BACKGROUND: The Charlson Score is a particularly popular form of comorbidity adjustment in claims data analysis. However, the effects of certain implementation decisions have not been empirically examined. OBJECTIVE: To determine the effects of alternative data sources and lookback periods on the performance of Charlson scores in the prediction of mortality following hospitalization. SUBJECTS: A representative sample of 1,387 elderly patients hospitalized in 1993, drawn from the Medicare Current Beneficiary Survey (MCBS). Three years of linked Medicare claims and survey instruments were available for all patients, as was 2-year mortality follow-up. STATISTICAL METHODS: Nested Cox regression and comparisons of areas under the Receiver Operating Characteristic (ROC) curve were used to evaluate ability to predict mortality. RESULTS: Compared with a 1-year lookback involving solely inpatient claims, statistically and empirically significant improvements in the prediction of mortality are obtained by incorporating alternative sources of data (particularly 2 years of inpatient data and 1 year of outpatient and auxiliary claims), but only if indices derived from distinct sources of data are entered into the regression distinctly. The area under the ROC curve for 1-year mortality predication increases from 0.702 to 0.741 (P = 0.002). Furthermore, these improvements in explanatory power obtained whether one also controls for Charlson scores based on self-reported health history and/or secondary diagnoses from the claim for the index hospitalization itself. Finally, claims-based comorbidity adjustment performs comparably to survey-derived adjustment, with areas under the ROC curve of 0.702 and 0.704, respectively. CONCLUSIONS: The widespread practice of comorbidity adjustment in pre-existing administrative data sources can be improved by taking more complete advantage of existing administrative data sources.
BACKGROUND: The Charlson Score is a particularly popular form of comorbidity adjustment in claims data analysis. However, the effects of certain implementation decisions have not been empirically examined. OBJECTIVE: To determine the effects of alternative data sources and lookback periods on the performance of Charlson scores in the prediction of mortality following hospitalization. SUBJECTS: A representative sample of 1,387 elderly patients hospitalized in 1993, drawn from the Medicare Current Beneficiary Survey (MCBS). Three years of linked Medicare claims and survey instruments were available for all patients, as was 2-year mortality follow-up. STATISTICAL METHODS: Nested Cox regression and comparisons of areas under the Receiver Operating Characteristic (ROC) curve were used to evaluate ability to predict mortality. RESULTS: Compared with a 1-year lookback involving solely inpatient claims, statistically and empirically significant improvements in the prediction of mortality are obtained by incorporating alternative sources of data (particularly 2 years of inpatient data and 1 year of outpatient and auxiliary claims), but only if indices derived from distinct sources of data are entered into the regression distinctly. The area under the ROC curve for 1-year mortality predication increases from 0.702 to 0.741 (P = 0.002). Furthermore, these improvements in explanatory power obtained whether one also controls for Charlson scores based on self-reported health history and/or secondary diagnoses from the claim for the index hospitalization itself. Finally, claims-based comorbidity adjustment performs comparably to survey-derived adjustment, with areas under the ROC curve of 0.702 and 0.704, respectively. CONCLUSIONS: The widespread practice of comorbidity adjustment in pre-existing administrative data sources can be improved by taking more complete advantage of existing administrative data sources.
Authors: Sachin Yende; Walter Linde-Zwirble; Florian Mayr; Lisa A Weissfeld; Steven Reis; Derek C Angus Journal: Am J Respir Crit Care Med Date: 2014-05-01 Impact factor: 21.405
Authors: M Gallucci; S Mazzuco; F Ongaro; E Di Giorgi; P Mecocci; M Cesari; D Albani; G L Forloni; E Durante; G B Gajo; A Zanardo; M Siculi; L Caberlotto; C Regini Journal: J Nutr Health Aging Date: 2013-04 Impact factor: 4.075
Authors: César A Hidalgo; Nicholas Blumm; Albert-László Barabási; Nicholas A Christakis Journal: PLoS Comput Biol Date: 2009-04-10 Impact factor: 4.475