A disease-specific comorbidity index for predicting mortality in patients admitted to hospital with a cardiac condition.

CMAJ

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

Published: March 2019

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 ( = 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 ( = 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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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6422783PMC
http://dx.doi.org/10.1503/cmaj.181186DOI Listing

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