Acta cardiologica · 2025
Predicting heart failure mortality using the Danish Comorbidity Index for Acute Myocardial Infarction (DANCAMI).
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Sikandar Usama, Bonnesen Kasper, Heide-Jørgensen Uffe, Schmidt Morten
Аннотация
BACKGROUND: Patients with congestive heart failure (HF) are often burdened with comorbidities that increase mortality. Comorbidity indices provide a standardised method to measure comorbidity burden and predict prognosis. We aimed to investigate whether the Danish Comorbidity Index for Acute Myocardial Infarction (DANCAMI) can discriminate mortality in patients with HF.
METHODS: We conducted a population-based cohort study of all adult Danish patients with first-time HF during 1995-2020 (N = 311,628). We used logistic regression to calculate the area under the receiver operating characteristic curve (AUC) for cardiovascular and all-cause mortality within 30 days, 1 year, and 10 years of diagnosis. The AUCs were computed for a model including age and sex (baseline) and models also including the DANCAMI, the Charlson Comorbidity Index (CCI), or the Elixhauser Comorbidity Index (ECI).
RESULTS: For all-cause mortality, the AUCs were higher for the DANCAMI than for the baseline model (30-day: 0.688 vs. 0.662; 1-year: 0.715 vs. 0.680; 10-year: 0.840 vs. 0.810). For cardiovascular mortality, the AUCs were comparable between the DANCAMI and the baseline model (30-day: 0.683 vs. 0.676; 1-year: 0.690 vs. 0.684; 10-year: 0.659 vs. 0.658). For both 30-day, 1-year, and 10-year all-cause and cardiovascular mortality, the AUCs for the CCI and the ECI were comparable to those for the DANCAMI.
CONCLUSIONS: Adding the DANCAMI to a model including patient age and sex improved discrimination of short and long-term all-cause mortality but not of cardiovascular mortality.
METHODS: We conducted a population-based cohort study of all adult Danish patients with first-time HF during 1995-2020 (N = 311,628). We used logistic regression to calculate the area under the receiver operating characteristic curve (AUC) for cardiovascular and all-cause mortality within 30 days, 1 year, and 10 years of diagnosis. The AUCs were computed for a model including age and sex (baseline) and models also including the DANCAMI, the Charlson Comorbidity Index (CCI), or the Elixhauser Comorbidity Index (ECI).
RESULTS: For all-cause mortality, the AUCs were higher for the DANCAMI than for the baseline model (30-day: 0.688 vs. 0.662; 1-year: 0.715 vs. 0.680; 10-year: 0.840 vs. 0.810). For cardiovascular mortality, the AUCs were comparable between the DANCAMI and the baseline model (30-day: 0.683 vs. 0.676; 1-year: 0.690 vs. 0.684; 10-year: 0.659 vs. 0.658). For both 30-day, 1-year, and 10-year all-cause and cardiovascular mortality, the AUCs for the CCI and the ECI were comparable to those for the DANCAMI.
CONCLUSIONS: Adding the DANCAMI to a model including patient age and sex improved discrimination of short and long-term all-cause mortality but not of cardiovascular mortality.