Model-based comorbidity clusters in patients with heart failure: association with clinical outcomes and healthcare utilization.

BACKGROUND: Comorbidities affect outcomes in heart failure (HF), but are not reflected in current HF classification. The aim of this study is to characterize HF groups that account for higher-order interactions between comorbidities and to investigate the association between comorbidity groups and outcomes. METHODS: Latent class analysis (LCA) was performed on 12 comorbidities from patients with HF identified from administrative claims data in the USA (OptumLabs Data Warehouse®) between 2008 and 2018. Associations with admission to hospital and mortality were assessed with Cox regression. Negative binomial regression was used to examine rates of healthcare use. RESULTS: In a population of 318,384 individuals, we identified five comorbidity clusters, named according to their dominant features: low-burden, metabolic-vascular, anemic, ischemic, and metabolic. Compared to the low-burden group (minimal comorbidities), patients in the metabolic-vascular group (exhibiting a pattern of diabetes, obesity, and vascular disease) had the worst prognosis for admission (HR 2.21, 95% CI 2.17-2.25) and death (HR 1.87, 95% CI 1.74-2.01), followed by the ischemic, anemic, and metabolic groups. The anemic group experienced an intermediate risk of admission (HR 1.49, 95% CI 1.44-1.54) and death (HR 1.46, 95% CI 1.30-1.64). Healthcare use also varied: the anemic group had the highest rate of outpatient visits, compared to the low-burden group (IRR 2.11, 95% CI 2.06-2.16); the metabolic-vascular and ischemic groups had the highest rate of admissions (IRR 2.11, 95% CI 2.08-2.15, and 2.11, 95% CI 2.07-2.15) and healthcare costs. CONCLUSIONS: These data demonstrate the feasibility of using LCA to classify HF based on comorbidities alone and should encourage investigation of multidimensional approaches in comorbidity management to reduce admission and mortality risk among patients with HF.