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Obesity paradox in acute heart failure decompensation
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Obesity paradox in acute heart failure decompensation
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Content
OBESITY PARADOX IN ACCUTE HEART FAILURE DECOMPENSATION
by
Mahdi Khoshchehreh
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTERS OF SCIENCE
(APPLIED BIOSTATISTICS AND EPIDEMIOLOGY)
DECEMBER 2014
Copyright: 2014 Mahdi Khoshchehreh
2
ACKNOWLEDGEMENTS
To my mentor
To my committee
To my professors and advisors
To my family and friends
3
Table of Contents
ACKNOWLEDGEMENTS .................................................................................................................... 2
LIST OF TABLES ................................................................................................................................. 4
ABBREVIATIONS ............................................................................................................................... 5
1 INTRODUCTION ......................................................................................................................... 6
1.1 Acute decompensated heart failure ............................................................................. 6
1.2 Epidemiology of heart failure ....................................................................................... 6
Risk factors of heart failure ......................................................................................... 6
Obesity as a risk factor in heart failure ....................................................................... 6
1.3 Hypotheses and specific aims ....................................................................................... 7
2 METHODS .................................................................................................................................. 8
2.1Study designs and study populations ............................................................................ 8
National Inpatient Sample ........................................................................................... 8
2.2 Definition of Heart Failure Population and Outcome Ascertainment Period ............... 8
Elixhauser risk-adjustment scheme ............................................................................. 8
Definition of Morbid Obesity....................................................................................... 9
2.3 Statistical Analysis ......................................................................................................... 9
3 RESULTS ................................................................................................................................... 10
3.1 Baseline Characteristics .............................................................................................. 10
3.2 In-hospital Mortality ................................................................................................... 10
Figure1 - Kaplan-Meier curves of the estimated probability of overall survival ....... 15
4 DISCUSSION ............................................................................................................................. 16
REFERENCES ................................................................................................................................... 18
Appendix ....................................................................................................................................... 21
4
LIST OF TABLES
Table 1: Baseline characteristics of acute heart failure decompensation patients
by morbid obesity .......................................................................................................... 12
Table 2: Comorbidity of acute heart failure decompensation patients by morbid obesity............ 13
Table 3: Multivariate analysis of predictors of in-hospital mortality among
patients with acute decompensated heart failure ............................................................. 14
5
ABBREVIATIONS
ADHF = Acute decompensated heart failure
BMI = Body mass index
CI = Confidence interval
HF = Heart failure
HR = Hazard ratio
ICD-9 = International classification of diseases, Ninth revision
ICU = Intensive care unit
NIS = National inpatient sampling
OR = Odds ratio
SD = Standard deviation
6
Chapter 1: Introduction
1.1 Acute decompensated heart failure
Acute decompensated heart failure (ADHF) is a potentially fatal cause of acute respiratory distress. The
acute decompensation can be new or an exacerbation of chronic heart failure. The clinical syndrome is
described as development of acute dyspnea with rapid accumulation of fluid within the pulmonary
interstitium and alveolar spaces. ADHF may also manifest as increased left-sided filling pressures, low
cardiac output state or dyspnea without pulmonary edema (Zile, et al. 2008).
1.2 Epidemiology of Heart failure
Over the last 2 decades, heart failure has remained a common reason for hospital admission. Aging of
the population combined with improved life expectancy of heart failure patients have increased the
prevalence of heart failure (PA, et al. 2013). The American Heart Association estimated that 5.1 million
patients were living with heart failure in the United States in 2013 (M. D. Go AS 2013 ). Heart failure
was the primary discharge diagnosis of over one million hospitalizations, and accounted for at least 20
percent of admissions in patients older than 65 in the United States in 2010 (M. D. Go AS 2014).
-Risk factors of heart failure
In the Framingham Study cohort, hypertension and coronary artery disease were the main contributors
to heart failure (Ho KK 1993). However, due to diagnostic and therapeutic enhancements, the
contribution of hypertension has decreased, while diabetes mellitus and obesity have become
progressively more important in the etiology of heart failure (He J 2001 ).
-Obesity as a risk factor of heart failure
Studies have shown evidence for a causal relationship between obesity and heart failure (Fall T 2013 ).
Obesity not only independently increases the risk of heart failure by at least two-fold (Kenchaiah S 2002
), but also contributes to exacerbate heart failure risk factors such as hypertension, diabetes mellitus,
and ventricular hypertrophy. Studies have demonstrated obesity to be a risk factor for development of
heart failure, and all-cause mortality is elevated in obese individuals in the general population (McTigue
K 2006 ). However, it has been suggested that obesity (measured as higher levels of BMI) is associated
with better survival after developing heart failure (Kalantar-Zadeh K 2004 ) (V. H. Lavie CJ 2013 ). This
phenomenon, described as the “obesity paradox”, has been reported in a number of health conditions
including chronic renal failure and peripheral vascular disease as well as ICU and surgical populations (R.
2010 ) (J. 2013 ) (RN. 2013 ) (Valentijn TM 2013 ). However, existing studies are often limited by
relatively small samples and sampling of a limited number of regional institutions.
7
1.3 Hypothesis and Specific Aims
In this context, we aimed to evaluate the effect of morbid obesity on in-hospital mortality in patients
with heart failure. We used The National Inpatient Sampling (NIS) database, the largest all-payer
inpatient care database in the United States, to distinguish the characteristics of obese and non-obese
heart failure patients and estimate the effect of obesity on in-hospital mortality among these patients.
8
Chapter 2: Methods
2.1 Study Design and Study Population
A retrospective cohort study was conducted using the Agency for Healthcare Research and Quality
Healthcare Cost and Utilization Project National Inpatient Sample (NIS) database.
-The NIS Database
The NIS database is the largest population-based sample of all patients admitted with heart failure. NIS
covers in-hospital data from 44 states, approximating a 20% stratified sample of US community, non-
military, nonfederal hospitals, to include almost 95% of all hospital discharges in the US (Agency for
Healthcare Research and Quality 2009). Using in-hospital data from 2009-2010, we analyzed the
association of obesity at hospital admission with in-hospital mortality among patients with heart failure.
The NIS database includes hospital characteristics such as hospital volume, location, and teaching status.
Individual hospitalization records include demographic information (Age, gender, and race), primary
payer status, and clinical data, coded using International Classification of Diseases, Ninth Revision,
Clinical Modification (ICD-9.). Each NIS record is weighted on the sample selection probability and allows
an estimate of the total hospitalization number in the United States.
Institutional review board approval was obtained from the University of California, Irvine Medical Center
to utilize the NIS database for the planned analyses.
2.2 Definition of Heart Failure Population and Outcome Ascertainment Period
For the current analysis, we included patients admitted with a principal diagnosis of heart failure using
ICD-9 code 428 (Appendix.1). The positive predictive value of ICD-9 codes for heart failure has been
validated and the ICD-9 code has been used as a heart failure outcome in prior studies (Lee DS 2005 )
(Quan H 2002 ). Patients were excluded if heart failure developed as an in-hospital complication. We
limited the patient population to those with less than 14-day hospital stays, as patients with longer
length of stays tend to have other complications and comorbidities not related to heart failure. Further,
lengthier hospital stay has been linked with less evidence-based care (Vavalle JP 2012 ) (Martín-Sánchez
FJ and ICA-SEMES. 2013).
- Elixhauser risk-adjustment scheme
Elixhauser risk-adjustment scheme, developed by agency for healthcare research and quality was used
for the presence of up to 30 chronic comorbidities (Elixhauser A 1998). The Elixhauser comorbidity
measure developed a list of comorbidities using the ICD-9-CM coding manual. Each comorbidity affects
health outcomes including length of hospital stay, hospital changes, and mortality. Elixhauser
comorbidity index has been validated in prior studies (van Walraven C 2009). Among various
comorbidities indices, Elixhauser index has been identified as the best performing comorbidity measure
in a recent systematic review (Sharabiani, Aylin and Bottle 2012).
9
-Definition of Morbid Obesity
We divided patients with heart failure into groups with and without the diagnosis of morbid obesity as
defined by Elixhauser comorbidity index, by a body mass index (BMI) of at least 40 kg/m
2
. The primary
outcome of our study was in-hospital mortality among these two groups (morbidly obese vs not
morbidly obese). Additionally, we investigated differences in the 15 most frequent primary procedures
among patients with morbid obesity vs non-morbidly obese individuals.
2.3 Statistical Analysis
We used SAS version 9.3 (SAS Institute Inc., Cary, North Carolina) for statistical analyses. Differences
between morbidly obese patients and non-morbidly obese individuals on what relevant demographic
and clinical variables used Student’s t tests for continuous variables and chi-square tests for categorical
variables.
Univariate logistic regression analysis was used to compare the mortality odds. Adjusted odds ratios for
in-hospital mortality were estimated using multivariate logistic regression with adjustments for patient
demographics, including age, gender, race, income, primary payer status, the presence of Elixhauser-
defined comorbidities, and hospital characteristics including bed-size, location, and teaching affiliation.
Interaction terms with morbid obesity were tested to determine if the morbid obesity association with
in-hospital mortality differed over important subgroups, including (gender, race, and comorbidities).
Interaction terms significant at p<0.05 were included in the final model. All analyses were weighted by
the sampling probability, to measure national estimates.
Additionally, we used Cox proportional hazards regression to estimate the adjusted overall hazard ratio
of in-hospital mortality associated with morbid obesity. The proportional hazards mortality model
adjusted for the same covariates defined in the multivariate logistic model.
10
Chapter 3: Results
3.1 Baseline Characteristics
The 2009 NIS database included 966,366 admissions for heart failure of which 13.5% were morbidly
obese (Table 1). Among morbidly obese patients, 52.9% were male and 47.0% were female; among non-
morbidly obese patients, 49.7% were male and 50.2% were female (p<0.0001). Morbidly obese patients
were younger than those without morbid obesity (63.7 years versus 74.4 years old, p<0.0001). More
patients with morbid obesity were African American (Table 1). Chronic pulmonary diseases, depression,
diabetes, hypertension and hyperlipidemia were statistically significantly more prevalent among heart
failure patients with morbid obesity (Table 2).
3.2 In-hospital Mortality
Patients admitted for acute decompensated heart failure with morbid obesity had a significantly
decreased chance of in hospital death when compared to patients without morbid obesity (OR 0.51,
95% Confidence Interval (CI), 0.48-0.53). Mortality remained significantly lower after adjustment for
demographics, insurance, hospital characteristics, and comorbidity measures (OR 0.87, 95% CI, 0.83-
0.92). However, the odds ratio did increase as the model was adjusted for all AHRQ comorbidities (Table
3).
In the multivariate logistic model, other significant correlates of in-hospital mortality emerged. When
comparing hospitals, there was a significant lower in mortality for acute decompensated heart failure
between teaching versus non-teaching hospitals (OR = 0.91, 95% CI = 0.88-0.94), as well as urban versus
rural hospitals (OR = 0.90, 95% CI =0.87-0.94). Compared to patients with Medicare, patients with
Medicaid, and private insurance had a higher rate of mortality (OR 1.27 P< .0001 and 1.42 P< .0001
respectively). However, self-paid patients had a lower rate of mortality compared to patients with
Medicare (OR 0.73, 95% CI, 0.64-0.84). Furthermore in the patient sample, whites experienced
significantly higher odds of mortality from acute heart failure decompensation in comparison to the
other races. In regards to gender, males had a higher rate of mortality than women (OR = 1.06, 95% CI =
1.03-1.09).
Interaction terms with morbid obesity were tested to determine if the morbid obesity association with
in-hospital mortality differed over different subgroups, including gender, race, and comorbidities.
Interaction terms were significant for “Alcohol abuse”, “Chronic pulmonary disease”, and “Renal
failure”.
The association of morbid obesity with in-hospital mortality significantly differed in patients with
comorbid alcohol abuse (OR = 0.55; 95% CI, 0.34-0.90) versus without alcohol abuse (OR = 0.87; 95% CI,
0.83-0.92; p-value for interaction = 0.02). For patients with comorbid chronic pulmonary disease, the
association of morbid obesity with in-hospital mortality were significantly different compare to patients
without chronic lung disease (OR=0.98; 95% CI, 0.91-1.05 vs OR=0.79 (0.73-0.85) respectively, p-value
11
for interaction = 0.0005). Patients with morbid obesity and normal renal function had significant
decrease chance of in-hospital mortality (OR = 0.76; 95% CI, 0.70-0.81 p-value for interaction < 0.0001).
Analyses using the Cox proportional hazards regression analysis resulted in comparable adjusted overall
hazard ratios of in-hospital mortality associated with morbid obesity (HR 0.87, 95% CI, 0.87-0.88),
adjusted for same covariates in the multivariate logistic model.
12
Table 1.
Baseline characteristics of acute heart failure decompensation patients by morbid obesity*
With Morbid Obesity Without Morbid Obesity p-Value
Total (966366) 130059 (13.4%) 836307 (86.5%)
Age, mean (SD) 63.7 (30.8) 74.4 (31.3) <0.0001
Male (%) 68 52.9 49.7 <0.0001
Race (%) 139096 <0.0001
White 63.7 70.2
Black 24.4 16.8
Hispanic 7.7 7.4
Asian 0.8 1.8
Native American 0.5 0.5
Other 2.7 3.0
Primary Payer (%) 1672 <0.0001
Medicare 60.8 76.8
Medicaid 12.8 6.8
Private 18.6 11.4
Self-pay 5.1 3.0
No Charge 0.5 0.2
Other 2.0 1.5
Location of hospital (%) 15755 <0.0001
Rural 14.3 16.8
Urban 85.6 83.1
Teaching status of hospital (%)15755 <0.0001
Teaching 40.9 40.0
Nonteaching 59.0 59.9
Bedsize of hospital (%) 15755 <0.0001
Small 12.4 14.5
Medium 24.4 24.1
Large 63.1 61.1
Median household income (%) <0.0001
1 - 40,999 35.1 31.8
41,000 - 50,999 27.4 27.3
51,000 - 66,999 22.0 22.2
67,000+ 15.3 18.4
*Morbid obesity defined by a body mass index of 40 kg/m2 or more
13
Table 2.
Comorbidity of acute heart failure decompensation patients by morbid obesity*
With Morbid Obesity With Morbid Obesity p-value
AHRQ comorbidity measure (%)
Acquired immune deficiency
syndrome
0.25 0.08 <0.0001
Alcohol abuse 2.4 2.4 0.62
Deficiency anemias 26.5 27.3 <0.0001
Rheumatoid arthritis/collagen
vascular diseases
2.2 2.5 <0.0001
Chronic blood loss anemia 0.8 0.9 <0.0001
Chronic pulmonary disease 44.8 34.6 <0.0001
Coagulopathies 3.2 3.9 <0.0001
Depression 11.2 7.9 <0.0001
Diabetes 63.2 39.7 <0.0001
Drug abuse 2.19 2.10 0.03
Hypertension 75.9 69.6 <0.0001
Hypothyroidism 14.5 15.2 <0.0001
Liver disease 2.6 2.2 <0.0001
Lymphoma 0.4 1.0 <0.0001
Fluid and electrolyte disorders 24.6 24.7 0.526
Metastatic cancer 0.4 1.0 <0.0001
Other neurological disorders 4.8 6.7 <0.0001
Paralysis 1.3 1.7 <0.0001
Peripheral vascular disorders 8.9 11.1 <0.0001
Psychoses 3.6 2.3 <0.0001
Pulmonary circulation disorders 0.3 0.2 <0.0001
Renal failure 34.6 36.1 <0.0001
Solid tumor without metastasis 1.0 1.8 <0.0001
Peptic ulcer disease 0.03 0.03 0.50
Valvular disease 0.2 0.2 0.28
Weight loss 1.6 3.0 <0.0001
Hyperlipidemia 44.1 36.3 <0.0001
*Morbid obesity defined by a body mass index of 40 kg/m2 or more
14
Table 3.
Multivariate analysis of predictors of in-hospital mortality among patients with acute
decompensated heart failure
Odds Ratio
(95% Confidence Interval)
P-Value
Morbid Obesity* 0.87 (0.83-0.92) <.0001
Age 1.03 (1.03-1.04) <.0001
Male 1.06 (1.03-1.09) <.0001
Race
White Reference
Black 0.65 (0.62-0.69) <.0001
Hispanic 0.71 (0.67-0.78) <.0001
Asian 0.81 (0.72-0.90) 0.0001
Native American 0.70 (0.57-0.85) 0.0004
Other 1.02 (0.95-1.11) 0.4661
Primary Payer (%)
Medicare Reference
Medicaid 1.27 (1.17-1.36) <.0001
Private 1.42 (1.36-1.48) <.0001
Self-pay 0.73 (0.64-0.84) <.0001
No Charge 2.15 (1.67-2.76) <.0001
Other 2.89 (2.66-3.14) <.0001
Location of hospital (%)
Urban Reference
Rural 1.10 (1.06-1.14) <.0001
Teaching status of hospital (%)
Nonteaching Reference
Teaching 0.91 (0.88-0.94) <.0001
Bedsize of hospital (%)
Large Reference
Medium 1.02 (0.98-1.05) 0.20
Small 1.09 (0.74-1.61) 0.19
*Morbid obesity defined by a body mass index of 40 kg/m2 or more
15
Figure 1 - Plot of the estimated probability of overall survival associated with morbid obesity (Y)
and with no morbid obesity (N). The estimated probabilities of survival are plotted through 14 days
post admission.
16
Chapter 4: Discussion
This study reveals that morbid obesity is associated with a significantly reduced risk for in-hospital
mortality among patients hospitalized with acute exacerbation of heart failure. In our analysis of this
large nationally representative sample, we found that patients with a diagnosis of heart failure and
morbid obesity have 13% less (95% CI 8%-17%) in-hospital mortality compared to their non-morbidly
obese counterparts. This relationship was independent of other measured known prognostic factors and
comorbidities.
While epidemiologic data are consistent with an increased risk of death from all causes and higher in-
hospital mortality in obese individuals in general population, the relationship between BMI (as a quality
index for obesity) and mortality, especially in HF population is less clear (Cohen SS 2012) (Berrington de
Gonzalez A 2010) (Prospective Studies Collaboration 2009).
Previous relevantly smaller studies have shown that patients with HF in an outpatient setting with a
higher BMI are at a lower risk for death and hospitalization, as compared with patients in the normal
BMI range (Horwich TB 2001). Obesity was associated with lower mortality risk at 1- and 2-year follow
up in outpatients with advanced systolic HF (Horwich TB 2001) (O. A. Lavie CJ 2003) . Mortality risks in
overweight and obese patients were 26.1% and 47.8%, respectively, lower as compared with healthy
weight patients with HF. Moreover, underweight patients had a 37.0% increase in risk (Fonarow GC and
Investigators. 2007).
While varied explanations have been proposed for the obesity paradox in HF patients, the mechanisms
are not entirely clear. A catabolic state of HF, in addition to worse prognosis in cachectic HF patients
(Anker SD 1997 ), suggests that HF patients may benefit from larger metabolic reserve in the form of
fatty tissue (Davos CH 2003 ). Amplified symptoms and higher functional impairment in heart failure
patients with obesity may result in presentation or diagnosis at an earlier stage in their disease course
(Curtis JP 2005 ). Catecholamine responses are reduced in obese patients; studies have shown that
decreased catecholamine response is linked to improved HF survival (Weber MA 2001 ). The same
association has been described for low adiponectin levels (Kistorp C 2005).
The findings of our study are consistent with studies exploring the relationship between obesity and
health outcomes among patients hospitalized with acute conditions like coronary syndromes, ICU
patients and surgical populations (EL Eisenstein 2005) (Dickerson 2013) (Valentijn TM 2013 ).
Important limitations to our study must be noted; we identified the study population on the basis of
ICD-9 codes for heart failure. Although HF ICD-9 codes have been validated in prior studies (Lee DS,
2005) (Quan H, 2002), it is possible that coding practices may vary among hospitals. Another possible
limitation is the fact that we were unable to consider some of the unmeasured confounders in HF
presentation when accounting for the morbid obesity association with in-hospital mortality. Although
analyses were adjusted for important covariates, we did not have access to more detailed clinical data
including signs and symptoms of HF and laboratory values. Moreover, in administrative datasets like the
NIS dataset, obesity is undercoded, in that other levels of obesity (e.g. overweight and obese) are
17
misclassified as normal (Trasande L 2009 ). Assuming that such misclassification is not related to
mortality risk, this misclassification would bias the results toward the null. Additionally, our results are
only reflective of in-hospital mortality, despite a large portion of mortality occurring in the post-
discharge period and upon hospital readmission. Given the large population of patients covered in the
NIH sample, some differences, while statistically significant, may not be clinically noteworthy.
Despite these limitations, this study extends previous findings regarding the morbid obesity
effect on mortality for patients with ADHF.
18
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21
Appendix 1.
ICD-9 codes for the principal diagnoses of heart failure
Code Description
428 Heart failure
428.0 Congestive heart failure, unspecified
428.1 Left heart failure
428.2 Systolic heart failure
428.20 Systolic heart failure, unspecified
428.21 Acute systolic heart failure
428.22 Chronic systolic heart failure
428.23 Acute on chronic systolic heart failure
428.3 Diastolic heart failure
428.30 Diastolic heart failure, unspecified
428.31 Acute diastolic heart failure
428.32 Chronic diastolic heart failure
428.33 Acute on chronic diastolic heart failure
428.4 Combined systolic and diastolic heart failure
428.40 Combined systolic and diastolic heart failure, unspecified
428.41 Acute combined systolic and diastolic heart failure
428.42 Chronic combined systolic and diastolic heart failure
428.43 Acute on chronic combined systolic and diastolic heart failure
428.9 Heart failure, unspecified
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Asset Metadata
Creator
Khoshchehreh, Mahdi
(author)
Core Title
Obesity paradox in acute heart failure decompensation
School
Keck School of Medicine
Degree
Master of Science
Degree Program
Applied Biostatistics and Epidemiology
Publication Date
10/21/2014
Defense Date
10/21/2014
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
ADHF,heart failure,heart failure decompensation,OAI-PMH Harvest,obesity,obesity paradox
Format
application/pdf
(imt)
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
Mack, Wendy Jean (
committee chair
), Azen, Stanley P. (
committee member
), Hodis, Howard N. (
committee member
), Malik, Shaista (
committee member
)
Creator Email
mahdi.kh@live.com,mahdikh@usc.edu
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-c3-507929
Unique identifier
UC11298504
Identifier
etd-Khoshchehr-3024.pdf (filename),usctheses-c3-507929 (legacy record id)
Legacy Identifier
etd-Khoshchehr-3024.pdf
Dmrecord
507929
Document Type
Thesis
Format
application/pdf (imt)
Rights
Khoshchehreh, Mahdi
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 a...
Repository Name
University of Southern California Digital Library
Repository Location
USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA
Tags
ADHF
heart failure
heart failure decompensation
obesity
obesity paradox