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Agreement between administrative and clinical data for term newborns with congenital malformations

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Agreement between administrative and clinical data for term newborns with congenital malformations

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Content AGREEMENT BETWEEN ADMINISTRATIVE AND CLINICAL DATA FOR
TERM NEWBORNS WITH CONGENITAL MALFORMATIONS.
by
Philippe S Friedlich
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirement for the Degree
MASTER IN SCIENCE
APPLIED BIOSTATISTICS AND EPIDEMIOLOGY
December 2004
Copyright 2004 Philippe S Friedlich
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UMI Number: 1424219
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Acknowledgements
I would like to recognize the invaluable mentorship and dedication to my education
from Dr. Azen, Dr. Korst, Dr. Kipke and Dr. Seri as well as the entire faculty of the
USC Division of Neonatal Medicine for without them I would not have had the
protected time to pursue my Master in Applied Biostatistics and Epidemiology.
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Table of Contents
1) Acknowledgements
2) Abbreviations
3) Abstract
4) Body
Introduction
Methods
Results
Discussion
5) Table
6) Bibliography
7) Appendix
Page number
ii
iv
v
1-10
1-2
2-3
3-4
7-10
5-6
11-12
13
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Abbreviations:
NICU: Neonatal Intensive Care Unit
OSHPD: California Office of Statewide Health Planning and Development
ICD-9-CM: International Classification of Disease, Ninth Revision, Clinical
Modification
Freq: Frequency
CNS: Central Nervous System
AVM: Arterio Venous Malformation
MMC: Meningomyelocele
VPS: Ventriculoperitoneal Shunt
TAPVR: Total Anomalous Pulmonary Venous Return
VSD: Ventricular-Septal Defect
ASD: Atrial-Septal Defect
TEF: Tracheo-Esophageal Fistula
CCAM: Congenital Cystic Adenomatoid Malformation
CDH: Congenital Diaphragmatic Hernia
PPHN: Persistent Pulmonary Hypertension of the Newborn
Chrs: Chromosomes
GU: Genito-Urinary
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ABSTRACT
Background: The purpose of this study was to determine the level of agreement
between administrative and clinical codes for neonatal conditions associated with
congenital malformations.
Methods: The study population consisted of newborns admitted to Childrens
Hospital Los Angeles in 2002. ICD-9-CM discharge codes reported to the California
Office of Statewide Health Planning and Development were obtained. Data
abstracted from clinical charts were merged with administrative data to categorize
each patient by clinical chart review and administrative data. Thirty-eight conditions
were classified into 6 organ categories. Condition frequency, organ category percent
agreement and inter-rater kappa statistics were calculated.
Results: Except for pulmonary diseases, these data show that for renal, CNS, genetic,
GI and cardiac conditions, there was a significant deviation from chance agreement
between clinical and administrative coding.
Conclusions: Administrative data tended to provide moderate to fair identification of
categories for congenital malformations among newborns when compared to chart
review.
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Introduction:
Uses of administrative healthcare data are increasing, particularly with respect to
both clinical and health services research, where investigators have used these data to
identify at-risk patients and to explore variation in healthcare services and outcomes
across hospitals or regions (3, 5-8,13). The quality indicators published by the
federal Agency for Healthcare Research and Quality are derived from administrative
data, and are intended to help organizations identify potential problems in quality of
care (1).
The clinical and healthcare services issues addressed by these efforts have largely
focused on conditions that are prevalent among adult, as opposed to pediatric
populations (4, 9-12, 14). One pediatric concern without a counterpart in adult
healthcare is the diagnosis, treatment, and outcome of severe congenital
malformations. Advances in perinatal and neonatal care have led to the survival of a
growing number of children bom with severe congenital malformations (12, 15).
Clinical outcomes associated with these malformations may vary widely, and depend
on social, medical and surgical risk factors, and complications. Hospital-level
administrative data have not only been essential to the performance of
epidemiological studies regarding these malformations, but also will likely provide
the foundation for the future identification and characterization of healthcare services
and outcomes of affected newborns.
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Given the widespread uses of the ICD-9-CM codes for both clinical research and
hospital operational purposes, the objective of this study is to describe the level of
agreement between clinical and administrative sources using the International
Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) codes
for neonatal conditions associated with congenital malformations.
Methods:
The study was approved by the Institutional Review Board at Childrens Hospital Los
Angeles (CHLA), which is a level 3 regional referral center for the greater Los
Angeles region. The study population consisted of a cohort of all newborns and
infants whose birth weights were equal or greater than 1500g, who were admitted to
the Center for Newborn and Infant Critical Care Unit at CHLA between January 1st
and December 31 2002. Premature and small for gestational age newborns with a
birth weight less than 1500g were excluded from this study for purposes of clarity;
they represent a small percentage of patients admitted for congenital malformations
at Childrens Hospital Los Angeles (< 5%). Only the initial inpatient visit to CHLA is
included in these analyses.
ICD-9-CM discharge codes reported to the California Office of Statewide Health
Planning and Development (OSHPD) were obtained from the hospital administrative
database. Medical charts for the entire study population were reviewed by a
neonatologist (PF) for demographic characteristics, the occurrence of congenital
2
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malformations, and their corresponding ICD-9-CM codes as listed in the Appendix.
A total of 38 malformations were categorized by the following organ system for
clarity of presentation and statistical analysis: central nervous system (CNS), cardiac,
gastrointestinal (GI), pulmonary, genetic, and renal. Data abstracted from clinical
charts were merged with the hospital’s administrative data so that each patient was
then classified with respect to the above information by both clinical chart review
data and administrative data. The frequency of each neonatal condition, percent
agreement, and inter-rater kappa statistics for each organ system were calculated.
Recognizing that chart review data might be considered an inadequate reference
standard, the kappa statistic was used to assess agreement between the administrative
and chart databases. Kappa values were ranked according to the criteria reported by
Landis and Koch ranging from 0 to 0.20 indicating a poor agreement between
databases, 0.21 to 0.40 fair agreement, 0.41 to 0.60 moderate agreement, 0.61 to 0.80
substantial agreement and 0.81 to 1.0 near perfect agreement (17).
Data were analyzed using SAS (Statistical Software Package v.9.0, Cary, NC) and
with Stata/SE 8.0 (Stata Corporation College Station, TX).
Results:
A total of 176 newborns met the study inclusion criteria. Both clinical charts and
administrative data were available for all patients.
Our results are presented in the Table, which describes the clinical frequency and
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administrative of the 38 neonatal conditions, the percent agreement, kappa
coefficients and p values for each of the 6 organ systems representing ICD-9-CM
coding for congenital malformations described in the Appendix.
These data show that for renal, CNS, genetic, GI, and cardiac conditions, there was a
significant deviation from chance agreement between clinical and administrative
coding. In contrast, for pulmonary diseases, the agreement between clinical and
administrative coding was not statistically different from chance (kappa= 0.11, p=
0.06).
Following the classification by Landis and Koch (17), of the six organ systems, 3
showed moderate level of agreement [renal conditions (kappa = 0.42, p<0.0001),
CNS conditions (kappa = 0.53, p<0.0001) and genetic conditions (kappa = 0.43,p
<0.0001)]. Tow organ systems showed fair agreements [GI conditions (kappa=0.39,
p<0.0001) and for cardiac conditions (kappa = 0.27, p=0.0001)]. Poor agreement is
noted for pulmonary conditions (kappa =0.11, p=0.06). No comparisons showed
substantial or perfect agreement.
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Table: Congenital Malformations: Agreement of the ICD - 9 -CM Coding. N= 176.
Clinical Freq Administrative Percent Kappa
Conditions and (%) Freq and (%) Agreement Coefficient p-value
Renal
Dysplastic Kidney 3(1.70) 3(1.70)
Potter Sequence 0 (0.00) 3(1.71)
Other GU defect 0(0.00) 7(3.97)
Renal Agenesis 1 (0.57) 1 (0.57)
Total Renal Conditions 4 (2.27) 14 (7.95) 94.32 0.42 < 0.0001
CNS
MMC and VPS 5(2.84) 3(1.71)
Spina Bifida 3(1.71) 5 (2.84)
Hydrocephaly 5 (2.84) 2 (1.14)
Microcephaly 1 (0.56) 2(1.14)
Holoprosencephaly 0 (0.00) 1 (0.56)
AVM 0(0.00) 1 (0.56)
Encephalocele 0(0.00) 0 (0.00)
Anencephaly 0(0.00) 0(0.00)
Total CNS Conditions 14 (7.95) 14 (7.95) 93.18 0.53 < 0.0001
Genetic
Trisomy 21 11 (6.25) 9(5.11)
Trisomy 13 0 (0.00) 0(0.00)
Trisomy 18 0 (0.00) 0 (0.00)
Other chrs 3(1.70) 4 (2.27)
Metabolic disorders 2(1.14) 2(1.14)
Hydrop fetalis 1 (0.57) 0 (0.00)
Skeletal dysplasia 1 (0.57) 0 (0.00)
Myotonic Dystrophy 0 (0.00) 0 (0.00)
Total Genetic Conditions 18(10.23) 15(8.52) 90.34 0.43 < 0.0001
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Table (continued): Congenital Malformations: Agreement of the ICD - 9 -CM Coding. N= 176
Clinical Freq Administrative Percent Kappa
Conditions and (%) Freq and (%) Agreement Coefficient p-value
GI
TEF 8 (4.45) 7(3.98)
Hirschprung disease 4 (2.27) 6 (3.41)
Small bowel atresia 7(3.98) 6 (3.41)
Large bowel atresia 8 (4.45) 9(5.11)
Pyloric stenosis 0 (0.00) 0 (0.00)
Abdominal wall defect 1 (0.57) 1 (0.57)
Meconium ileus 0 (0.00) 2 (1.14)
Cleft lip/palate 1 (0.56) 1 (0.56)
Other GI Conditions 2(1.14) 4(2.27)
Total GI Conditions 31(17.61) 36 (20.45) 81.25 0.39 < 0.0001
Pulmonary
PPHN 23 (13.06) 10(5.68)
CDH 9(5.11) 14 (7.95)
CCAM 1 (0.57) 1 (0.57)
Total Pulmonary Conditions 33(18.75) 23(13.06) 76.08 0.11 0.06
Cardiac
ASD 25 (14.20) 28(15.91)
TAPVR 2(1.14) 1 (0.54)
Pericardial effusion 1 (0.56) 4 (2.27)
VSD 15 (8.52) 19(10.80)
Other cardiac Conditions 8 (4.45) 33(18.75)
Total Cardiac Conditions 51 (28.98) 85 (48.29) 63.64 0.27 < 0.0001
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Discussion:
To our knowledge, this study is the first to attempt to examine the level of agreement
between clinical and administrative use of ICD-9-CM codes for diagnoses related to
congenital malformations of term newborns. Overall, the percent agreement
between administrative data coding and clinical diagnoses was generally high within
each organ system. The percent agreement for most congenital malformations
associated with renal, CNS, and genetics diseases were usually better than 90%. On
the other hand, the coding percent agreements for GI, pulmonary or CV systems
were less than 80%. This is not unexpected. Previous authors have examined the
relationship between ICD-9-CM administrative and clinical chart review data and
found similar results with respect to coding and the level of agreement using the
ICD-9-CM administrative codes (17).
In our study, conditions associated with significant phenotypic presentations (renal
conditions such as renal agenesis or dysplasia; central nervous system malformation
such as microcephaly, spina bifida; or genetic syndromes and chromosomal
anomalies) resulted in higher percent agreements as compared to more subtle disease
phenotypes (GI, cardiac or pulmonary malformations).
Similarly, conditions that were asymptomatic or do not play a significant role at the
time of admission, such as a small ventricular septal defect or an atrial septal defect,
appear less likely to be identified by administrative codes. Additional possible
reasons for the failure of hospital coders to identify clinical conditions may be:
1) Lack of clinical consensus regarding the definition of the condition. For example,
7
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newborn infants are often described to have physiologically patent foramen ovale
(a normal anatomical finding not associated with congenital malformations), which
may be falsely clinically recognized as an atrial septal defect (a true congenital
malformation). In these instances, a physician initially describes and documents such
finding as a possible congenital malformation and this may be miscoded by the
hospital coder. 2) Lack of consistency in coding the condition within the medical
chart. In these instances, conditions such as persistent pulmonary hypertension of the
newborn (PPHN) can have a wide range of clinical presentation and disease
manifestation. Coders may positively identify those newborns with the most
clinically significant forms of PPHN, i.e. those causing life-threatening disease, but
often are misled by the lack of documented criteria to confirm such pathology.
Furthermore, the ICD-9-CM code is not necessarily applicable in these less serious
forms of the disease. This is illustrated in our study where the charts reviewed
identified 23 cases of PPHN using clinical criteria, as compared to 10 cases
recognized in the administrative dataset. 3) Concerns regarding supporting
documentation of specific diagnoses that may affect reimbursements. 4) Limited
medical knowledge or experience on behalf of the coding staff. In this study,
occurrences of pericardial effusions were erroneously identified by the coders as
congenitally acquired, when indeed they resulted from medical or surgical
complications not associated with congenital malformations.
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These results emphasize that education and training is important for clinicians and
for hospital-based medical coders to assure accurate diagnostic code assignment. To
achieve this goal, efforts to assign ICD-9-CM codes in conjunction with practices of
the patient care team must continue. It is likely that electronic medical record
documentation will become more developed and widespread over time and ease the
ability to improve the accuracy of diagnosis and analysis of patient outcomes. In the
meantime, administrative data collection systems already in place will require
iterative improvement; particularly since they currently provide the only feasible
option for public assessment of variation and quality of care.
We acknowledge that not all codes may be sufficiently prevalent to serve a
monitoring or surveillance function. Measurements across regions and hospitals over
time may improve the precision and reproducibility of ICD-9-CM validation as well
as decrease the limitation of generalizability of the data. This study cohort is from a
single hospital. The application of coding guidelines is likely to be inconsistent
across hospitals and over time. A study of this variation in the application of ICD-9-
CM codes remains to be done for neonatal administrative data, although such data
are the basis for much of the information that is released to insurers, employers,
regulatory agencies, and the public regarding the quality and content of neonatal care
received.
In summary, administrative databases such as the California Office of Statewide
Health Planning and Development appear to provide moderate agreement as
9
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compared to clinical data for congenital malformations with strong phenotypic
penetrance. These results suggest that epidemiologists and researchers may be able
to begin studying these congenital malformations using broad organ classifications.
The lack of inter-rater agreement for codes such as cardiac congenital defect (ASD,
VSD), or pulmonary conditions such as PPHN, remains an important issue; and it is
likely that with the implementation and continuation of collaborative processes
between clinical care teams and hospital-based coders the accuracy of administrative
data may be improved. Larger studies across multiple hospital types and regions are
needed for improved understanding of the utility and limitation of these data.
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Bibliography:
1) Agency for Healthcare Research and Quality. AHRQ Quality Indicators -
Guide to Inpatient Quality Indicators. Rockville, MD: Agency for Healthcare
Research and Quality, 2003. AHRQ Publication 03-R203.
2) Arias E, MacDorman MF, Strobino D, Guyer B. Annual Summary of Vital
Statistics - 2002. Pediatrics 2003; 112: 1215-1230.
3) Beal AC, JP T Co, Dougherty D, Jorsling T, Kam J, Perrin J, palmer RH.
Quality Measures for Children’s Health Care. Pediatrics 2004; 113: 199-209.
4) DiGiuseppe DD, Aron DC, Payne SMC, Snow RJ, Dierker L, Rosenthal GE.
Risk adjusting cesarean delivery rates: A comparison of hospital profiles
based on medical record and birth certificate data. Health Serv Res 2001;
36(5): 959-977.
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identify indications for elective primary cesarean delivery. Health Services
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to identify indications for both primary and repeat cesarean sections.
American Journal of Public Health 1995; 85(8): 1143-1146.
7) Herrchen-Danielsen, B. and J.B. Gould, User Manual and Technical Report:
Linkage of Vital Statistics Linked Birth/Infant Death, Infant, and Maternal
Hospital Discharge File. 1996, UC Berkeley: Berkeley.
8) Herrchen, B., J.B. Gould, and T.S. Nesbitt, Vital Statistics Linked
Birth/Infant Death and Hospital Discharge Record Linkage for
Epidemiological Studies. Computers and Biomedical Research, 1997.
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of hospital care: Pitfalls and potential of ICD-9-CM." International Journal
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10) Iezzoni LI. 1997. "Assessing quality using administrative data." Annals of
Internal Medicine; 127(8S) Supplement: 666-74.
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11) Iezzoni LI. 1994. "Using risk-adjusted outcomes to assess clinical practice:
An overview of issues pertaining to risk adjustment." Annals of Thoracic
Surgery; 58:1822-6.
12) Iezzoni LI, Ash AS, Shwartz M, Daley J, Hughes JS, Mackieman YD.
Predicting who dies depends on how severity is measured: Implications for
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18(2): 112-119.
14) Meux EF, Stith SA, Zach A. Report of Results from the OSHPD
Reabstarcting Project: An Evaluation of the Reliability of Selected Patient
Discharge Data July Through December 1988. Sacramento, CA and Los
Angeles, CA: Patient Discharge Data Section, Office of Statewide Health
Planning and Development, State of California; 1990.
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Appendix:
Conditions ICD-9-CM
Anencephaly 740.0
Encephalocele 742.0
Arterio Venous Malformation (AVM) 747.81
Holoprosencephaly 742.2/758.1/758.2
Microcephaly 742.1
Hydrocephalus 742.3
Spina Bifida 741.x
Meningomylocele (MMC) no hydrocephalus 741.9
Any CNS abnomalies 740.x - 742.x
Ebstein anomaly 746.2
Total Anomalous Pulmonary Venous Return (TAPVR) 747.4
Pericardial effusion 423.9
Ventricular Septal Defect (VSD) 745.4
Atrial Septal Defect (ASD) 745.5
Any cardiac abnomalies 745.x/746.x/747.x
Pyloric stenosis 750.5
Abdominal wall defect 756.79
Meconium ileus 777.1
Cleft lip/palate 749.x
Tracheal Esophageal Fistula (TEF) 750.3
Hirschprung disease 751.3
Small bowel atresia 751.1
Large bowel atresia 751.2
Congenital Cystic Adenomatoid Malformation (CCAM) 748.3/748.60/748.61/748.69
Congenital Diaphragmatic Hernia (CDH) 756.6
Persistent fetal circulation (PPHN) 743.83
Myotonic dystrophy 359.0
Skeletal dysplasia 259.4/756.0/756.4/756.5
Hydrop fetalis 778.0
Dysplastic kidney 753.1x
Inborn Error Metabolism 270.X/271.X/272.X/277.5/277.9
Renal agenesis 753.0
Potter seguence 753.0/761.1/761.2
Other Genito-urinary defect 753.3/753.4/753.6-753.7
Trisomy 18 758.2
Trisomy 13 758.1
Trisomy 21 758.0
Other chromosomal anomalies 758.3-758.9
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Asset Metadata

Creator Friedlich, Philippe S. (author)

Core Title Agreement between administrative and clinical data for term newborns with congenital malformations

School Graduate School

Degree Master of Science

Degree Program Applied Biostatistics and Epidemiology

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

Tag health sciences, medicine and surgery,health sciences, obstetrics and gynecology,OAI-PMH Harvest

Language English

Contributor Digitized by ProQuest (provenance)

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c16-319821

Unique identifier UC11328997

Identifier 1424219.pdf (filename),usctheses-c16-319821 (legacy record id)

Legacy Identifier 1424219.pdf

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Document Type Thesis

Rights Friedlich, Philippe S.

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

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