Administrative hospital discharge abstract data are widely used in studies of healthcare outcomes. For valid and meaningful comparisons of providers, risk adjustment is essential since risk factors of outcomes are unevenly distributed across providers and variation in baseline status could make a major contribution to differences in patient outcomes. Risk adjustment is a complex construct that involves patient's socio-demographic factors (e.g. age, gender, and race), acute clinical stability, severity of primary disease, functional status, and burden of comorbidity [1
]. As major determinants of patient outcomes, comorbidities or coexisting conditions have been studied extensively for decades. Many methods have been developed to measure and control comorbidities. The Charlson [2
] and Elixhauser comorbidity measures [3
] are two commonly used instruments for risk adjustment analyses.
Charlson et al.
] studied numerous clinical conditions by reviewing inpatient hospital charts and assessing their relevance in the prediction of mortality. A weighted score was assigned to each of 17 comorbidities and the Charlson index was created as an indicator of disease burden. Elixhauser et al.
] used sets of individual ICD-9-CM diagnosis codes to identify categories of comorbidities. They measured 30 individual comorbidities that are associated with mortality. The performance of the Charlson and Elixhauser measures in predicting poor outcomes has been assessed on various large populations [4
]. These studies consistently demonstrated that they were valid prognostic measures of outcomes.
The World Health Organization adopted the first version of the International Classification of Disease (ICD) in 1900 to internationally monitor and compare mortality statistics and causes of death. Since then, the classification has been revised periodically to accommodate new knowledge of disease and health. The sixth revision, published in 1949, was more radical than the previous five revisions because this edition made it possible to record information from patient charts to compile morbidity statistics. Subsequent revisions were made in 1958 (7th
ed.), in 1968 (8th
ed.) and in 1979 (9th
ed.). The latest version ICD-10, was introduced in 1992 to replace the ICD-9 [12
]. The United States modified ICD-9 by specifying many categories and extending coding rubrics to describe the clinical picture in more detail. These modifications resulted in the publication of ICD-9-CM in 1979 for coding diagnoses in patient charts [13
Many countries such as Canada, Australia, New Zealand, Japan, China and European countries have already implemented the ICD-10. When compared to the ICD-9, the ICD-10 has a more comprehensive scope, effective structure, presentation and guidelines and allows for enhancements to accommodate newly discovered diseases [14
]. The codes in ICD-10 are alphanumeric whereas codes in ICD-9 are numeric. Each code in ICD-10 starts with a letter (i.e. A to Z), followed by two numeric digits, a decimal point, and a digit (e.g. I21.4 for acute subendocardial myocardial infarction). In contrast, codes in ICD-9-CM begin with three digit numbers (i.e. 001 to 999), that are followed by a decimal and up to two digits (e.g. 410.7 for subendocardial infarction).
Since implementation of the ICD-10, researchers have been evaluating the performance of the Charlson and Elixhauser comorbidity measures using ICD-10 data. Quan et al.
] developed ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities and assessed the performance of the resulting algorithms in predicting in-hospital mortality. Sundarajan et al.
] used Australian ICD-10 administrative data to evaluate the Charlson comorbidity measure in predicting in-hospital mortality. In both studies, the adaptation of the Charlson comorbidity measure for use with ICD-10 data yielded similar prevalence and prognosis information to a Charlson comorbidity measure based on ICD-9-CM.
These two studies assessed the performance of Charlson or Elixhauser measures in risk-adjustment in adult hospital admissions. However, these studies did not test the performance of the Charlson and Elixhauser comorbidity measures in predicting short and long term mortality in disease specific patient cohorts using ICD-10 administrative data. The present study addresses the above gap by using a large Canadian provincial hospital discharge administrative database containing ICD-9 and ICD-10 codes. Our study assesses the performance of Charlson and Elixhauser comorbidity measures in predicting in-hospital and one year mortality in five cohorts, including patients with congestive heart failure (CHF), diabetes, chronic renal failure (CRF), stroke and patients undergoing coronary artery bypass grafting (CABG).