piper Fatigue
Scale for this population. Due to the high collinearity between fatigue and
depression, we used fatigue and depression as dependent variables and ran separate
regression analyses for each. We calculated a kappa statistic to determine the
agreement between the subjects’ fatigue and depression classifications. We used
unconditional logistic regression methods to calculate odds ratios (OR) and
corresponding 95% confidence intervals (Cl) to estimate the risk of fatigue and the
risk of depression. We classified subjects as fatigued when Piper total fatigue scores
were 4 or higher. We classified subjects as depressed when CES-depression scores
were greater than 15 (Weissman, et al. 1977). Bivariate logistic regression analyses
were conducted to assess the association of demographic, disease/treatment and late
effects/co-morbidities variables with fatigue and with depression. Tests of linear
trend were calculated across ordinal categories of increasing exposure. Multiple
logistic regression analysis, using a forward elimination procedure, was used to
identify significant predictive factors associated with fatigue and depression. Pearson
correlations were used to evaluate the association between fatigue and depression
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and SF-36 quality of life scales. Paired t-tests were used to examine the mean
differences between SF-36 subscales. Two sample t-tests were used to compare
mean SF-36 scores for the fatigued and non-fatigued groups.
All statistical testing used an alpha of 0.05 (two-sided). All data analysis was
conducted using SAS (Version 6.12) (SAS Institute; Cary, NC) and Epilog
(Epicenter Software; Pasadena, CA) statistical software packages.
C. Results
1. Subjects
The CHLA Cancer Registry and the Cancer Surveillance Program (CSP) at
USC identified 364 eligible subjects. We interviewed 161 (44%) long-term
survivors. Of the 203 survivors who did not participate, 119 (52%) were lost to
follow-up, 36 (18%) were too busy or not interested, 29 (14%) agreed to participate
but never returned their consent form, 13 (6%) had severe cognitive impairments and
6 (3%) found it too difficult to talk about their past illness. Survivors lost to follow-
up were more likely to be male (p = 0.05) and diagnosed earlier (p = 0.02) than those
not lost to follow-up. Half of the survivors lost to follow-up had been diagnosed 20
or more years ago. We did not find a higher loss to follow-up rate among any
specific race/ethnicity group. Participants, compared to non-participants, were more
often female (p = 0.0001), white (p = 0.06), older at diagnosis (chi sq p value=0.02)
and off-therapy for a shorter period of time (p = 0.02) (Table 9).
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Table 9. Demographic, Diagnosis and Treatment Characteristics of Eligible Subjects
Eligible
subjects
N
Participants
N (%)
Lost to
Follow-up
N (%)
Refusals
N (%) p-valuea
Total 364 161 (44) 119(33) 84 (23)
Current age 0.38
18-19 70 34 (49) 19 (27) 17 (25)
20-24 128 57 (44) 39 (30) 32 (25)
25-29 103 39 (38) 37 (36) 27 (26)
>30 63 31 (49) 24 (38) 8(13)
Gender 0.0001
Male 205 74 (36) 76 (37) 55 (27)
Female 159 87 (54) 43 (28) 29 (28)
Raceb 0.06
White 152 79 (53) 46 (30) 26 (17)
Hispanic 156 63 (41) 51 (33) 43 (28)
Other 50 19 (39) 17 (34) 14 (29)
Age at diagnosis 0.02
<3 97 37 (38) 28 (29) 32 (33)
4-6 122 46 (37) 47 (39) 29 (24)
7-9 73 41 (56) 24 (33) 8(11)
>10+ 72 37 (51) 20 (28) 15 (21)
Cranial irradiation0 0.13
No 111 56 (50) 31 (28) 24 (22)
Yes 246 103 (42) 86 (35) 57 (23)
Years since diagnosis 0.02
<10 17 10 (59) 5(29) 2(12)
10-14 77 41 (53) 20 (26) 16(21)
15-19 125 59 (47) 33 (26) 33 (26)
>20+ 145 51 (35) 61 (42) 33 (23)
a p-value = chi-square, comparing participants to non-participants. Non-participants include subjects
lost to follow-up and refusals.
b 5 subjects who were lost to follow-up and 1 subject who refused to participate are missing
information on race
°2 subjects who participated, 2 subjects who were lost to follow-up and 3 subjects who refused to
participate are missing information on cranial irradiation
Among the 161 survivors who were interviewed, 74 (46%) were males, 87
(54%) were females and their ages ranged from 18-41 years. The vast majority of the
survivors were working or going to school with only 18 (11%) reporting no work or
school activities. Personal income was low; nearly half (49%) of the survivors
reported an income of less than $10,000. The majority of subjects (80%) were
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unmarried. Twenty-nine (18%) subjects had children with eleven being single
parents.
2. Diagnosis and treatment factors
The average age at diagnosis was 7.4 years (range, 0-18 years; mode =
3years). Nearly half of the subjects (49%) were treated per high-risk protocols.
Sixty-five percent of the survivors (n=103) received cranial irradiation with the
majority (81%) of these survivors having received a total dose of 1800 cGy. Two-
thirds of the survivors (n=104) received anthracyclines (range 75-580mg/m2) with 24
receiving a total cummulative dose of 350m/m2 or more. Twenty patients
experienced a relapse. Twelve survivors had received a bone marrow transplant.
Average time off-therapy was 13.9 years (sd = 4.5, range = 4 to 23 years).
3. Late Effects and other comorbidities
The number of late effects/medical comorbidities reported per subject ranged
from 0 to 11 (mean = 4, sd = 3). Only fifteen survivors reported no late
effects/medical comorbidities. Among the specific health conditions we assessed
(hearing loss, vision problems, neuro-cognitive impairments, chronic headaches or
migraines, seizures, hepatitis C, obesity, anemia, cardiac problems, exercise induced
symptoms, thyroid abnormalities, sleep problems, pain, second malignancy, surgical
procedures, growth hormone deficiency, menopausal symptoms and gonadal failure),
the most frequent were sleep problems (n=78, 48%), surgery following cancer
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therapy (n=63, 39%), neuro-cognitive impairments (n=62, 39%), pain (n=49, 30%),
obesity (n=46, 29 %) and frequent headaches/migraines (n=49, 30%). In response to
an open-ended question about additional comorbidities, 41% of the subjects reported
one or more medical conditions. For example, 18 survivors reported respiratory
problems (sinus, allergy, asthma), 15 reported back problems, 15 reported physical
injuries (knee, wrist, hip, head), and 12 reported gastrointestinal problems (colitis,
gastro-esophageal reflux disease, bulimia).
Survivors of ALL had a CES-depression mean score of 12.6 (sd 11.7). When
categorized as depressed and non-depressed (cut-point, CES-depression score =>16),
50 survivors (31%) were classified as depressed. These results are in contrast to
community norms, in which CES-depression mean scores range from 7.8-9.9 (sd 7.5-
9.3) and 20% have CES depression scores of 16 or above (Radloff, 1977). CES-
depression mean scores for severely depressed populations fall in the range of 24-38
(Weissman, et al., 1977; Craig and Van Natta, 1976). Survivors in this study who
were classified as depressed had CES-D mean scores of 27.2 sd = 9.1.
4. Fatigue
On the Symptom Distress Scale, subjects reported an average of four
symptoms that were causing them some degree of distress. Fatigue was the most
frequently reported symptom (61%), followed by mood (58%), concentration (56%)
and insomnia (56%). Distress levels were greater for fatigue than any other symptom
(mean distress score = 2.3, sd 1.2, range 1-5). Among the subjects who reported
fatigue symptoms, 58% rated their level of distress at 3 or more.
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Survivors’ mean score for the POMS fatigue-inertia subscale was 7.2 (sd
6.3). Using college student norms (McNair, Lorr and Droppleman, 1992), we found
that survivors’ scores fell well within the normal range.
The SF-36 vitality mean score for survivors (mean vitality score = 63.4, sd
23.2) was slightly higher (more energy) than the published norms for the general
population in the United States (mean vitality score = 61.3, sd = 20.2) (Ware, et al.
1993). However, using the recommended cut-point of 50 or below on the SF-36
vitality scale (Bower, et al. 2000) to dichotomize subjects as fatigued and not-
fatigued, we found that 30% of the survivors (n=48) were fatigued, while 25% of the
general population falls within this fatigued category (Ware, 1993).
The R-PFS, although never previously used with survivors of childhood
cancer, demonstrated excellent reliability. Cronbach alpha for the entire scale was
0.98, with subscale coefficients ranging from 0.92-0.97. The R-PFS subscales were
highly correlated with each other (Pearson correlation, r = 0.73-0.94). We observed
a high correlation between the R-PFS and the other fatigue measures: SF-36 vitality
subscale (r = -0.80), POMS energy scale (r=0.85) and Symptom Distress fatigue
single item (r = 0.71) (all p-values < 0.001).
Table 10 shows the distribution of the R-PFS scores.
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Piper Fatigue
Scale
Total
Fatigue
Behavior
subscale
Affective
subscale
Sensory
subscale
Cognitive
subscale
All subjects
(n=161)
2.6 (2.2) 2.0 (2.3) 2.4 (2.6) 3.0 (2.4) 2.8 (2.2)
Not-fatigued
(n=113)
1.4 (1.0) 0.8 (0.8) 1.0 (1.1) 1.8 (1.5) 1.8 (1.5)
Fatigued3
(n=48)
5.4 (1.3)c 5.0 (1.8)° 5.8 (1.8)° 5.8 (1.6)° 5.2 (1.8)°
Not-depressed
(n=l 11)
1.5 (1.4) 1.0 (1.3) 1.3 (1.6) 2.0 (1.8) 1.9 (1.6)
Depressed6
(n=50)
4.8(1.83)° 4.3 (2.2)° 5.0 (2.4)° 5.2 (2.1)° 4.8 (2.0)°
Fatigued3 and
depressed6
(n = 38)
5.6 (1.3)d 5.1 (1.8) 6.1 (1.7)d 6.0 (1.7) 5.3 (1.9)d
Fatigued3 and
not depressed
(n=10)
4.7 (0.8) 4.4 (1.4) 4.6 (1.7) 5.3 (1.3) 4.4 (1.0)
°t-test p-value < 0.0001; t-test p-value < 0.05
The R-PFS total mean score for survivors was 2.6 (sd = 2.2, range 0-8.5). Mean
scores for the R-PFS subscales ranged from a low of 2.0 (behavior subscale) to a
high of 3.0 (sensory subscale) (paired t-test p-value = 0.001). Fatigue scores were
skewed to the left with 42% of subjects (n = 67) reporting total fatigue scores <1 and
only 6% (n = 10) reporting scores > 7, i.e., severe level of fatigue. When survivors
were categorized as fatigued or non-fatigued (total fatigue score, cut-point = 4), 48
subjects (30%) were classified as fatigued and 113 (70%) were classified as non
fatigued. R-PFS total and subscales mean scores were significantly higher for the
fatigued group than the non-fatigued group (all t-test p-values < 0.0001).
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5. Interrelationship between fatigue and depression
Piper total and subscale scores were highly correlated with CES-depression
scores (Pearson r = 0.67-0.75). We found a fair amount of agreement when we
examined the survivors who were classified as fatigued/non-fatigued and the
survivors who were classified as depressed/non-depressed (Kappa = 0.68, 95% Cl =
0.55-0.80). Among the 161 survivors, 139 (87%) were classified as being fatigued
and depressed or not fatigued and not depressed. Among the 48 survivors who were
classified as fatigued, 38 (79%) were also classified as depressed. Only ten of the 48
fatigued subjects (21%) were not classified as depressed. Among the fifty depressed
subjects, only twelve (24%) were not classified as fatigued. When we compared
those subjects who were both fatigued and depressed (n=38) to those who were
fatigued but not depressed (n=10), we found higher fatigue scores among those who
were both depressed and fatigued (Table 10). Significant differences were present
for the R-PFS total score (t-test p-value = 0.03, mean difference = 1.0), affective
subscale (t-test p-value = 0.2, mean difference = 1.5) and cognitive subscale (t-test p-
value = 0.05, mean difference = 0.9) (Table 10).
6. Bivariate analyses of fatigue and depression
Bivariate analyses examining the relationship between demographic factors
and fatigue and depression are summarized in Table 11.
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Table 11.
N (%)
Fatigued3
N (%) OR (95% CI)C
Depressed”
N (%) OR (95% CI)C
Age at interview (years)
18-19 34 (22) 9(26) 1.00 7(21) 1.00
20-24 57 (35) 12(21) 0.74 (0.27-2.00) 18(32) 1.78 (0.65-4.85)
25-29 39 (24) 16(41) 1.93 (0.72-5.22) 14 (36) 2.16(0.75-6.22)
30-41 31 (19) 11 (35) 1.53 (0.53-4.41) 11(35) 2.12(0.70-6.44)
Trend p-value = 0.13 Trend p-value = 0.17
Gender
male 74 (46) 16 (22) 1.00 19 (26) 1.00
female 87 (54) 32 (37) 2.11 (1.04-4.27) 31 (36) 1.60(0.81-3.17)
Ethnicity
white 79 (49) 17 (22) 1.00 18 (23) 1.00
Hispanic 63 (39) 26 (41) 2.56(1.23-5.34) 25 (40) 2.23(1.08-4.62)
other 19(12) 5(26) 1.30 (0.41-4.13) 7(37) 1.98 (0.68-5.76)
Education
< high school
graduate
43 (27) 14 (33) 1.00 14 (33) 1.00
some college 80 (50) 23 (29) 0.84 (0.38-1.86) 27 (34) 1.06 (0.48-2.32)
> college
graduate
38 (24) 11 929) 0.84 (0.33-2.18) 7(24) 0.64 (0.24-1.72)
Marital status
single 129 (80) 41 (32) 1.00 42 (33) 1.00
Married 32 (20) 7 (22) 0.60 (0.24-1.50) 8(25) 0.69(0.29-1.67)
Employment status
work full-time 56 (35) 14 (25) 1.00 13 (23) 1.00
work part-time 13(8) 5(38) 1.88(0.53-6.68) 7(54) 3.86(1.10-13.53)
student 34 (21) 10 (29) 1.25 (0.48-3.25) 9(26) 1.19(0.45-3.18)
Student and
working
40 (25) 7(18) 0.64 (0.23-1.76) 9(23) 0.96 (0.37-2.53)
not student or
employed
18(11) 12 (67) 6.00(1.90-18.98) 12 (67) 6.62 (2.07-21.10)
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Table 11. Demographic characteristics, distribution and association with fatigue and depression
(continued)
N (%)
Fatigued3
N (%) OR (95% CI)c
Depressed1 1
N (%) OR (95% CI)C
Incomed
<10,000 79 (49) 28 (35) 1.00 28 (35) 1.00
10,000-19,999 24 (15) 6 925) 0.61 (0.22-1.71) 9(38) 1.09 (0.42-2.82)
20,000-39,999 33 (21) 9(27) 0.68 (0.28-1.67) 8(24) 0.58(0.23-1.46)
<40,000 23 (15) 4(19) 0.38 (0.12-1.24) 4(14) 0.38(0.12-1.24)
Trend p-value = 0.0.9 Trend p-value = 0.07
Offspring
no 132 (82) 34 (260 1.00 38 (29) 1.00
yes 29(18) 14 (48) 2.69(1.18-6.15) 12(41) 1.75 (0.76-4.00)
Marital status and offspring
Not married,
no children
118(73) 32 927) 1.00 35 (30) 1.00
Not married,
children
11(7) 9(82) 12.09 (2.48-59.01) 7(64) 4.15(1.14-15.08)
Married, no
children
14(9) 2(14) 0.45 (0.01-2.11) 3(21) 0.65 (0.17-2.46)
Married,
children
18(11) 5(28) 1.03 (0.34-3.13) 5(28) 0.91 (0.30-2.75)
“ fatigued = Piper total fatigue score >4
b depressed = CES-depression score >=16
°OR=odds ratio, Cl = 95% confidence interval
d 2 subjects missing information on income
Females were twice as likely as males to be fatigued (OR = 2.11, 95% Cl = 1.04 -
4.27). While not significant, the risk of depression was also greater for females than
males (OR = 1.60, 95% Cl = 0.81-3.17). Hispanic subjects had a significantly higher
risk of fatigue (OR = 2.56, 95% Cl = 1.23-5.34) and depression (OR = 2.23, 95% Cl
= 1.08-4.62) when compared to non-Hispanic white subjects. Subjects who were
neither working and not in school had the greatest risk of fatigue (OR = 6.00,95%
Cl = (1.90-18.98) and depression (OR = 6.62, 95% Cl = 2.07-21.10). Subjects who
worked part-time were more likely to be depressed than those who worked full-time
(OR = 3.86, 95% Cl = 1.10-13.53). Part-time work status did not increase one’s risk
86
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of fatigue. Having children increased one’s risk of fatigue (OR = 2.69,95% CI=
1.18-6.15) but did not significantly increase one’s risk of depression (OR=1.75, 95%
Cl = 0.76-4.00). Single parents faced a greater risk of fatigue and depression.
Eighty-two per-cent of the single parents were fatigued while only 28% of the
married subjects with children were fatigued (p = 0.01). Similar but non-significant
results were found for depression (p = 0.06). Current age, marital status, level of
education and income status were unrelated to fatigue or depression.
Bivariate analyses examining the relationship between disease/treatment
factors and fatigue and depression are summarized in Table 12.
Table 12. Disease and treatment factors, distribution and association with fatigue and depression
N (%)
Fatigued”
N (%) OR (95% CI)c
Depressedb
N (%) OR (95% CI)C
Age at diagnosis (years)
<3 37 (23) 11 (30) 1.00 14 (38)
4-6 29(18) 6(21) 0.62 (0.20-1.93) 6(21) 0.43 (0.14-1.31)
7-9 46 (29) 16 (35) 1.26 (0.50-3.20) 14 (30) 0.72 (0.29-1.79)
>10+ 49 (30) 15 (31) 1.04(0.41-2.65) 16 (33) 0.80 (0.33-1.95)
Therapeutic risk group
Low 28(17) 8(17) 1.00 7(14) 1.00
Moderate 54 (34) 9(19) 0.50 (0.17-1.49) 14 (28) 1.05 (0.37-3.00)
High 79 (49) 31(65) 1.62(0.63-4.12) 29 (58) 1.74 (0.66-4.59)
Trend p-value = 0.07 Trend p-value = 0.16
Cranial irradiationd
No 56 (35) 12(21) 1.00 16 (29) 1.00
Yes 103(65) 36 (35) 1.97 (0.93-4.20) 34 (33) 1.23 (0.61-2.51)
Cranial irradiation,
total d o se d
0 56 (35) 12(21) 1.00 16 (29) 1.00
1800** 84 (53) 29 (35) 1.93 (0.89-4.22) 26 (31) 1.12(0.53-2.35)
2400+ 19(12) 7(37) 2.14 (0.69-6.62) 8(42) 1.82 (0.62-5.35)
Trend p-value = 0.19 Trend p-value = 0.34
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Table 12. Disease and treatment factors, distribution and association with fatigue and depression
(continued)
N (%)
Fatigued3
N (%) OR (95% CI)c
Depressed6
N (%) OR (95% CI)c
Anthracycline, total cummulative dose mg/m2e
(range = 0-570, mean = 150)
0 53 (34) 16 (30) 1.00 14 (26) 1.00
75-349 80 (51) 21 (26) 0.82(0.38-1.78) 27 (34) 1.39 (0.49-3.96)
350+ 24 (15) 11 (46) 1.96 (0.72-5.29) 8(33) 1.42(0.66-3.06)
Trend p-value = 0.18 Trend p-value = 0.44
Relapse*
No 140 (88) 38 (27) 1.00 40 (29) 1.00
Yes 20(13) 10 (50) 2.68 (1.04-6.96) 10 (50) 2.50 (0.97-6.47)
Bone marrow transplant8
No 148 (93) 45 (30) 1.00 47 (32) 1.00
Yes 12(7) 3(25) 0.76 (0.20-2.95) 3(25) 0.72 (0.19-2.77)
Years since last treatment1 1
<10 34(22) 12 (35) 1.00 11 (32) 1.00
11-15 70 (45) 18 (26) 0.64 (0.27-8.00) 21 (30) 0.88(0.36-2.12)
16+ 53 (34) 17 (32) 0.87 (0.35-2.15) 17 (32) 0.99 (0.39-2.48)
Years since diagnosis
<10 10(6) 2(20) 1.00 3(30) 1.00
10-14 41(25) 11(27) 1.47 (0.27-8.00) 12 (29) 0.97 (0.21-4.37)
15-19 59 (37) 17 929) 1.62 (0.31-8.42) 17 929) 0.94 (0.22-4.09)
>20+ 51 (32) 18 (35) 2.18(0.42-11.39) 18 (35) 1.27 (0.29-5.53)
Trend p-value = 0.25 Trend p-value = 0.56
Length of treatment (years)1
< 3years 129 (92) 36 (28) 1.00 31(24) 1.00
> 3 8(11) 2(18) 0.57(0.12-2.79) 2(18) 0.70(0.14-3.34)
“ Fatigued = Piper total fatigue score >4
b Depressed = CES-depression score >16
cOR=odds ratio, Cl = 95% confidence interval
d 2 subjects missing information on cranial irradiation
e 4 subjects missing information on adriamycin, total dose
f 1 subject missing information on relapse
8 1 subject missing information on bone marrow transplant
h 4 subjects missing information on years since last treatment
1 subjects who have relapsed were excluded
Relapse was the only demographic variable to reach significance. Subjects who
relapsed were two and a half times more likely to be fatigued than those who had
never relapsed (OR= 2.68, 95% Cl = 1.04-6.96). Results were similar, although non-
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significant, for depression (OR = 2.50, 95% Cl = 0.97-6.47). Although not reaching
significance, there was nearly a two-fold increase in the risk of fatigue among
survivors who received cranial irradiation (OR = 1.97, 95% Cl = 0.93-4.20). We
found no significant modifying effect of age (<3 and =>3 years at diagnosis) or
gender on the relationship between cranial irradiation and fatigue. Cranial
irradiation was not associated with an increase in depression (OR=1.23, 95% Cl =
0.61-2.51). Subject’s age at diagnosis, cranial irradiation dose, anthracycline total
dose, history of bone marrow transplant, years since diagnosis and years since last
treatment were not associated with fatigue or depression. Among the patients who
had never relapsed, we found no relationship between length of therapy and risk of
fatigue and depression.
Bivariate analyses examining the relationship between late
effects/comorbidity factors and fatigue and depression are summarized in Table 13.
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Table 13. Distribution of fatigue and depression by self-reported late effects and other comorbidities
N(% )
Fatigued3
N (%) OR
(95%CI)C
Depressed"
N (%) OR (95%CI)C
Hearing loss
no 146 (91) 43 (29) 1.00 45 (31) 1.00
yes 15 (9) 5(33) 1.20 (0.39-3.71) 5(33) 1.12(0.36-3.47)
Vision problems
no 140 (87) 41 (29) 1.00 39 (28) 1.00
yes 21 (13) 7(33) 1.21 (0.45-3.21) 10 (48) 2.33 (0.92-5.93)
Neuro-cognitive impairments d
No 99 (61) 20(20) 1.00 20 (20) 1.00
Yes 62 (39) 28 (45) 3.25(1.61-6.56) 30 (48) 3.70(1.84-7.45)
Chronic headaches or migraines
No 112(70) 21 (19) 1.00 23 (21) 1.00
Yes 49 (30) 27 (55) 5.32 (2.55-11.11) 27 (55) 4.75 (2.30-9.82)
Seizures
No 148 (92) 40 (27) 1.00 42 (28) 1.00
Yes 13(8) 8(62) 4.32(1.33-13.99) 8(62) 4.04(1.25-13.05)
Hepatitis C
No 154 (96) 46 (30) 1.00 47 (31) 1.00
Yes 7 (4 ) 2(29) 0.94 (0.18-5.02) 3(43) 1.71 (0.37-7.93)
Obesitye
No 115(71) 26 (23) 1.00 27 (24) 1.00
Yes 46 (29) 22 (48) 3.14(1.52-6.48) 23 (50) 3.26(1.59-6.70)
Anemia in the past 12 months
No 148 (92) 42 (28) 1.00 43 (29) 1.00
Yes 13(8) 6(46) 2.16(0.69-6.82) 7(54) 2.85 (0.91-8.97)
Cardiac problems'
No 158 (98) 47 (33) 1.00 0
Yes 3(2) 1 (33%) 1.18(0.11-13.34) 0
Exercise induced symptoms g
No 119(74) 27 (23) 1.00 30 (25) 1.00
Yes 42 (26) 21 (50) 3.41 (1.62-7.15) 20 (48) 2.70(1.30-5.62)
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Table 13. Distribution of fatigue and depression by self-reported late effects and other
comorbidities (continued)
N (%)
Fatigued3
N (%) OR (95%CI)C
Depressed”
N (%) OR (95%CI)C
Thyroid abnormality1 1
No 148 (92) 40 (27) 1.00 41 (28) 1.00
Yes 13(8) 8(62) 4.32(1.33-13.99) 9(69) 5.87(1.71-20.12)
Sleep problems 1
No 82 (51) 9(11) 1.00 22 (20) 1.00
Yes 78 (49) 38 (49) 7.71 (3.39-17.54) 28 (57) 5.46 (2.62-11.35)
Pain1
No 112(70) 21 (19) 1.00 22 (20) 1.00
Yes 49 (30) 27 955) 5.32 (2.5-11.12) 28 (57) 5.46 (2.62-11.35)
Second malignancyk
no 156 (97) 47 (30) 1.00 48 (31) 1.00
yes 5 (3 ) 1(20) 0.58 (0.07-5.33) 2(40) 1.50 (0.24-9.27)
Surgical procedure(s) following end of cancer treatment
no 98 (61) 22 (22) 1.00 26 (27) 1.00
yes 63 (39) 26 941) 2.43 (1.22-4.84) 24 (38) 1.70 (0.87-3.36)
Other comorbidities
no 95 (59) 26 (27) 1.00 27 (28) 1.00
1 48 (30) 13 (27) 0.99 (0.45-2.15) 14 (29) 1.04(0.48-2.23)
2 or more 18(11) 9(50) 2.65 (0.95-7.42) 9(50) 2.52 (0.90-7.03)
Growth hormone deficiency
No 136 (86) 37 (2) 1.00 39 (29) 1.00
Yes 24 (15) 11 (46) 2.26 (0.93-5.50) 10 (43) 1.89 (0.77-4.68)
Menopausal symptoms1
no 76 (88) 23 (30) 1.00 24 (32) 1.00
yes 10(12) 8(80) 9.22(1.82-46.81) 6(60) 3.25 (0.84-12.59)
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Table 13. Distribution of fatigue and depression by self-reported late effects and other
_________comorbidities (continued)_____________________________________________
N (%)
Fatigued2
N (%) OR (95%CI)C
Depressedb
N (%) OR (95%CI)°
Gonadal failure
no 154 (96) 43 (28) 1.00 46 (30) 1.00
yes 7(4) 5(71) 6.45 (1.21-34.53) 4(57) 3.13. (0.67-14.55)
“ Fatigued = Piper total fatigue score >4
b Depressed = CES-depression score >16
°OR=odds ratio, Cl = 95% confidence interval
d neuro-cognitive impairments: 34 survivors had a diagnosis of a learning disability, 15 survivors
received special education services, and 13 survivors reported significant problems with their memory
or concentration
eobesity, defined as body mass index >30
fcardiac problems: 1 survivor reported a stroke, 2 survivors reported congestive heart failure
8 exercise induced symptoms were defined as severe chest pain, shortness or breath or irregular
heartbeat with exercise
h thyroid problems: 4 survivors reported hyperthyroidism, 7 survivors reported hypothyroidism, 8
survivors reported thyroid nodules and 1 survivor reported an enlarged thyroid gland
‘sleep problems defined as Pittsburgh Sleep Quality Index score > 5, 1 survivor missing sleep index
score
J pain defined as SF-36 pain subscale score < 72
k second malignancies: 3 survivors reported a brain tumor, 1 survivor reported basal cell carcinoma
and 1 survivor reported a parotid tumor
'analyses were restricted to females
The number of late effects/medical conditions reported by a subject was highly
associated with risk of fatigue and depression (trend p-value < 0.001) (data not
shown). Cognitive problems, frequent headaches/migraines, history of seizures,
obesity, cardiac problems, exercise-induced symptoms, thyroid abnormalities, sleep
problems and pain were significantly associated with fatigue and depression. Three
additional factors, gonad failure, menopausal symptoms and surgical procedures
following cancer treatment, increased the risk of fatigue but were unrelated to
depression.
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7. Multivariate models for fatigue and depression
Results of multivariate modeling are presented in Table 14.
Table 14. Multivariate models for fatigue and depression
Fatigue Depression
Model la, demographic factors
OR (95% CI)a
Model lb, demographic factors
OR (95% CI)a
Married1 1 0.22 (0.06-0.86) Hispanicb 2.30(1.04-5.07)
Children6 5.95 (1.73-20-51) Part time work6 4.43(1.22-16.08)
Not working/not a
student0
5.85 (1.70-20.19) Not working/not a
student6
5.43 (1.65-17.84)
Model 2a, disease and treatment factors Model 2b, disease and treatment factors
Relapse 2.68 (1.04-6.96) No factors
Model 3a, late effects/comorbidity factors Model 3b, late effects/comorbidity factors
Sleep dysfunction 6.01 (2.44-14.83) Sleep dysfunction 4.93 (2.09-11.63)
Pain 5.18(2.16-12.38) Pain 5.04(2.15-11.85)
Obesity 3.56(1.45-8.72) Obesity 3.42(1.43-8.18)
Cognitive impairment 2.70(1.15-6.30) Cognitive impairment 2.96(1.30-6.77)
Model 4a, demographic, disease and treatment,
and late effects/comorbidity factors
Model 4b, demographic, disease and treatment,
and late effects/comorbidity factors
OR (95% CI)a OR (95% CI)a
Marriedd 0.11 (0.02-0.50)
Children6 5.80(1.30-25.82)
Sleep dysfunction 6.15 (2.33-16.22) Sleep dysfunction 4.93 (2.09-11.63)
Pain 5.56 (2.13-14.48) Pain 5.04(2.15-11.85)
Obesity 3.80(1.41-10.26) Obesity 3.42(1.43-8.18)
Neuro-cognitive
impairment
2.56(1.02-6.38) Neuro-cognitive
impairment
2.96(1.30-6.77)
Exercise induced
symptoms
2.98(1.11-8.02)
a OR = odds ratio; Cl = 95% confidence interval
b referent group is white
0 referent group is working full time
d referent group is unmarried
e referent group is no children
In the first set of models, we included all demographic factors. Significant factors
found to be independently associated with fatigue (Model la) were having children,
not working or attending school and being married. Gender and ethnicity were not
multivariably significant factors in the multivariate model for fatigue. With
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depression (Model lb), we continued to find a significant association with Hispanic
ethnicity, working part-time and not working or attending school.
The second set of models included all diagnosis and treatment factors. These
results were similar to our bivariate results. Relapse increased survivors’ risk of
fatigue (Model 2a) while no treatment or disease factors were significantly
associated with depression (Model 2b).
The third model set included all late effects and medical comorbidities. We
found that the multivariate model for fatigue (Model 3a) and the multivariate model
for depression (Model 3b) had four common factors. Neuro-cognitive impairments,
obesity, sleep problems and pain significantly increased survivors’ risk of fatigue
and depression.
In the final phase of developing a predictive model for fatigue and
depression, we included all demographic variables, disease/treatment factors and late
effects/comorbidity factors in the models. The final models for depression and
fatigue are presented in Table 14. The final predictive model for fatigue includes
seven significant factors (Model 4a). Sleep problems, pain, obesity, cognitive
problems, exercise-induced symptoms and having children were found to increase
survivors’ risk of fatigue. Being married decreased survivors’ risk of fatigue.
The final predictive model for depression includes four significant factors
(Model 4b). Sleep problems, heart problems, pain, obesity and cognitive problems
increased the risk of depression. Once again the overlap between fatigue and
depression in the population is apparent because for all predictive factors for
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depression are present in the final model for fatigue. No disease/treatment factors
were found to be significant predictors for either depression or fatigue.
8. Quality of life
The distribution of SF-36 quality of life scores is presented in Table 15.
The survivors’ SF-36 subscale scores for the bodily pain, role function-physical,
general health and physical functioning fell significantly below the norms for the
general population. On the remaining subscales, vitality, role function-emotional,
mental health and social functioning, survivors’ scores fell within normal limits.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Table 15. SF-36, mean and standard deviations
SF-36 A ll
survivors
(n=161)
N ot
fatigued
survivors
(n=113)
Fatigued
Survivors
(n=48)
N ot
depressed
survivors
(n = l 11)
Depressed
survivors
(n=50)
Fatigued
and
depressed
(n=38)
US
norms
SF-36
subscales
M ean
(sd)
M ean
(sd)
M ean
(sd)
M ean
(sd)
M ean
(sd)
M ean
(sd)a
Mean
(sd)a
Vitality 63.4
(23.2)
74.1
(14.3)**
38.2
(20.5)**
72.1
(17.6)**
44.1
(22.5)**
36.9
(19.6)**
61.3
(20.2)
B odily Pain 77.5
(22.6)*
83.7
(17.3)
63.4
(27.2)**
83.9
(17.9)
63.4
(25.5)**
60.5
(27.3)**
81.4
(19.7)
Role-
Em otional
78.5
(36.4)
93.5
(20.3)**
43.1
(41.3)**
92.8
(20.3)**
46.6
(43.7)**
34.2
(29.2)**
82.2
(31.5)
Role-
Physical
81.8
(32.9)**
92.5
(20.3)
56.3
(42.1)**
90.8
(23.6)
61.5
(41.1)**
54.6
(41.9)**
89.3
(24.9)
General
Health
69.9
(23.5)**
79.6
(17.6)
4 7.0
(19.6)**
79.1
(18.3)
49.5
(21.0)**
45.3
(20.3)**
77.1
(17.3)
M ental
Health
75.6
(19.7)
83.8
(12.1)**
56.9
(21.7)**
84.0
(12.9)**
57.0
(19.7)**
53.1
(19.7)**
73.3
(18.0)
Physical
Functioning
88.3
(17.6)*
92.7
(13.8)
78.0
(21.0)**
93.1
(12.7)
77.6
(21.9)**
75.4
(22.3)**
92.0
(15.8)
Social
Functioning
84.7
(22.5)
94.1
(11.3)**
62.4
(26.4)**
93.3
(12.5)**
65.4
(27.4)**
58.7
(27.2)**
84.9
(20.8)
Physical
Com ponent
Scale
50.8
(8.9)**
53.0
(6.7)
45.5
(10.9)**
52.9
(6.4)
46.2
(11.5)**
45.2
(1.9)**
53.6
(7.3)b
Mental
Com ponent
Scale
49.8
(11.6)
55.1
(5.8)**
37.3
(12.2)**
54.8
(6.4)**
38.6
(12.7)**
34.9
(11.7)**
48.8
(10.2)b
a U S norms for m ales and fem ales, ages 25-34 years (N =474)
b U S norms for m ales and fem ales, ages 18-34 years
*compared to U S norms, t-test p-vlaue =<0.05
** compared to U S norms, t-test p-vlaue =<0.01
The R-PFS total and subscale scores and CES-depression scores were
negatively correlated with SF-36 mean scores (all p-values <0.0001). Higher levels
of fatigue and higher levels of depression were associated with a poorer quality of
life. In figure 5, we compare fatigued and non-fatigued survivors’ SF-36 mean scores
to published norms for males and females, ages 18-34 years. We found that all SF-36
mean scores for the non-fatigued survivors were higher than the normative values.
97
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Significant mean differences were found for the vitality, role function-emotional,
mental health and social functioning domains (all t-test p-values < 0.0001). With
the fatigued survivors, we found that all SF-36 mean subscale scores were
significantly below the normative values (p <0.0001). With the SF-36 summary
scores, non-fatigued survivors scored significantly higher than their peers on the
MCS (mean difference = 6.3, sd = 5.8, t-test p-value < 0.0001) while fatigued
survivors scored significantly lower than their peers on the MCS (mean difference
-11.6, sd = 12.2) and the PCS (mean difference = - 8.1, sd = 10.9).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Figure 5. SF-36 Mean Scores, Fatigue and Non-Fatigued Survivors
Compared to US Norms
1 0 0 -i
90 -
70
o
8
Fatigued survivors
- t B B — Non-latigucd survivors
US norms
GH SF PCS MCS VT BP RE RP MH PF
VT=vitality; BP=bodily pain; RE=role limitation-emotional; RP=role limitation-
physical; GH=general health; MH=mental health; PF=physical functioning;
SF=social functioning; PCS=physical component score; MCS=mental component
score
D. Discussion
This study is the first to report on the prevalence and correlates of fatigue
among long-term survivors of childhood ALL. We used both a multi-dimensional
measure, the Piper Fatigue Scale, and a norm-based uni-dimensional measure, the
SF-36 vitality subscale, to estimate the prevalence of fatigue in a population of
cancer survivors who were on average 24 years of age and 14 years from the end of
treatment. Thirty percent of the survivors reported moderate to severe levels of
fatigue and were classified as fatigued using recommended cut points for each
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measure. This rate of fatigue is similar to that reported for survivors of adult cancers
(range 17-30%) (Bower, et al, 2000; Curt, et al., 2000, Celia, Davis, Breibart and
Curt, 2001, Servaes, van der Werf Prins, Verhagen and Bleijenberg, 2001; Sadler, et
al., 2002) and for the general population (range 11-45%) (Lewis and Wessely, 1992).
In addition, using the SF-36 vitality and POMS fatigue-inertia subscales we found
mean levels of fatigue among ALL survivors to be within normal limits when
compared to age-approximate norms in the general population.
Our results are consistent with the other two studies that examined fatigue in
survivors of childhood cancer. Zeltzer and colleagues (1997) found no difference in
POMS fatigue-inertia subscale scores (fatigue) when survivors of ALL (n=552) were
compared to sibling controls (n=394). Langeveld et al. (2003) found that fatigue
levels among childhood cancer survivors (mixed cancer diagnoses, n= 1026) were no
different than a healthy peer-nominated control group.
In this study, we found that fatigue and depression were closely related.
Seventy-nine percent of the survivors who were fatigued were also classified as
depressed. Adult cancer studies report similar findings, with correlation coefficients
between off-treatment fatigue and depression ranging from 0.32-0.68 (Smets, et al.
1998; Andrykowski, Curran and Lightner, 1998; Loge, Abrahamsen, Ekeberg and
Kaasa, 1999; Howell, Radford, Smets and Shalet, 2000). The overlap of symptoms
was even stronger in this study, with a correlation coefficient between Piper total
fatigue and CES-depression scores of 0.75. Langeveld et al. (2003) also reported a
high crossover of symptoms in childhood cancer survivors. Using the
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multidimensional fatigue scale and a modified version of the CES-depression scale,
in which the ‘somatic and retarded activity’ domain questions were removed, they
still found a high correlation between fatigue and depression (correlation coefficient
= 0.61).
The cause and effect relationship between fatigue and depression is unclear.
Fatigue is a recognized symptom of clinical depression (American Psychiatric
Association, 1994) and chronic fatigue can lead to depression (Hays, 1991). Recent
studies suggest that fatigue and depression may originate from the same pathology,
such as drug-induced neuro-toxicities (Quesada, Talpaz, Rios, Kurzrock and
Gutterman, 1986; Simpson, Baldwin, Jackson and Bums, 1998; Gutstein, 2001;
Valentine and Meyers, 2001). This hypothesis may apply to survivors of ALL for
treatment of ALL includes central nervous system prophylaxis (CNS), the
administration of intrathecal chemotherapy with or without cranial irradiation. CNS
late effects, including functional (neuro-endocrine abnormalities and cognitive
impairments) (Copeland, et al. 1985; Mulhem, Wasserman, Fairclough and Ochs,
1988) and stmctural changes (cortical atrophy, mineralizing microangiography and
demyelination) (Bleyer and Griffin, 1980; Hopewell, 1998) have been documented
in survivors of ALL, especially among those exposed to cranial irradiation. It has
been proposed, although never tested, that a centrally mediated mechanism, such as
neurotransmitter dysfunction following CNS prophylaxis, may play some role in the
development of depression and fatigue (Chen, et al., 1998). Developmental factors
may also help to explain why the crossover of symptoms may be higher among
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survivors of childhood cancer than among survivors of adult cancers. The
survivorship issues facing young adult survivors of childhood cancer are known to
differ from those encountered by survivors of adult cancers (Richardson, Nelson and
Meeske, 2001). In this study, the cancer survivors are now young adults whose life
goals include establishing families, professional careers and financial security. If
persistent fatigue were to interfere with achievement of these goals, one might expect
to see higher levels of despair in this group than among survivors of adult cancers
who have already achieved many of life’s major goals before becoming ill.
The rate of depression for survivors of ALL was 30%, higher than the 20%
prevalence rate reported for the general population (Radloff, 1977). Although study
results regarding psychological sequelae among survivors of ALL have been mixed
(Mackie, Hill, Kondryn and McNally, 2000), some studies report increased
symptoms of depression (Lansky, List and Ritter-Sterr, 1986; Zeltzer et al., 1997;
Zebrack, et al., 2002). In a study of Hodgkin’s survivors (n=403) (15-78 years of
age; median time off therapy nine years), in which 37% reported that their energy
had not returned to normal, depression was found to be a significant predictor of a
failure to recover energy (Fobair et al., 1986). Our study found that the survivors
who were fatigued were 33 times more likely to be depressed than those who were
not fatigued (OR = 32.98, 95% Cl = 12.77-80.12). Fatigue levels were highest for
the survivors who were both fatigued and depressed. In the general population, the
longer an individual experiences fatigue the greater the risk of depression (Clare and
Blacker, 1986).
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In this study, we studied fatigue and depression as separate outcomes to
understand further how these concepts may be inter-related in this population of
survivors. The results of our univariate and multivariate analyses for fatigue and
depression showed many similarities. Most factors associated with fatigue were also
associated with depression while only a few factors were associated with one
outcome and not the other. This high cross-over was also present in the final models
for fatigue and depression. All factors in the final multivariate model for depression
(neuro-cognitive impairments, obesity, sleep problems and pain) were present in the
final model of fatigue.
In the final multivariate models, we found that two demographic factors,
marriage and children, were associated with fatigue but not depression. Although
few survivors were married and fewer had children, marriage reduced the risk of
fatigue while having children increased fatigue. Single parents face a greater risk of
fatigue and depression than married couples. Such vulnerability is also found in the
general population, where prevalence rates for psychiatric morbidity among single
parents is twice that of married parents (Caimey, Thorpe, Rietschlin and Avison,
1999). Several cancer studies have found that female gender (Smets, et al., 1998;
Andrykowski, Curran and Lightner, 1998; Langeveld, et al., 2003) and
unemployment (Langeveld, Grootenhuis, Voute, de Haan and van den Bos, 2003)
were associated with off-treatment fatigue. While these associations were present in
our univariate analyses, they did not remain significant in the final multivariate
models. Our final multivariate models for fatigue and depression included several
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late effects and comorbidity factors, including sleep problems, pain, neuro-cognitive
impairments, obesity and exercise-induced symptoms. Exercise-induced symptoms,
the only non-shared late effect/comorbidity factor, was associated with fatigue but
not depression. Langeveld et al. (2003) also found a significant association between
fatigue and medical late effects. Interpretations from this study are limited, however,
because late effects were defined globally as causing mild, moderate or severe
medical limitations. No data regarding specific late effects are available.
Pain and sleep problems were found to increase survivors’ risk of fatigue and
depression. These associations are well documented (Smets et al., 1998, Bower et al.
2000, Broeckel et al., 1998, Hann et al., 1997, Andrykowski, Curran and Lightner,
1998; Okuyama, et al., 2000; Jacobsen et al, 1999). The Practice Guidelines
developed by National Comprehensive Cancer Network (2003) have recognized both
sleep problems and pain as primary screening items in the assessment of cancer-
related fatigue. In this study, thirty percent (n=49) of the survivors reported pain
levels on the SF-36 pain subscale that fell below the 25th percentile for the general
population. In contrast, Oeffinger, et al. (2000) found pain in only 3% of a
population of young adult survivors of childhood cancer (n= 96, mixed diagnoses)
attending their transition follow-up clinic. While pain increased survivors’ risk of
fatigue and depression, we have limited information on the source of this pain.
Using our comorbidity data, we found a significant increase in pain among the 49
survivors who reported chronic headaches/migraines (OR= 2.93, 95% Cl = 1.44-
5.96). The association between pain and self-reported back problems (n=16) was
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non-significant (OR = 2.54, 95% Cl = 0.89-7.21). Pain was positively associated
with age (trend per year, OR = 1.10, 95% Cl = 1.01-1.19) and unrelated to age at
diagnosis, years from treatment and treatment protocol. Pain, its etiology,
relationship to past therapy and management need to be further evaluated in this
population.
Nearly 50% of the survivors in this study reported sleep problems while 15-
35% of the general population report such problems (Karacan, Thornby and
Williams, 1985). The Pittsburgh Sleep Quality Index scores indicate that survivors
with sleep problems have the greatest difficulties in subjective sleep quality (poorer
quality), sleep duration (less hours of sleep) and habitual sleep efficiency (fewer
hours of sleep compared to number of hours in bed). Such sleep disturbances in the
general population are manifestations of depression as well as risk factors for the
later development of depression (Gillin, 1998). While many factors, including pain,
depression, endocrine dysfunction and post-traumatic stress can disrupt sleep, sleep
quality among adult survivors of childhood cancer has never been systematically
studied hor is sleep a symptom routinely assessed in follow-up clinics.
Our finding that 30% of the survivors were obese is consistent with other
follow-up studies of young adult survivors of ALL (Oeffinger, Eschelman,
Tomlinson, Buchanan and Foster, 2001). Some studies find that children treated
with cranial irradiation are more likely to be obese than those treated with only
chemotherapy (Sklar, et al. 2000; Didi, 1995). In this study, risk of obesity increased
two-fold among survivors who had received cranial irradiation (OR = 2.11, 95% Cl
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= 0.97-4.57). While the specific mechanisms underlying obesity in survivors of ALL
are unclear, it has been postulated that subtle GH deficiencies secondary to cranial
irradiation may lead to alterations in body composition (decrease in muscle mass and
increase in adipose tissue), which in turn affect exercise capacity and physical
activity level (Warner, Bell, Webb and Gregory, 1998). Decreased energy
expenditure during exercise and reduced levels of physical activity have been
documented in survivors of ALL and are thought to contribute to the development
and maintenance of obesity (Mayer, Reuter, Dopfer and Ranke, 2000; Reilly, et al.
1998; Oeffinger, et al. 2001; Warner, et al. 1998). In one study, 42% of children
previously treated for ALL (n=146) reported some degree of exercise intolerance
(Jenny, Faragher, Morris-Jones and Woodcock, 1995). This finding is interesting
because 26% of our subjects reported ‘exercise-induced symptoms’, defined as
severe chest pain, palpitations and shortness of breath.
In this population, it is possible that such exercise-induced symptoms may
represent subclinical cardiac abnormalities. Survivors of ALL are at risk of
cardiomyopathy, ranging from minor electrocardiogram abnormalities to congestive
heart failure following anthracycline therapy (Lipshultz, et al. 1991). Prevalence and
severity of cardiac abnormalities increase with higher cumulative doses of
anthracyclines and longer follow-up (Steinherz, Steinherz and Tan, 1995; Lipshultz,
et al. 1995). In this study, we found a positive relationship between exercise-induced
symptoms and the total cumulative doses of anthracyclines (Spearman correlation
coefficient r = 0.15, p-value = 0.06). Without a complete cardiac evaluation, it is
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impossible to determine if self-reported ‘exercise-induced symptoms’ represent
cardiac abnormalities. Previous studies have found that symptoms of fatigue,
shortness of breath, palpitation and syncope correlate poorly with measures of
ventricular function. Exercise intolerance, although common among survivors of
ALL (43%), did not predict cardiac abnormalities (Lipshultz, et al. 1991). In this
study, self-reported respiratory problems, including asthma, sinus and allergy
problems were unrelated to exercise-induced symptoms (OR = 0.79, 95% Cl = 0.25-
2.55). ,
It appears that obesity and exercise-induced symptoms may be related to
physical activity. In the general population, inactivity is associated with a two-fold
increase in risk of fatigue (Chen, 1986). As with fatigue and depression, the cause
and effect relationship between fatigue and physical inactivity is often unclear. It is
possible, however, that physical inactivity may represent one of the mechanisms
underlying fatigue in this population. Future studies should address physical activity,
its assessment and possible value as an intervention.
Thirty-nine percent (n= 62) of the survivors were classified as being
cognitively impaired. Thirty-four subjects had been diagnosed with a learning
disability, 15 had received special education services and 13 reported significant
memory or concentration problems. Impairments in memory, attention, academic
achievement, intelligence and increased distractibility have been documented in
long-term survivors of ALL, especially among those treated with cranial irradiation
(Copeland, et al. 1985; Mulhem, Wasserman, Fairclough and Ochs, 1988). In this
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study, we also found a higher risk of cognitive impairments among survivors who
had received cranial irradiation (OR= 3.46, 95% Cl = 1.64-7.30).
Directed attention requires mental effort. When the demand for mental effort
exceeds available capacity, individuals tend to experience mental fatigue. Such
cognitive fatigue is different than physical fatigue and further reduces the capacity to
pay attention or concentrate (Kaplan and Kaplan, 1982). In this study, R-PFS fatigue
scores for survivors who were cognitively impaired were highest for the cognitive
fatigue subscale. This finding demonstrates the importance of using a
multidimensional measurement of fatigue in this survivor population that is at risk of
cognitive fatigue secondary to late effects of treatment. While cognitive fatigue has
not been previously measured in this population, neuro-psychologists and school
teachers have described a ‘fatigue effect’ that interferes with school performance
(Brouwers, 1987; Lockwood, Bell and Colegrove, 1999). In these studies,
investigators were unable to determine whether fatigue caused poor performance or
whether cognitive impairments produced fatigue.
Although no demographic and treatment factors were included in the final
< ■
multivariate model of fatigue, several of these factors were directly related to
predictive factors of fatigue. Cranial irradiation increased the survivors’ risk of
obesity and neuro-cognitive impairments. Therapeutic risk groups were positively
related to neuro-cognitive impairments. Current age was positively related to pain
symptoms. Time from the end of therapy, age at diagnosis, relapse and
anthracycline total dose were unrelated to predictive factors. While Langeveld, et al.
/ 108
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(2003) did not report on such indirect relationships, their final multivariate model
indicated that survivors of leukemia and non-Hodgkin’s lymphoma who had
received cranial irradiation had a lower risk of fatigue than survivors who had not
received cranial irradiation. It is important to explore these indirect relationships in
long-term survivors. While specific late effects increase survivors’ risk of fatigue, it
is likely that the risk factors associated with these late effects, such as age, cranial
irradiation and high risk therapy, may be more informative in determining the
underlying mechanisms of off-treatment fatigue.
Fatigue is inversely related to quality of life (Bower, et al. 2000; Sadler, et al.
2002; Hwang, Chang, Cogswell and Kasimis, 2002; Broeckel, Jacobsen, Horton,
Balducci and Lyman, 1998). In this study, that relationship was most apparent when
we stratified survivors by fatigue status. Survivors who reported moderate to severe
levels of fatigue had a poorer quality of life, on all domains, than non-fatigued
survivors and their peers in the general population. The mental and physical SF-36
summary scores for the fatigued survivors fell far below the 25th percentile for the
general population, indicating “substantial functional limitations, severe social and
role disabilities, distress and a very unfavorable evaluation of health status and
outlook” (Ware, Kosinski and Keller, 1994, pg. 7.2) (25th percentile for
males/females, ages 25-34: PCS = 51.2, MCS = 44.3). Quality of life was even
A
poorer for the subset of survivors who were both fatigued and depressed. These
findings suggest that although fatigue and depression are closely related, they are
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different and have a profound negative effect on quality of life when both are
present.
The above findings are in sharp contrast to our findings for the survivors who
were not fatigued. These individuals, who represent 70% of the survivors, reported a
better quality of life than their peers, especially in areas of social functioning, mental
health and limitations due to emotional health. The SF-36 PCS mean score for the
non- fatigued survivors was at the norm level and their MCS mean score was well
above the 50th percentile for the general population. These findings illustrate a
resiliency often documented in childhood cancer survivors (Elkin, Phipps, Mulhem
and Fairclough, 1977; Kupst, et al. 1995; Kazak and Meadows, 1989; Wasserman,
Thompson, Williams and Fairclough, 1987; Gray, et al. 1992).
E. Strengths and limitations
One strength of this study is our measurement of fatigue and depression. We
used several self-report instruments, including Piper’s multidimensional scale, to
measure fatigue, and a standardized questionnaire to assess depressive symptoms.
While normative data were available for comparisons, the absence of a control group
is a limitation of this study. During the pilot phase of this study, we surveyed
survivors who were 18 years and older in the clinic setting and discovered that often
the parent, not the subject, completed the survey. Interviews, the methodology used
in this study, ensured the accurate collection of subjective data in this population.
While our lost to follow-up rate of 33% is consistent with other follow-up studies of
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childhood cancer survivors, maintaining contact with patients after the completion of
therapy continues to be a major challenge for pediatric oncology centers. Relying on
self-report measures to capture late-effects and health history is a potential limitation
of this study. Some survivors may have had difficulty recalling past medical events
because they were treated at a young age. Underreporting of some medical
conditions, such as cardiac problems, may have occurred because recent medical
work-ups had not been completed. Such under-reporting of events would pull risk
estimates toward the null value.
In summary, while the rate of fatigue among survivors of ALL was no greater
than the general population, the survivors who reported moderate to severe level of
fatigue had a high rate of depression and a very poor quality of life. Obesity, neuro-
cognitive impairments, exercise-induced symptoms, pain, sleep problems and having
children, significantly increased survivors’ risk of fatigue. This area of research in
pediatric oncology is in its infancy. Since late effects are disease/treatment specific
and appear to be associated with off-treatment fatigue, studies of fatigue will be most
informative when limited to a single cancer diagnosis. Prospective longitudinal
studies are needed to determine the directionality of interrelated factors, such as
fatigue and depression. Fatigue, a symptom often overlooked by clinicians, appears
to be a powerful predictor of survivors’ overall quality of life.
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Nicholson HS & Byrne J. (1997). Comparison of psychologic outcome in adult
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Appendix A
Follow-Up Study of Adult Survivors of
Childhood Leukemia
Study Questionnaire
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SUBJECT’S WORD FOR ILLNESS
END OF THERAPY DATE
130
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TIME STARTED
INTERVIEWER
Hi (-----)
THIS IS KATHY MEESKE FROM CHILDRENS HOSPITAL.
HOW ARE YOU?
ARE YOU READY TO BEGIN OUR INTERVIEW?
DO YOU HAVE THE ANSWER SCALES I SENT YOU? GREAT
BEFORE WE BEGIN, LET ME GIVE YOU AN IDEA OF HOW W E’LL WORK
THE INTERVIEW.
FIRST, I WILL ASK YOU A QUESTION AND ASK YOU TO ANSWER IT
USING ONE OF THE ANSWER SCALES. THE SCALES ARE NUMBERED TO
HELP US STAY TOGETHER. IF AT ANY TIME, YOU GET CONFUSED OR
HAVE ANY QUESTIONS.. .JUST LET ME KNOW. WE CAN STOP AND GO
OVER THINGS.
I ALSO WANT TO REMIND YOU, THAT YOU DO NOT HAVE TO ANSWER
ANY QUESTIONS YOU DO NOT WANT TO ANSWER. AND AGAIN, ALL
YOUR ANSWERS WILL BE KEPT CONFIDENTIAL.
NOW, DO YOU HAVE ANY QUESTIONS FOR ME BEFORE WE BEGIN?
I’M GOING TO BEGIN BY ASKING YOU SOME GENERAL BACKGROUND
QUESTIONS.
WHEN YOU LOOK AT THE FIRST RESPONSE SCALE, THE ETHNICITY
SCALE, WHICH CATEGORY BEST DESCRIBES YOU?
(ask subject if they would like you to read the list of answers)
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1. When you look at the first scale the ETHNICITY SCALE (Pg 1, #1), which
category best describes you?
1. White
2. Black or African American
3. Hispanic or Latino
4. Asian or Pacific Islander
5. Native American
6. other, please specify______
2. Using the EDUCATION SCALE (Pg 1, #2), what is the highest level of
education you have completed?
1. 8th grade or less
2. some high school
3. high school graduate
4. some college or vocational school
5. college graduate
6. advanced or graduate training
3. With the EM PLOYM ENT SCALE (Pg 1. #3), what is your current work or
school status?
1. Student
2. Work full time
3. Work part-time
4. Not working
5. Retired
What is your occupation?
4. Looking at the INCOM E SCALE (Pg 1. #4), what best describes your personal
income for year 2000 before taxes?
1. Less than 9,999
2. 10,000-19,999
3. 20,000-39,999
4. 40,000-59,999
5. 60,000-89,999
6. over 90,000
132
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5. Which of the possibilities listed on the RELATIONSHIP SCALE (Pg 1. #5)
best describes your current status?
1. Single
2. Married
3. Divorced
4. Widowed
5. Separated
6. Living with partner
7. Other (specify)
6. Do you have children? (NO SCALE FO R THIS QUESTION)
_________ No
_________ Yes (if yes, ask 6a-b)
6a. How many of your children are living with you?
6b. What are the ages of the children that are living with you
(i.e., in your home)?
1s t (vrs)
2n d (yrs)
3r d
(vrs)
4th
(vrs)
5th (vrs)
6th (yrs)
133
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THE NEXT SET OF QUESTIONS ASK ABOUT YOUR QUALITY OF LIFE.
THE FIRST QUESTIONS RELATE TO YOUR HEALTH NOW AND YOUR
CURRENT A C rrv rriE S . USING THE HEALTH SCALE ON PAGE TWO.
WOULD YOU SAY
YOUR HEALTH IS....
Ql. In general would you say your health is (HEALTH SCALE, pg 2, #6)
1. excellent
2. very good
3. good
4. fair
5. poor
Q2. Compared to 1 year ago, how would you rate your health in general
now? Would you say it is (COMPARISON SCALE, pg 2. #7)
1. much better now than one year ago
2. somewhat better now than one year ago
3. about the same as one year ago
4. somewhat worse now than one year ago
5. much worse now than one year ago
Now I’m going to read a list of activities that you might do during a typical day.
As I read each item, please use the LIMITATION SCALE (pg2..#8) to tell me if
your health now limits you a lot, limits you a little, or does not limit you at all in
these activities.
Q3. First, vigorous activities, such as running, lifting heavy objects,
participating in strenuous sports. Does your health now limit you a lot,
limit you a little, or not limit you at all?
1. limited a lot
2. limited a little
3. not limited at all
Q4. ...moderate activities, such as moving a table, pushing a vacuum cleaner,
bowling, or playing golf. Does your health now limit you a lot, limit you a
little, or not limit you at all?
1. limited a lot
2. limited a little
3. not limited at all
134
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Q5. ...lifting or carrying groceries. Does your health now limit you a lot,
limit you a little, or not limit you at all?
1. limited a lot
2. limited a little
3. not limited at all
Q6. ...climbing several flights of stairs. Does your health now limit you a lot,
limit you a little, or not limit you at all? LIMITATION SCALE
(pg2..#8)
1. limited a lot (several defined as 3)
2. limited a little
3. not limited at all
Q7. ...climbing one flight of stairs. Does your health now limit you a lot, limit
you a little, or not limit you at all?
1. limited a lot
2. limited a little
3. not limited at all
Q8. ...bending, kneeling, or stooping. Does your health now limit you a lot,
limit you a little, or not limit you at all?
1. limited a lot
2. limited a little
3. not limited at all
Q9. ...walking more than a mile. Does your health now limit you a lot, limit
you a little, or not limit you at all?
1. limited a lot
2. limited a little
3. not limited at all
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Q10. ... walking several blocks. Does your health now limit you a lot, limit you
a little, or not limit you at all?
1. limited a lot
2. limited a little
3. not limited at all
Qll. ... walking one block. Does your health now limit you a lot, limit you a
little, or not limit you at all?
1. limited a lot
2. limited a little
3. not limited at all
Q12 ... bathing or dressing yourself. Does your health now limit you a lot,
limit you a little, or not limit you at all? LIMITATION SCALE (pg2.
#8]
1. limited a lot
2. limited a little
3. not limited at all
The following four questions ask you about your physical health and your daily
activities. These questions require only a YES/NO RESPONSE.
Q13. During the past 4 weeks, have you had to cut down the amount of time
you spent on work or other regular daily activities as a result of your
physical health?
1. yes
2. no
Q14. During the past 4 weeks, have you accomplished less than you would like
as a result of your physical health?
1. yes
2. no
Q15. During the past 4 weeks, were you limited in the kind of work or other
regular daily activities you do as a result of your physical health?
1. yes
2. no
136
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Q16. During the past 4 weeks, have you had difficulty performing work or
other regular daily activities as a result of your physical health, for
example, it took extra effort?
1. yes
2. no
The following three questions ask about your emotions and your daily activities.
Q17. During the past 4 weeks, have you had to cut down the amount of time
you spent on work or other regular daily activities as a result of any
emotional problems, such as feeling depressed or anxious?
1. yes
2. no
Q18. During the past 4 weeks, have you accomplished less than you would like
as a result of any emotional problems, such as feeling depressed or
anxious?
1. yes
2. no
Q19. During the past 4 weeks, did you not do work or other regular daily
activities as carefully as usual as a result of any emotional problems,
such as feeling depressed or anxious?
1. yes
2. no
With the following questions, we will again use the answer scales. For the first
question, we will use the INTERFERENCE SCALE on pg 2. #9.
Q20. During the past 4 weeks, how much of the time has your physical health
or emotional problems interfered with your social activities like visiting
with friends or relatives?
1. not at all
2. slightly
3. moderately
4. quite a bit
5. or extremely
137
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Q21. Using the PAIN SCALE (pg 2. #10). how much bodily pain have you had
during the past 4 weeks?
1.
2.
3.
4.
5.
6.
none
very mild
mild
moderate
severe
very severe
Q22. Using the INTERFERE SCALE (pg 3. #11). During the past 4 weeks,
how much did pain interfere with your normal work (including both
work outside the home and housework)?
1. Not at all
2. A little bit
3. Moderately
4. Quite a bit
5. Extremely
WITH THE NEXT SET OF QUESTIONS WE WILL USE THE TIME SCALE
(PG 3. #12).
AS I READ EACH STATEMENT, PLEASE GIVE ME THE ONE ANSWER
THAT COMES CLOSEST TO THE WAY YOU HAVE BEEN FEELING IN
THE PAST 4 WEEKS; IS IT ALL OF THE TIME, MOST OF THE TIME, A
GOOD BIT OF THE TIME, SOME OF THE TIME, A LITTLE OF THE
TIME, OR NONE OF THE TIME?
Q23. Did you feel full of pep?
Do you know what the work pep means? If no....
First provide example, such as, “have you ever heard anyone say she/he is a
very peppy person?” ; second, provide definition, “having energy and high
spirits”
1. all of the time
2. most of the time
3. a good bit of the time
4. some of the time
5. a little of the time
6. none of the time
138
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Q24. Have you been a very nervous person?
1. all of the time
2. most of the time
3. a good bit of the time
4. some of the time
5. a little of the time
6. none of the time
Q25. Have you felt so down in the dumps that nothing could cheer you up
TIME SCALE (PG 3. #12)
1. all of the time
2. most of the time
3. a good bit of the time
4. some of the time
5. a little of the time
6. none of the time
Q26. Have you felt calm and peaceful?
1. all of the time
2. most of the time
3. a good bit of the time
4. some of the time
5. a little of the time
6. none of the time
Q27. Did you have a lot of energy?
1. all of the time
2. most of the time
3. a good bit of the time
4. some of the time
5. a little of the time
6. none of the time
Q28. Have you felt downhearted and blue?
1. all of the time
2. most of the time
3. a good bit of the time
4. some of the time
5. a little of the time
6. none of the time
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Q29. Did you feel worn out?
1. all of the time
2. most of the time
3. a good bit of the time
4. some of the time
5. a little of the time
6. none of the time
Q30. Have you been a happy person? TIME SCALE (PG 3. #12)
1. all of the time
2. most of the time
3. a good bit of the time
4. some of the time
5. a little of the time
6. none of the time
Q31. Did you feel tired?
1. all of the time
2. most of the time
3. a good bit of the time
4. some of the time
5. a little of the time
6. none of the time
Q32. Using the SOCIAL SCALE (Pg 3. #13), during the past 4 weeks, how
much of the time has your physical health or emotional problems
interfered with your social activities (like visiting with friends, relatives,
etc.)?
1. All of the time
2. Most of the time
3. Some of the time
4. A little of the time
5. None of the time
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Now I’m going to read a list of statements. Using the TRUE/FALSE SCALE
(pg 3. #14). please tell me if it is definitely true, mostly true, mostly false, or
definitely false. If you don’t know, just tell me.
Q33. I seem to get sick a little easier than other people.
1. definitely true
2. mostly true
3. don’t know
4. mostly false
5. definitely false
Q34. I am as healthy as anybody I know.... TRUE/FALSE SCALE (pg 3.
#1£ ,
1. definitely true
2. mostly true
3. don’t know
4. mostly false
5. definitely false
Q35. I expect my health to get worse.
1. definitely true
2. mostly true
3. don’t know
4. mostly false
5. definitely false
Q36. My health is excellent.
1. definitely true
2. mostly true
3. don’t know
4. mostly false
5. definitely false
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THE NEXT SET OF QUESTIONS ASK ABOUT SYMPTOMS YOU MAY BE
EXPERIENCING. WITH THESE QUESTIONS WE WILL USE TH E
SYM PTOM SCALE ON PAGE 4. THIS SCALE ALLOWS YOU TO RATE
EACH SYMPTOM FROM 1 TO 5, WITH 5 BEING THE MOST AND 1 BEING
THE LEAST.
FOR EXAMPLE, WITH THE FIRST SYMPTOM... NAUSEA. IF YOU WERE
TO ‘FEEL AS SICK AS YOU COULD POSSIBLY BE” YOUR ANSWER
WOULD BE A 5 AND IF YOU “DO NOT FEEL SICK AT ALL” YOUR
ANSWER WOULD BE A 1.. .AND IF YOU HAD SOME NAUSEA YOU
WOULD NEED TO DECIDE IF YOU WOULD RATE IT AS A 2, 3, OR
4.. .WITH THE 2 BEING LESS THAN THE 4. DO YOU UNERSTAND HOW TO
USE THIS SCALE?
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OK, USING THIS RATING SCALE (SYMPTOM SCALE. Pg 4) PLEASE TELL
ME WHAT NUMBER MOST CLOSELY MEASURES HOW YOU ARE
FEELING TODAY
1. Nausea
I feel as sick as I could possibly be 5 4 3 2 1 I do not feel sick
2. Mood
Could not feel more miserable Could not feel happier
3. Appetite
Can’t face food at all 5 4 3 2 1 Normal appetite
4. Insomnia
Couldn’t have been worse 5 4 3 2 1 A perfect night
5. Pain
Worst pain I have ever had 5 4 3 2 1 No pain
6. Mobility
Not able to get around 5 4 3 2 Able to do
everything
7. Fatigue
Could not feel more tired 5 4 3 2 I am not tired at
all
8. Bowel Pattern
The worst I’ve ever had 5 4 3 2 Normal bowel
pattern
9. Concentration
Cannot concentrate at all 5 4 3 2 Normal
concentration
10. Appearance
The worst I’ve ever had 5 4 3 2 1 Appearance has
not changed
143
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POMS
With these next questions we will use the FEELING SCALE (Pg 5. #16). Can you
tell me how you have been feeling during the past week?
Not at all A little Moderately Quite a bit Extremely
Worn out 0 1 2 3 4
Listless* 0 1 2 3 4
Fatigued 0 1 2 3 4
Exhausted 0 1 2 3 4
Sluggish* 0 1 2 3 4
Weary* 0 1 2 3 4
Bushed* 0 1 2 3 4
• DEFINITIONS:
listless = opposite of lively, hard to get going
Sluggish = slow, inactive; lacking energy
Weary = to be physically and mentally tired or fatigued
Bushed = extremely tired, “so beat you could flop down and go to sleep
anywhere”:
144
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NOW, I” M GOING TO ASK YOU SOME QUESTIONS CONCERNING
FATIGUE.
DO YOU KNOW WHAT THIS WORD FATIGUE MEANS?
(IF NEEDED DEFINITION OF FATIGUE: AN UNUSUAL SENSE OF
TIREDNESS THAT IS NOT USUALLY RELIEVED BY EITHER A GOOD
NIGHT’S SLEEP OR REST.)
FOR THESE QUESTIONS WE WILL USE THE FATIGUE SCALE
BEGINNING ON PAGE 6.
WITH THE FOLLOWING QUESTIONS IF YOU COULD TELL ME THE
NUMBER THAT BEST DESCRIBES YOUR FATIGUE OVER THIS PAST
MONTH. THIS FATIGUE SCALE IS SIMILAR TO THE RATING SCALE WE
USED BEFORE. DO YOU HAVE ANY QUESTONS ON HOW TO USE THIS
SCALE?
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Using the FATIGUE SCALE, can you tell me the number that best describes the
fatigue you have been experiencing these past 4 weeks.
Question 2. To what degree is the fatigue you are feeling now causing you distress?
No distress
A great deal
01234567 89 10
Question 3. To what degree is the fatigue you are feeling now interfering with your
ability to complete your work or school activities?
None A great deal
0123456789 10
Question 4. To what degree is the fatigue you are feeling now interfering with your
ability to visit or socialize with your friends?
None A great deal
0 1 2 3 4 5 6 7 8 9 10
Question 5. To what degree is the fatigue you are feeling now interfering with your
ability to engage in sexual activity?
None A great deal
0 I 2 3 4 5 6 7 8 9 10
Question 6. Overall how much is the fatigue which you are experiencing now,
interfering with your ability to engage in the kind of activities you enjoy
doing?
None A great deal
0 1 2 3 4 5 6 7 8 9 10
146
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Question 7. How would you describe the degree of intensity or severity of the
fatigue which you are experiencing now?
Mild Severe
0123456 78 9 10
Question 8. How would you describe the degree of intensity or severity of the
fatigue which you are experiencing now?
Pleasant Unpleasant
0 1 2 3 4 5 6 7 8 9 10
**********pLEASE TURN TO NEW PAGE...Pg 7********
Question 9. How would you describe the degree of intensity or severity of the
fatigue which you are experiencing now?
Agreeable Disagreeable
0123456789 10
Question 10. How would you describe the degree of intensity or severity of the
fatigue which you are experiencing now?
Protective Destructive
01234567 89 10
Question 11. How would you describe the degree of intensity or severity of the
fatigue which you are experiencing now?
Positive Negative
0 1 2 3 456789 10
Question 12. How would you describe the degree of intensity or severity of the
fatigue which you are experiencing now?
Normal Abnormal
0 1 2 3 4 5 6 7 8 9 10
147
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Question 13. To what degree are you now feeling:
Strong
0 1 2 3 4 5
Weak
9 10
Question 14. To what degree are you now feeling:
Awake
0 1 2 3 4 5
Sleepy
9 10
Question 15. To what degree are you now feeling:
Lively
0 1 2 3 4 5
Listless
9 10
**********PLEAs E TURN TO NEW PA G E...Pg 8********
Question 16. To what degree are you now feeling:
Refreshed
0 1 2 3 4 5 6 7
Tired
9 10
Question 17. To what degree are you now feeling:
Energetic
0 1 2 3 4 5 6 7
Unenergetic
8 9 10
Question 18. To what degree are you now feeling:
Patient Impatient
0123456789 10
Question 19. To what degree are you now feeling:
Relaxed
0 1 ^ 3 4
v . 1 ~ •
Tense
9 10
148
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Question 20. To what degree are you now feeling:
Exhilarated Depressed
0 1 2 3 4 5 6 7 8 9 10
Question 21. To what degree are you now feeling:
Able to concentrate Unable to concentrate
0 1 2 3 4 5 6 7 8 9 10
Question 22. To what degree are you now feeling:
Able to remember Unable to remember
0 1 2 3 4 5 6 7 8 9 10
Question 23. To what degree are you now feeling:
Able to think clearly Unable to think clearly
0 1 2 3 4 5 6 7 8 9 10
NO SCALE FOR NEXT TWO QUESTIONS
24. Overall, what do you believe is most directly contributing to or causing your
fatigue?
1.______________________________________________________________________________________________
2.______________________________________________________________________________________________
25. Overall, the best thing you have found to relieve your fatigue is?
1.____________________________________________________________
2.___________________________________________________________________________
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In this next section I will read some statements about ways you might have felt
during the past week. Using the FREQUENCY SCALE (pg 9. #18) please let me
how often you have felt this way during the past seven days.
Frequency Scale
0 = Rarely or none of the time (less than 1 day)
1 = Some or a little of the time (1-2 days)
2 = Occasionally or a moderate amount of time (3-4 days)
3 = Most or all of the time (5-7 days)
_______ 1. You were bothered by things that usually don’t bother you.
2. You did not feel like eating: your appetite was poor.
3. You felt that you could not shake off the blues.
4. You felt that you were just as good as other people.
5. You had trouble keeping your mind on what you were
doing.
6. You felt depressed.
7. You felt that everything you did was an effort.
8. You felt hopeful about the future.
9. You thought your life had been a failure.
_______ 10. You felt fearful.
_______ 11. Your sleep was restless.
_______ 12. You were happy.
_______ 13. You talked less than usual.
_______ 14. You felt lonely.
_______ 15. People were unfriendly.
_______ 16. You enjoyed life.
_______ 17. You had crying spells.
_______ 18. You felt sad.
_______ 19. You felt people disliked you.
_______ 20. You could not get “going”.
150
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THE NEXT QUESTIONS RELATE TO YOUR USUAL SLEEP HABITS DURING
THE PAST MONTH.
1. During the past month, when have you usually gone to bed at night?
USUAL BED TIME________
2. During the past month, how long (in minutes) has it usually taken you to fall
asleep each night?
NUMBER OF MINUTES________
3. During the past month, when have you usually gotten up in the morning?
USUAL GETTING UP TIME________
4. During the past month, how many hours of actual sleep did you get at night?
(This may be different than the number of hours you spend in bed).
HOURS OF SLEEP PER NIGHT________
With the next group of questions, please answer using the SLEEP PROBLEM
SCALE (Pe 9. #191.
5. During the past month, HOW OFTEN HAVE YOU HAD TROUBLE
SLEEPING BECAUSE Y O U ...
a. Cannot get to sleep within 30 minutes
1. Not during the past month
2. Less than once a week
3. Once or twice a week
4. Three or more times a week
b. Wake up in the middle of the night or early morning
1. Not during the past month
2. Less than once a week
3. Once or twice a week
4. Three or more times a week
151
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During the past month, HOW OFTEN HAVE YOU HAD TROUBLE
SLEEPING BECAUSE Y O U ...
SLEEP PROBLEM SCALE (Pg 9. #19).
c. Have to get up to use the bathroom
1. Not during the past month
2. Less than once a week
3. Once or twice a week
4. Three or more times a week
d. Cannot breathe comfortably
1. Not during the past month
2. Less than once a week
3. Once or twice a week
4. Three or more times a week
e. Cough or snore loudly
1. Not during the past month
2. Less than once a week
3. Once or twice a week
4. Three or more times a week
f. Feel too cold
1. Not during the past month
2. Less than once a week
3. Once or twice a week
4. Three or more times a week
g. Feel too hot
1. Not during the past month
2. Less than once a week
3. Once or twice a week
4. Three or more times a week
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h. Had bad dreams
1. Not during the past month
2. Less than once a week
3. Once or twice a week
4. Three or more times a week
During the past month, HOW OFTEN HAVE YOU HAD TROUBLE
SLEEPING BECAUSE Y O U ...
SLEEP PROBLEM SCALE (Pg 9. #19).
i. Have pain
1. Not during the past month
2. Less than once a week
3. Once or twice a week
4. Three or more times a week
j. Other reasons(s), please describe__________________________
1. Not during the past month
2. Less than once a week
3. Once or twice a week
4. Three or more times a week
6. Using the SLEEP QUALITY SCALE (Pg 9. #20). During the past month,
how would you rate your sleep quality?
1. Very good
2. Fairly good
3. Fairly bad
4. Very bad
7. Using the SLEEP FREQUENCY SCALE (Pg. 9. #21) During the past
month, how often have you taken medicine (prescribed or “over the counter”)
to help you sleep?
1. Not during the past month
2. Less than once a week
3. Once or twice a week
4. Three or more times a week
153
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8. During the past month, how often have you had trouble staying awake while
driving, eating meals, or engaging in social activity?
1. Not during the past month
2. Less than once a week
3. Once or twice a week
4. Three or more times a week
9. During the past month, how much of a problem has it been for you to keep up
enough enthusiasm to get things done? Please use the PROBLEM SCALE
(Pg 9. #22) to answer.
1. No problem at all
2. Only a very slight problem
3. Somewhat of a problem
4. A very big problem
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NOW, I HAVE JUST A FEW QUESTIONS CONCERNING MEMORY AND
CONCENTRATION.
USING THE DIFFICULTY SCALE ON PAGE 10. #23,
1. How much difficulty do you have remembering things at school/college or
work?
(0) (1) (2) (3) (4)
None a little some quite a lot a whole lot
2. How much difficulty do you have with reading and writing?
(0) (1) (2) (3) (4)
None a little some quite a lot a whole lot
3. How much difficulty do you have with math and calculations?
(0) (1) (2) (3) (4)
None a little some quite a lot a whole lot
FOR THE NEXT QUESTIONS WE WILL USE THE CONCENTRATION
SCALE. (Pg 10. #24)
4. Do you have difficulty concentrating at school/college or work?
(0) (1) (2) (3) (4)
Never Occasionally Sometimes Most of the time All the time
5. Do you have difficulty concentrating at other times, (e.g., playing cards,
computer games or reading)?
(0) (1) (2) (3) (4)
Never Occasionally Sometimes Most of the time All the time
6. In elementary, junior, or high school were you ever in a learning disabled or
special education or resource program?
(0) (1) (99)
NO YES DK
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NOW, W E’RE GOING TO CHANGE GEARS HERE A BIT. THE NEXT FIVE
QUESTIONS HAVE TO DO WITH YOUR GROWTH.
1. What is your current height without shoes?
_________Feet Inches
2._____What is your current weight without shoes?
______________________ Pounds
3. FEM ALES ONLY:
What is your dress size?
3. Males ONLY:
What is your waist size?
6. Using the (HEIGHT SCALE, PglO. #25), how would you describe your
height?
I am the tallest in my family
I am taller than most of my family.
I am about the same height as most of my family.
I am shorter than most of my family.
I am the shortest in my family.
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THE NEXT SET OF QUESTIONS RELATE TO YOUR PHSYCIAL HEALTH.
Have you ever been told by a doctor or other health care professional
that you have or have had
1. HEARING LOSS
No _____ (0) DK ______(99)
Yes _____ (1)
IF YES,
How old were you when this first occurred___________YRS
^Limitation Scale) Are any of your current activities limited by your hearing
loss?
( Pg 10. #26) No, not limited at all _____(0)
Yes, limited a little _______(1)
Yes, limited a lot ________(2)
DK ______(99)
2. VISION PROBLEMS. SUCH AS ANY TROUBLE SEEING WITH ONE OR
BOTH EYES EVEN WHEN WEARING GLASSES
No _____ (0) DK ______(99)
Yes _____ (1)
IF YES,
How old were you when this first occurred__________ YRS
(Limitation Scale) Are any of your current activities limited by your vision
problems?
No, not limited at all _____(0)
Yes, limited a little _______(1)
Yes, limited a lot ________(2)
DK _____(99)
157
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Have you ever been told by a doctor or other health care professional
that you have or have had (Limitation Scale. Pg 10, #26)
3. LEARNING DISABILITY
No (0) DK ______ (99)
Yes _____ (1)
IF YES,
How old were you when this first occurred___________ YRS
Do you currently take prescription medicine for your
learning disability?
No _____(0)
Yes _____(1)
DK _____(99)
(Limitation Scale) Are any of your current activities limited by your learning
disability?
No, not limited at all _____(0)
Yes, limited a little _______(1)
Yes, limited a lot ________(2)
D K _____(99)
4. FREQUENT HEADACHES OR MIGRAINES
No (0) DK _____ (99)
Yes _____ (1)
IF YES,
How old were you when this first occurred___________ YRS
Do you currently take prescription medicine for your headaches?
No _____(0)
Yes _____(1)
DK _____(99)
(Limitation Scale) Are any of your current activities limited by your
headaches?
No, not limited at all _____(0)
Yes, limited a little _______(1)
Yes, limited a lot ________(2)
DK _____(99)
158
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Have you ever been told by a doctor or other health care professional
that you have or have had (Limitation Scale. Pg 10, #26)
5. EPILEPSY, SEIZURES. CONVULSIONS OR FREQUENT BLACKOUTS
No
Yes
_(0)
. ( 1 )
DK .(99)
IF YES,
How old were you when this first occurred________
(Limitation Scale) Are any of your current activities limited by
9
YRS
No, not limited at all
Yes, limited a little
Yes, limited a lot _
DK _____(99)
.(0)
_(1)
_(2)
6. HEPATITIS
No
Yes
.(0)
- 0 )
DK .(99)
IF YES,
How old were you when this first occurred.
Type of hepatitis______________
YRS
Do you currently take prescription medicine for your
hepatitis?
No _____(0)
Yes _____(1)
DK _____(99)
(Limitation Scale) Are any of your current activities limited by your hepatitis?
No, not limited at all _____(0)
Yes, limited a little _______(1)
Yes, limited a lot ________(2)
DK _____(99)
7. DIABETES
No
Yes
.(0)
_(D
DK .(99)
159
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IF YES,
How old were you when this first occurred___________YRS
(Limitation Scale) Are any of your current activities limited by your diabetes?
No, not limited at all _____(0)
Yes, limited a little _______(1)
Yes, limited a lot ________(2)
D K (99)
Have you ever been told by a doctor or other health care professional
that you have or have had (Limitation Scale, Pg 10, #26)
8. CLINICAL DEPRESSION OR ANXIETY
No (0) DK ______(99)
Yes _____ (1)
IF YES,
How old were you when this first occurred___________ YRS
(Limitation Scale) Are any of your current activities limited by your
?
No, not limited at all _____(0)
Yes, limited a little _______(1)
Yes, limited a lot ________(2)
DK _____(99)
9. CHRONIC FATIGUE SYNDROME
No (0) DK ______(99)
Yes _____ (1)
IF YES,
How old were you when this first occurred___________ YRS
Do you currently take prescription medicine for your CFS?
No _____(0)
Yes _____(1)
DK _____(99)
(Limitation Scale) Are any of your current activities limited by your CFS?
No, not limited at all _____(0)
Y e s , lim ite d a little _________ (1 )
Yes, limited a lot ________(2)
DK _____(99)
160
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The next several questions ask about your thyroid. Have you ever been
told by a doctor or other health care professional that you have or have
had
10. A N OVERACTIVE THYROID GLAND (HYPERTHYROID)
No (0) DK (99)
Yes _____ (1)
IF YES,
How old were you when this first occurred___________YRS
(Limitation Scale. Pg 10. #26)
11. AN UNDERACTIVE THYROID GLAND (HYPOTHYROID)
No (0) DK (99)
Yes _____ (1)
IF YES,
How old were you when this first occurred___________YRS
12. THYROID NODULES
No (0) DK (99)
Yes _____ (1)
IF YES,
How old were you when this first occurred___________YRS
13. OTHER THYROID ENLARGEMENTS
No (0) DK (99)
Yes _____ (1)
IF YES,
How old were you when this first occurred___________YRS
14. I f YES to any thyroid question (questions 9-13 )
(Limitation Scale) Are any of your current activities limited by your thyroid
condition(s)?
No, not limited at all _____(0)
Yes, limited a little _______(1)
Yes, limited a lot ________(2)
DK _____ (99)
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The next set of questions ask about your heart. Have you ever been told
by a doctor or other health care professional that you have or have had
(Limitation Scale. Pg 10. #26)
15. IRREGULAR HEARTBEAT OR PALPITATIONS (ARRHYTHMIA)
REQUIRING MEDICATION OR FOLLOW-UP BY A DOCTOR
No (0) DK _(99)
Yes _____ (1)
IF YES,
How old were you when this first occurred___________ YRS
16. CONGESTIVE HEART FAILURE OR CARDIOMYOPATHY (WEAK
HEART MUSCLE)
No _____ (0) DK (99)
Yes _____ (1)
IF YES,
How old were you when this first occurred____________ YRS
17. A STROKE OR A CEREBROVASCULAR ACCIDENT
No _____ (0) DK (99)
Yes _____ (1)
IF YES,
How old were you when this first occurred___________ YRS
18. A MYOCARDIAL INFARCTION (HEART ATTACK)
No _____ (0) DK (99)
Yes _____ (1)
IF YES,
H o w o ld w ere y o u w h en th is first occu rred ________________Y R S
162
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(Limitation Scale. Pg 10. #26)
19. ANGINA (SEVERE CHEST PAIN)
No ______(0) DK _(99)
Yes _____ (1)
IF YES,
How old were you when this first occurred___________ YRS
19. CORONARY ARTERY DISEASE OR HEART DISEASE
N o ______(0) DK (99)
Y es ______(1)
IF YES,
How old were you when this first occurred____________ YRS
20. DOES EXERCISE CAUSE SEVERE CHEST PAIN SHORTNESS OF
BREATH, OR IRREGULAR HEARTBEAT
N o _____ (0) DK (99)
Yes ______(1)
IF YES,
How old were you when this first occurred____________ YRS
***FEMALE.. ASK QUESTION 22
***MALE..GO TO QUESTION23
22. HAVE YOU EVER BEEN PREGNANT? IF YES, DID YOU EXPERIENCE
HEART FAILURE DURING PREGNANCY OR AFTER DELIVERY
N o ______(0) DK (99)
Y es ______(1)
IF YES,
How old were you when this first occurred____________ YRS
23. I f YES to any question on cardiac disease (questions 13-20)
Do you currently take prescription medicine for your heart condition?
No _____ (0)
Yes _____ (1)
DK _____(99)
163
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(Limitation Scale) Are any of your current activities limited by your heart
condition (s)?
No, not limited at all _____(0)
Yes, limited a little _______ (1)
Yes, limited a lot _______ (2)
DK _____(99)
In this section I will be asking you some questions concerning your growth, puberty,
and fertility.
Have you ever been told by a doctor or other health care professional that you
have or have had
24. Deficiency o f growth hormone
No _____ (0) Yes (1) DK (99)
IF YES,
How old were you when this first occurred____________ YRS
25. Have you ever received injections o f growth hormone (Protropin or
Humatrope)
No _____ (0) Yes (1) DK (99)
IF YES,
How old were you when this first occurred____________ YRS
26. A t what age did you enter puberty ____________________ YRS
Definition o f puberty:
MALES: voice change, growth of facial and body hair
FEMALES: breast development, growth of body hair and
menses
Probe fo r precocious purberty:
boys, physical changes before age 9
girls, physical changes before age 8
27. Did you need medication to so into puberty
No _____ (0) Yes (1) DK (99)
IF YES,
How old were you when this first occurred____________ YRS
28. Has a doctor ever told you that you might have trouble having children
No _____ (0) Yes (1) DK (99)
164
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IF YES,
How old were you when this first occurred YRS
29. Have you ever had medical test (such as a blood test, an ultrasound or sperm
count) to see whether or not you mieht have trouble havins children
No _____ (0) Yes (1) DK (99)
IF YES,
How old were you when this first occurred____________ YRS
What were the results of this test____________________
!!MALES only:
30. Have you ever been told you had a low sperm count
No _____ (0) Yes______ (1) DK (99)
IF YES,
How old were you when this first occurred____________ YRS
*******IF M ALE. SK IP TO QUESTION NUMBER 34
HFEMALES only:
31. Have you ever had a menstrual period
No _____ (0) Yes______ (1) DK (99)
IF YES,
How old were you when this first occurred____________ YRS
32. Have you ever taken female hormones, including birth control pills (oral
contraceptives) to have your period
No _____ (0) Yes______ (1) DK (99)
IF YES,
How old were you when this first occurred____________ YRS
Do you currently take prescription medicine fo r_____________ ?
No _____ (0)
Yes _____ (1)
DK _____ (99)
33. Are you currently havins menstrual periods
No _____ (0) Yes _____ (1) DK (99)
165
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IF NO,
1. at what age was your last menstrual period?_________ YRS
IF YES,
1. would you describe your periods as
reg u lar_________ (1)
irregular_________ (2)
Definition o f regular periods: monthly periods lasting 3-5 days
2. Have you experienced menopausal symptoms?
Definition o f menopausal symptoms: hot flashes, insomnia, change
in or cessation of periods, mood swings
No _____ (0) DK (99)
Yes _____ (1)
IF YES,
How old were you when this first occurred___________YRS
(Limitation Scale) Are any of your current activities limited by your
menopausal symptoms?
No, not limited at all _____(0)
Yes, limited a little _______(1)
Yes, limited a lot ________(2)
D K (99)
34. You finished your (leukemia) therapy o n ______(end o f therapy date). Have
you had any operations or sureeries since__________ (end o f therapy date)
No _____ (0) DK (99)
Yes _____ (1)
IF YES,
Type of surgery______________________ date_______________
Type of surgery______________________ date_______________
Type of surgery______________________ date_______________
Type of surgery______________________ date_______________
Type of surgery______________________ date_______________
(Limitation Scale) Are any of your current activities limited by
your_______________________?
N o, not lim ited at all ______(0)
Yes, limited a little _______(1)
Yes, limited a lot ________(2)
DK _____ (9)
166
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35. A t any time following your original diagnosis (of leukemia), were you
diaenosed with another tumor, cancer or similar illness
No ____ (0)
Yes ____ (1)
if yes,
a. what was vour diagnosis (name):___________________
b. where was this diagnosed:
Hospital Name
Address:
City, State
Doctor’s Name
c. date of diagnosis
Month
Year
Have you had any further tumor, cancer, or similar illnesses after this second
episode?
No ____ (0)
Yes ____ (1)
if yes,
a. what was vour diagnosis (name):________________
b. where was this diagnosed:
Hospital Name____
Address: _____
City, State ___________
Doctor’s Name___________
c. date of diagnosis
Month___________ Year
167
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IN THE PAST 12 MONTHS, HAVE YOU BEEN TOLD BY A DOCTOR OR
OTHER HEALTH CARE PROFESSIONAL THAT YOU HAVE
(Limitation Scale, Pg 10, #26)
36. ANEMIA
No _____ (0) DK ______(99)
Yes _____ (1)
IF YES,
How old were you when this first occurred___________YRS
Do you currently take prescription medicine for____________?
No _____(0)
Yes _____(1)
D K (99)
(Limitation Scale) Are any of your current activities limited by your anemia?
No, not limited at all _____(0)
Yes, limited a little _______(1)
Yes, limited a lot ________(2)
DK _____(99)
168
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37. DO YOU HAVE ANY OTHER MEDICAL CONDITIONS OR PROBLEMS
THAT LIMIT YOUR CURRENT DAILY ACTIVITIES
(Limitation Scale. Pg 10, #26)
No (0) DK ______(99)
Yes _____ (1)
IF YES,
Please describe_________________________ __
How old were you when this first occurred___________ YRS
Do you currently take prescription medicine for____________?
No _____(0)
Yes _____(1)
DK _____(99)
(Limitation Scale) Are any of your current activities limited by your
?
No, not limited at all _____(0)
Yes, limited a little _______(1)
Yes, limited a lot ________(2)
D K (99)
IF YES,
Please describe_______________________________
How old were you when this first occurred___________ YRS
Do you currently take prescription medicine for___________
No _____(0)
Yes _____(1)
DK _____(99)
(Limitation Scale) Are any of your current activities limited by your
?
No, not limited at all _____(0)
Yes, limited a little _______(1)
Yes, limited a lot ________(2)
DK _____(99)
169
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THIS IS THE LAST SECTION. THE QUESTIONS IN THIS SECTION
CONCERN MEDICATIONS YOU MAY HAVE TAKEN.
PLEASE TURN TO THE M EDICATION SCALE ON PAGE 11.
WITH EACH GROUP OF MEDICATIONS, YOU WILL FIND THAT I HAVE
LISTED THE MORE COMMON PRODUCTS.
USING THE MEDICATION SCALE, CAN YOU PLEASE TELL ME IF YOU
HAVE EVER USED ANY OF THE FOLLOWING MEDICATIONS FO R AT
LEAST 2 M ONTHS?
LET’S BEGIN WITH THYROID MEDICATIONS. HAVE YOU EVER USED
ANY THYROID MEDICATIONS FOR AT LEAST 2 MONTHS?
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MEDICATION HISTORY
(Medication Scale..pg 11)
The next set of questions concern medications you may have taken.
Have you ever used any o f the following medications for at least 2 months?
1. Thyroid medication such as L-thyroxin, Levothyroid, levothyroxin, Synthroid
others?
No (1) Yes (2) DK (99)
If yes,
specify the name of the dmg(s)_____________________________
have you used this medication for at least 2 months during the past 6 months?
No (1) Yes (2) DK (99)
2. Drugs for high blood pressure or for vour heart, such as Atenolot (Tenoretic),
Captopril, Digoxin (Lanoxin), Lasix (Furosemide), Inderal, (Aldomet) Methyl
Dopa, Dyazide (Triamterene), Procardia, Vasotec, Clonidine or others?
No (1) Yes (2) DK (99)
If yes,
specify the name of the dmg(s)_____________________________
have you used this medication for at least 2 months during the past 6 months?
No (1) Yes (2) DK (99)
3. Medication for diabetes such as Insulin, Diabinase, Glucotrol, Micronase,
Orinase, Tolinase or others?
No (1) Yes (2) DK (99)
If yes,
specify the name of the dmg(s)_____________________________
have you used this medication for at least 2 months during the past 6 months?
No (1) Yes (2) DK (99)
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4. Anti-epileptic (anti-seizure) drugs such as dilantin, Phenobarbital, Depakane,
Tegretol (Carbamazepine), Klonipen, Primidone (Mysoline), Zarontin or others?
No (1) Yes (2) DK (99)
If yes,
specify the name of the drag(s)_____________________________
have you used this medication for at least 2 months during the past 6 months?
No (1) Yes (2) DK (99)
5. Testosterones (male hormones) such as Delatesteral, Testosterone cypionate,
enanthate or others?
No (1) Yes (2) DK (99)
If yes,
specify the name of the drug(s)_____________________________
have you used this medication for at least 2 months during the past 6 months?
No (1) Yes (2) DK (99)
(NEW PA G E »»»>M edication Scale..pg 12)
6. Estrogens or progesterones (Female hormones) such as Estrace, Estraderm patch,
Premarin, Prempro, Provera, Medroxyprogesterone or others?
No (1) Yes (2) DK (99)
If yes,
specify the name of the drag(s)_____________________________
have you used this medication for at least 2 months during the past 6 months?
No (1) Yes (2) DK (99)
7. Other medicines to replace body hormones such as prednisone, DDAVP
(Desmopressin), hydrocortisone, growth hormones or others?
No (1) Yes (2) DK (99)
If yes,
specify the name of the dmg(s)_____________________________
have you used this medication for at least 2 months during the past 6 months?
No (1) Yes (2) DK (99)
172
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8. Antidepressants or other prescribed drugs for depression or other mood disorders
such as Elavil, Prozac, Serzone, Paxil, Zoloft, Navane, Ritalin, Clonidine? or
others?
No (1) Yes (2) DK (99)
If yes,
specify the name of the drag(s)_____________________________
have you used this medication for at least 2 months during the past 6 months?
No (1) Yes (2) DK (99)
9. Allergy medications such as Benadryl, Dimatapp, Sudafed, Claritin, Allegra or
others?
No (1) Yes (2) DK (99)
If yes,
specify the name of the drug(s)_____________________________
have you used this medication for at least 2 months during the past 6 months?
No (1) Yes (2) DK (99)
10. Muscle Relaxants such as Baclofen, Flexeril, Valium, Chlorzoxazone (Paraflex)
or others ?
No (1) Yes (2) DK (99)
If yes,
specify the name of the drug(s)_____________________________
have you used this medication for at least 2 months during the past 6 months?
No (1) Yes (2) DK (99)
11. Are you currently taking any other prescribed drugs?
No (1) Yes (2) DK (99)
If yes,
specify the name of the drug(s)_____________________________
have you used this medication for at least 2 months during the past 6 months?
No (1) Yes (2) DK (99)
173
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If yes,
specify the name of the drug(s)_____________________________
have you used this medication for at least 2 months during the past 6 months?
No (1) Yes (2) DK (99)
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1. When you think about your energy level, do you think you have the same amount
of energy as others your age? Or do you think you have less or more energy than
others your age?
(3) SAME
If LESS is it If MORE...... is it
(2) A little less? (4) A little more?
(1) A lot less? (5) A lot more?
2. Your need for sleep and rest, do you think you need the same amount of sleep
and rest as others your age? Or do you think you need more or less sleep and rest
than others your age?
(3) SAME
If LESS is it If MORE....is it
(2) A little less? (4) A little more?
(1) A lot less? (5) A lot more?
3. When you think about being tired, do you think you are just as tired as others
your age? Or do you think you are more or less tired than others your age?
(3) SAME
If LESS is it If MORE....is it
(2) A little less? (4) A little more?
(1) A lot less? (5) A lot more?
175
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THE END OF THE INTERVIEW !!!!!!
I’d like to thank you again for taking he time to talk with me.
Do you have any questions concerning the issues we discussed today?
Would you like to talk with someone concerning these issues?
Yes No
Action taken:
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A p p en d ix B
Medical Chart Abstraction Form
ID #_____________ Abstraction Date.
MEDREC_________________ Interview Date _
CA-REG_____________________ Last CHLA Visit/Date__________
Attending M D _________________ Cognitive functioning Yes No
Medical Chart Abstraction Form
ATTENDING M D _________________________
DOB __________________ Gender m ale_______ (1)
female_________ (2)
Diagnosis______________________ CCG ALL Risk Group
low(l) average (2) high risk (3)
Date of Diagnosis.
unknown (4)
CNS disease at diagnosis y es (1) no__________ (0)
CNS disease ever yes__________ (1) no___________ (0)
End of Therapy (date)__________________________
Relapse yes___________ (1)_ _ no_____________ (0)
If yes, date of relapse_______________
site of relapse _______________
BMT y es___________ (1) n o _____________ (0)
Radiation Therapy
Cranial: yes no
Date of first treatment
Total dose
Dose/fraction
Number of treatments
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Spinal: yes no
Date of first treatment
Total dose
Radiation Therapy
Testes yes no
Date of first treatment
Total dose
IV Methotrexate (total dose) _ _
IT Chemotherapy (total dose)
Methotrexate_____________ ______________________ (total dose)
Other (name/total dose)
Other _________________ _______________ (name/total dose)
Anthracycline (total dose) ______________________ mg/m2
NOTES:
178
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

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Asset Metadata

Creator Meeske, Kathleen Ann (author)

Core Title Assessment of fatigue as a late effect of therapy among survivors of childhood leukemia

School Graduate School

Degree Doctor of Philosophy

Degree Program Epidemiology

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag health sciences, oncology,OAI-PMH Harvest

Language English

Contributor Digitized by ProQuest (provenance)

Advisor Bernstein, Leslie (committee chair), [illegible] (committee member), Mack, Wendy (committee member)

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c16-645536

Unique identifier UC11340137

Identifier 3116754.pdf (filename),usctheses-c16-645536 (legacy record id)

Legacy Identifier 3116754.pdf

Dmrecord 645536

Document Type Dissertation

Rights Meeske, Kathleen Ann

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the au...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus, Los Angeles, California 90089, USA