Original articles
A Comparison of the Charlson Comorbidity Index Derived from Medical Record Data and Administrative Billing Data

https://doi.org/10.1016/S0895-4356(98)00154-1Get rights and content

Abstract

The objective of this article is to compare the Charlson comorbidity index derived from medical record data (Chart Index) with the same index derived from billing data (ICD-9 Index) to determine how well each predicted inpatient and 30-day mortality, length of stay, and complications among Medicare beneficiaries hospitalized for carotid endarterectomy. Economic and time constraints have increased the need for risk adjusters derived from administrative data, yet few studies have compared these measures with those derived from chart review. Using logistic regression, the Chart Index was found to be a significant predictor of inpatient mortality, 30-day mortality, length of stay, and complications, after controlling for age, gender, and neurologic and medical risk factors (P values = 0.004, 0.056, 0.0001, and 0.042, respectively). The ICD-9 Index approached significance as a predictor of the outcomes (P values = 0.092, 0.100, 0.093, and 0.080, respectively). The Chart Index was shown to be superior to the ICD-9 Index within this patient sample.

Introduction

Risk adjustment in clinical and health services research studies has been advocated for several reasons 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13. For example, the effects of confounding factors in nonrandomized studies can be removed or reduced by risk adjusting the outcome measures. Failing to adjust for patient risk factors, including comorbidity, may call into question the scientific validity of the entire study and subsequent results.

Before development of the risk adjustment measure known as the Charlson comorbidity index, the only available method for classifying comorbidity was one developed by consensual criteria. The Charlson comorbidity index was originally developed using data abstracted from the medical record to provide a prognostic taxonomy for comorbid conditions that singly or in combination might alter the risk of short-term mortality for patients enrolled in longitudinal studies [14].

Charlson and colleagues [14] investigated the relationship between potential prognostically important variables and survival using Cox’s regression for life-table data. They calculated unadjusted and adjusted relative risks for each comorbid condition (controlling for all coexistent comorbid conditions, illness severity, and reason for admission) and used the adjusted relative risks as the basis for assigning weights to the comorbid conditions. The weighted index was tested for its ability to predict mortality in a cohort of women with histologically proved primary cancer of the breast. With each increased level of the comorbidity index, there was a stepwise increase in the cumulative mortality attributable to comorbid disease [14].

After the development of the Charlson comorbidity index in 1987, investigators attempted to adapt the index for use with administrative data based on recorded ICD-9-CM diagnoses. Although several ICD-9-CM adaptations of the Charlson index exist, the two most common versions are those developed by Deyo et al. [15] and the Dartmouth-Manitoba group [10]. These two adaptations differ slightly in their selection of ICD-9-CM codes chosen to represent the various comorbidities. When the two versions were compared through application to data from coronary artery bypass surgery, identical Charlson comorbidity scores were assigned to 90% of the cases, and kappa values for specific comorbidities were generally high [16]. In this study, we constructed an ICD-9 index based upon the codes selected by Deyo et al.

Using data from a large study that examined the practice patterns for carotid endarterectomy among Georgia Medicare beneficiaries, we evaluated the ability of the Charlson comorbidity index to predict each of several outcomes. We compared the Charlson comorbidity index derived from medical record data (Chart Index) with the same index derived from the ICD-9-CM codes contained in the Medicare administrative data (ICD-9 Index) to determine how well each predicted inpatient mortality, 30-day mortality, complications of stroke or myocardial infarction, and length of stay. There have been few studies designed to compare the ability of clinical and administrative databases to predict patient outcomes. Hannan et al. [17] compared the ability of a clinical database, the Cardiac Surgery Reporting System, and an administrative database, the Statewide Planning and Research Cooperative System, to predict in-hospital mortality and assess hospital performance for coronary artery bypass graft surgery (CABG) patients. The clinical database was substantially better at predicting case-specific mortality.

Section snippets

Data Collection

These data were collected as part of a project initiated by the Georgia Medical Care Foundation (GMCF) under the auspices of the Health Care Financing Administration’s (HCFA’s) Health Care Quality Improvement Program. The project was designed to assess the appropriateness and outcomes of carotid endarterectomy among Medicare beneficiaries in the state of Georgia. The GMCF, the Medicare peer review organization for the state of Georgia, collaborated with Case Mix Research at Queens University in

Results

Slightly more than half the Medicare beneficiaries undergoing carotid endarterectomy were male (53.3%). Most of the patients in this study were white (90.8%), with 4.7% patients identified as black, 2.0% other, and 2.5% unknown. Their mean age was 72.32 ± 6.80.

Sixty-eight Georgia hospitals performed a carotid endarterectomy and contributed data during the study period. The number of carotid endarterectomy procedures performed at each hospital during the study year varied from 1 to 223.

The mean

Conclusions

As more studies are conducted using administrative data, the need to have a valid and reliable measure derived from this data source for use as a risk adjuster becomes increasingly important. The advantages of using a risk adjuster based on administrative data include lower cost and reduced data collection time required. These advantages must be weighed against the possible loss in predictive ability and/or credibility associated with the use of administrative data for risk adjustment. In this

Acknowledgements

The analyses upon which this publication is based were performed under Contract Number 500-93-0704, entitled, “Operation of Utilization and Quality Control Peer Review Organization (PRO) for the State of Georgia,” sponsored by the Health Care Financing Adminsitration (HCFA), Department of Health and Human Services. The conclusions and opinions expressed, and methods used herein are those of the authors. They do not necessarily reflect HCFA policy. The authors assume full responsibility for the

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