Predicting 6-month mortality of patients from their medical history: Comparison of multimorbidity index to Deyo–Charlson index

Abstract

While every disease could affect a patient’s prognosis, published studies continue to use indices that include a selective list of diseases to predict prognosis, which may limit its accuracy. This paper compares 6-month mortality predicted by a multimorbidity index (MMI) that relies on all diagnoses to the Deyo version of the Charlson index (DCI), a popular index that utilizes a selective set of diagnoses. In this retrospective cohort study, we used data from the Veterans Administration Diabetes Risk national cohort that included 6,082,018 diabetes-free veterans receiving primary care from January 1, 2008 to December 31, 2016. For the MMI, 7805 diagnoses were assigned into 19 body systems, using the likelihood that the disease will increase risk of mortality. The DCI used 17 categories of diseases, classified by clinicians as severe diseases. In predicting 6-month mortality, the cross-validated area under the receiver operating curve for the MMI was 0.828 (95% confidence interval of 0.826–0.829) and for the DCI was 0.749 (95% confidence interval of 0.748–0.750). Using all available diagnoses (MMI) led to a large improvement in accuracy of predicting prognosis of patients than using a selected list of diagnosis (DCI).