Lorenzo Azzalini1, Malorie Chabot-Blanchet1, Danielle A Southern1, Anna Nozza1, Stephen B Wilton1, Michelle M Graham1, Guillaume Marquis Gravel1, Jean-Pierre Bluteau1, Jean-Lucien Rouleau1, Marie-Claude Guertin1, E Marc Jolicoeur2. 1. Department of Medicine (Azzalini, Marquis Gravel, Rouleau, Jolicoeur), Montreal Heart Institute, Université de Montréal; Montreal Health Innovations Coordinating Center (Chabot-Blanchet, Guertin); Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'île-de-Montréal (Bluteau), Montréal, Que.; O'Brien Institute for Public Health (Southern), Cumming School of Medicine, University of Calgary, Calgary, Alta.; Libin Cardiovascular Institute of Alberta, Departments of Cardiac Sciences and Community Health Sciences (Wilton), University of Calgary, Calgary, Alta.; Department of Medicine, University of Alberta and Mazankowski Alberta Heart Institute (Graham), Edmonton, Alta.; Interventional Cardiology Unit, Cardio-Thoraco-Vascular Department (Azzalini), San Raffaele Scientific Institute, Milan, Italy. 2. Department of Medicine (Azzalini, Marquis Gravel, Rouleau, Jolicoeur), Montreal Heart Institute, Université de Montréal; Montreal Health Innovations Coordinating Center (Chabot-Blanchet, Guertin); Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'île-de-Montréal (Bluteau), Montréal, Que.; O'Brien Institute for Public Health (Southern), Cumming School of Medicine, University of Calgary, Calgary, Alta.; Libin Cardiovascular Institute of Alberta, Departments of Cardiac Sciences and Community Health Sciences (Wilton), University of Calgary, Calgary, Alta.; Department of Medicine, University of Alberta and Mazankowski Alberta Heart Institute (Graham), Edmonton, Alta.; Interventional Cardiology Unit, Cardio-Thoraco-Vascular Department (Azzalini), San Raffaele Scientific Institute, Milan, Italy marc.jolicoeur@icm-mhi.org.
Abstract
BACKGROUND: Comorbidity indexes derived from administrative databases are essential tools of research in global health. We sought to develop and validate a novel cardiac-specific comorbidity index, and to compare its accuracy with the generic Charlson-Deyo and Elixhauser comorbidity indexes. METHODS: We derived the cardiac-specific comorbidity index from consecutive patients who were admitted to hospital at a tertiary-care cardiology hospital in Quebec. We used logistic regression analysis and incorporated age, sex and 22 clinically relevant comorbidities to build the index. We compared the cardiac-specific comorbidity index with refitted Charlson-Deyo and Elixhauser comorbidity indexes using the C-statistic and net reclassification improvement to predict in-hospital death, and the Akaike information criterion to predict length of stay. We validated our findings externally in an independent cohort obtained from a provincial registry of coronary disease in Alberta. RESULTS: The novel cardiac-specific comorbidity index outperformed the refitted generic Charlson-Deyo and Elixhauser comorbidity indexes for predicting in-hospital mortality in the derivation population (n = 10 137): C-statistic 0.95 (95% confidence interval [CI] 0.94-0.9) v. 0.81 (95% CI 0.77-0.84) and 0.86 (95% CI 0.82-0.89), respectively. In the validation population (n = 17 877), the cardiac-specific comorbidity index was similarly better: C-statistic 0.92 (95% CI 0.89-0.94) v. 0.76 (95% CI 0.71-0.81) and 0.82 (95% CI 0.78-0.86), respectively, and also numerically outperformed the Charlson-Deyo and Elixhauser comorbidity indexes for predicting 1-year mortality (C-statistic 0.78 [95% CI 0.76-0.80] v. 0.75 [95% CI 0.73-0.77] and 0.77 [95% CI 0.75-0.79], respectively). Similarly, the cardiac-specific comorbidity index showed better fit for the prediction of length of stay. The net reclassification improvement using the cardiac-specific comorbidity index for the prediction of death was 0.290 compared with the Charlson-Deyo comorbidity index and 0.192 compared with the Elixhauser comorbidity index. INTERPRETATION: The cardiac-specific comorbidity index predicted in-hospital and 1-year death and length of stay in cardiovascular populations better than existing generic models. This novel index may be useful for research of cardiology outcomes performed with large administrative databases.
BACKGROUND: Comorbidity indexes derived from administrative databases are essential tools of research in global health. We sought to develop and validate a novel cardiac-specific comorbidity index, and to compare its accuracy with the generic Charlson-Deyo and Elixhauser comorbidity indexes. METHODS: We derived the cardiac-specific comorbidity index from consecutive patients who were admitted to hospital at a tertiary-care cardiology hospital in Quebec. We used logistic regression analysis and incorporated age, sex and 22 clinically relevant comorbidities to build the index. We compared the cardiac-specific comorbidity index with refitted Charlson-Deyo and Elixhauser comorbidity indexes using the C-statistic and net reclassification improvement to predict in-hospital death, and the Akaike information criterion to predict length of stay. We validated our findings externally in an independent cohort obtained from a provincial registry of coronary disease in Alberta. RESULTS: The novel cardiac-specific comorbidity index outperformed the refitted generic Charlson-Deyo and Elixhauser comorbidity indexes for predicting in-hospital mortality in the derivation population (n = 10 137): C-statistic 0.95 (95% confidence interval [CI] 0.94-0.9) v. 0.81 (95% CI 0.77-0.84) and 0.86 (95% CI 0.82-0.89), respectively. In the validation population (n = 17 877), the cardiac-specific comorbidity index was similarly better: C-statistic 0.92 (95% CI 0.89-0.94) v. 0.76 (95% CI 0.71-0.81) and 0.82 (95% CI 0.78-0.86), respectively, and also numerically outperformed the Charlson-Deyo and Elixhauser comorbidity indexes for predicting 1-year mortality (C-statistic 0.78 [95% CI 0.76-0.80] v. 0.75 [95% CI 0.73-0.77] and 0.77 [95% CI 0.75-0.79], respectively). Similarly, the cardiac-specific comorbidity index showed better fit for the prediction of length of stay. The net reclassification improvement using the cardiac-specific comorbidity index for the prediction of death was 0.290 compared with the Charlson-Deyo comorbidity index and 0.192 compared with the Elixhauser comorbidity index. INTERPRETATION: The cardiac-specific comorbidity index predicted in-hospital and 1-year death and length of stay in cardiovascular populations better than existing generic models. This novel index may be useful for research of cardiology outcomes performed with large administrative databases.
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