Abstract
BackgroundComorbidities 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.MethodsLatent 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.ResultsIn 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.ConclusionsThese 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.
Highlights
Comorbidities affect outcomes in heart failure (HF), but are not reflected in current HF classification
There is increasing recognition that this does not relay the full picture of HF as a complex and heterogeneous syndrome, including both cardiovascular and non-cardiovascular factors implicated in its pathophysiology and prognosis [3,4,5,6,7,8]
We identified all individuals at least 18 years old with incident HF, defined as having at least one episode of acute HF that resulted in hospital admission within the study period (January 1, 2008, to January 1, 2019) or at least two outpatient claims on different dates within the study period, with any International Classification of Diseases, Ninth or Tenth Revision [ICD9, ICD10] HF code in any position on the claim
Summary
Comorbidities affect outcomes in heart failure (HF), but are not reflected in current HF classification. While clinical characteristics related to cardiac structure and function were generally available in such studies, allowing for detailed characterization, these data are frequently not available in population studies. This limits the possibility of replication across larger cohorts from administrative databases, where such variables are not recorded, as well as the generalizability of identified subgroups in a routine clinical setting. We hypothesized that there will be significant differences in both clinical and utilization outcomes between clusters as well as differential prescription rates of HF medication
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