D Tharanga Fernando1, Janneke Berecki-Gisolf2, Stuart Newstead2, Zahid Ansari3. 1. Monash University Accident Research Centre, Victoria, Australia. Electronic address: tara.fernando@monash.edu. 2. Monash University Accident Research Centre, Victoria, Australia. 3. Health Intelligence Unit, Victorian Agency for Health Information, Melbourne, Victoria, Australia.
Abstract
PURPOSE: Accounting for comorbidity in predicting outcomes for patients is vital in clinical care, epidemiological research, and health service planning. The aim of this study was to review published literature to compare the performance of existing comorbidity indices and their use in injury populations. METHODS: A thematic literature search for comorbidity indices and/or injury outcomes was conducted. Methods, results, and recommendations from selected articles were abstracted, documented, and compared; comparisons of results were made in terms of the indices' ability to predict outcomes, using the C-statistic, R2, and odds ratios. RESULTS: Fifty-two articles relating to the derivation and/or validation of comorbidity measures were found. The most commonly used measures were the Charlson Comorbidity Index (CCI) and the Elixhauser Comorbidity Measure (ECM). The ECM was found to outperform the CCI in terms of predictive ability, although the CCI was more widely used. Derivation of study-specific weights to the CCI added more predictive power to the index. CONCLUSIONS: Existing literature that compared the predictive abilities of the ECM and CCI favors the ECM. This literature review did not identify a measure specifically designed for general injury populations. Development of an injury-specific comorbidity measure will be timely and assist future research in injury epidemiology.
PURPOSE: Accounting for comorbidity in predicting outcomes for patients is vital in clinical care, epidemiological research, and health service planning. The aim of this study was to review published literature to compare the performance of existing comorbidity indices and their use in injury populations. METHODS: A thematic literature search for comorbidity indices and/or injury outcomes was conducted. Methods, results, and recommendations from selected articles were abstracted, documented, and compared; comparisons of results were made in terms of the indices' ability to predict outcomes, using the C-statistic, R2, and odds ratios. RESULTS: Fifty-two articles relating to the derivation and/or validation of comorbidity measures were found. The most commonly used measures were the Charlson Comorbidity Index (CCI) and the Elixhauser Comorbidity Measure (ECM). The ECM was found to outperform the CCI in terms of predictive ability, although the CCI was more widely used. Derivation of study-specific weights to the CCI added more predictive power to the index. CONCLUSIONS: Existing literature that compared the predictive abilities of the ECM and CCI favors the ECM. This literature review did not identify a measure specifically designed for general injury populations. Development of an injury-specific comorbidity measure will be timely and assist future research in injury epidemiology.