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      Comparison of different comorbidity measures for use with administrative data in predicting short- and long-term mortality

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      1 , 2 , 3 , 1 ,
      BMC Health Services Research
      BioMed Central

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          Abstract

          Background

          It is important to find a comorbidity measure with better performance for use with administrative data. The new method proposed by Elixhauser et al. has never been validated and compared to the widely used Charlson method in the Asia region. The objective of this study was to compare the performance of three comorbidity measures using information from different data periods in predicting short- and long-term mortality among patients with acute myocardial infarction (AMI) and chronic obstructive pulmonary disease (COPD).

          Methods

          We conducted a retrospective cohort study using National Health Insurance claims data (2001-2002) in Taiwan. We constructed the Elixhauser, the Charlson/Deyo, and the Charlson/Romano methods based on the International Classification of Disease, 9th Revision, Clinical Modification codes in the claims data. Two data periods, including the index hospitalization as well as the index and prior 1-year hospitalizations, were used in the analysis. The performances were compared using the c-statistics derived from multiple logistic regression models that included age, gender, race, and whether the patient received surgery or not. The outcomes of interest were in-hospital and 1-year mortality.

          Results

          The performance was in the same rank order among both populations regardless of the outcome and data period: Elixhauser > Charlson/Romano > Charlson/Deyo. In predicting in-hospital mortality, the Elixhauser models using information from the index hospitalization performed best, even better than the Charlson/Deyo or Charlson/Romano models using information from the index and prior hospitalizations. Nevertheless, in predicting 1-year mortality, the Elixhauser models using information from the index and 1-year prior hospitalizations performed better than using information from the index hospitalization only.

          Conclusions

          This is so far the first study to validate the Elixhauser method and compare it to other methods in the Asia region, and is the first to report its differences in data periods between short- and long-term outcomes. The comorbidity measurement developed by Elixhauser et al. has relatively good predictive validity, and researchers should consider its use in claims-based studies.

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          Most cited references21

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          Comorbidity measures for use with administrative data.

          This study attempts to develop a comprehensive set of comorbidity measures for use with large administrative inpatient datasets. The study involved clinical and empirical review of comorbidity measures, development of a framework that attempts to segregate comorbidities from other aspects of the patient's condition, development of a comorbidity algorithm, and testing on heterogeneous and homogeneous patient groups. Data were drawn from all adult, nonmaternal inpatients from 438 acute care hospitals in California in 1992 (n = 1,779,167). Outcome measures were those commonly available in administrative data: length of stay, hospital charges, and in-hospital death. A comprehensive set of 30 comorbidity measures was developed. The comorbidities were associated with substantial increases in length of stay, hospital charges, and mortality both for heterogeneous and homogeneous disease groups. Several comorbidities are described that are important predictors of outcomes, yet commonly are not measured. These include mental disorders, drug and alcohol abuse, obesity, coagulopathy, weight loss, and fluid and electrolyte disorders. The comorbidities had independent effects on outcomes and probably should not be simplified as an index because they affect outcomes differently among different patient groups. The present method addresses some of the limitations of previous measures. It is based on a comprehensive approach to identifying comorbidities and separates them from the primary reason for hospitalization, resulting in an expanded set of comorbidities that easily is applied without further refinement to administrative data for a wide range of diseases.
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            Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives.

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              Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data.

              Comorbidity is an important confounder in epidemiologic studies. The authors compared the predictive performance of comorbidity scores for use in epidemiologic research with administrative databases. Study participants were British Columbia, Canada, residents aged >or=65 years who received angiotensin-converting enzyme inhibitors or calcium channel blockers at least once during the observation period. Six scores were computed for all 141,161 participants during the baseline year (1995-1996). Endpoints were death and health care utilization during a 12-month follow-up (1996-1997). Performance was measured by using the c statistic ranging from 0.5 for chance prediction of outcome to 1.0 for perfect prediction. In logistic regression models controlling for age and gender, four scores based on the International Classification of Diseases, Ninth Revision (ICD-9) generally performed better at predicting 1-year mortality (c = 0.771, c = 0.768, c = 0.745, c = 0.745) than medication-based Chronic Disease Score (CDS)-1 and CDS-2 (c = 0.738, c = 0.718). Number of distinct medications used was the best predictor of future physician visits (R(2) = 0.121) and expenditures (R(2) = 0.128) and a good predictor of mortality (c = 0.745). Combining ICD-9 and medication-based scores improved the c statistics (1.7% and 6.2%, respectively) for predicting mortality. Generalizability of results may be limited to an elderly, predominantly White population with equal access to state-funded health care.
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                Author and article information

                Journal
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central
                1472-6963
                2010
                27 May 2010
                : 10
                : 140
                Affiliations
                [1 ]Institute of Health and Welfare Policy, School of Medicine, National Yang-Ming University, Taipei, Taiwan
                [2 ]Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
                [3 ]Department of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan
                Article
                1472-6963-10-140
                10.1186/1472-6963-10-140
                2897792
                20507593
                998ec502-386a-4b0e-b463-f2287d46dc02
                Copyright ©2010 Chu et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 4 August 2009
                : 27 May 2010
                Categories
                Research article

                Health & Social care
                Health & Social care

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