Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Gulea C

PROVIDER: S-EPMC7812726 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

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

Gulea Claudia C Zakeri Rosita R Quint Jennifer K JK

BMC medicine 20210118 1

<h4>Background</h4>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.<h4>Methods</h4>Latent class analysis (LCA) was performed on 12 comorbidities from patients with HF identified from administrative claims data in the USA (OptumLabs Data Warehouse®) bet ...[more]

PMID: 33455580

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Project description:AimsThe aims of this study were to describe patient characteristics of the adult chronic heart failure (HF) population and to estimate the prevalence, incidence, healthcare resource utilization (HCRU), and mortality associated with HF in Southwest Finland.Methods and resultsThis was a retrospective biobank and clinical registry study. Adult patients with an HF diagnosis (International Statistical Classification of Diseases and Related Health Problems (ICD) code I50) during 2004-2013 in secondary care were included in the study and compared with age-matched and gender-matched control patients without an I50 diagnosis. HF patients were stratified in groups by left ventricular ejection fraction (LVEF) as follows: LVEF < 40% [HF with reduced ejection fraction (HFrEF)]; LVEF ? 40% [HF with preserved ejection fraction (HFpEF)]; or unknown (LVEF unknown). HCRU was stratified by inpatient, outpatient, and emergency room visits. In 2013, the incidence of HF was 3.2/1000, and the prevalence was 13.9/1000 inhabitants (n = 15 594). In the stratified analysis of HF patients (n = 8833, average ± SD age 77.1 ± 11.2), 1115 (12.6%) patients had HFrEF (female 31.3%), 1449 (16.4%) had HFpEF (female 50.9%), and 6269 (71%) had unknown LVEF (female 52.1%). The most common co-morbidities were essential hypertension (58%), chronic elevated serum creatinine (57.3%), atrial fibrillation and flutter (55.1%), and chronic ischaemic heart disease (46.4%). Patients with HF diagnosis had higher HCRU compared with that of age-matched and gender-matched controls (3.7 more days per year at the hospital for HF patients compared with the controls). The total 5 year mortality was 62.6% for HF patients and 28.3% for controls, with higher age being the strongest predictor of mortality. Moreover, multivariable Cox regression analysis showed that patients with HFrEF had a 13% (95% confidence interval 2.7-25%) increased risk of mortality compared with HFpEF patients.ConclusionsThe high mortality rate and HCRU among the studied HF patients highlight the severity of the disease and the economic and social burden on both patients and society. This calls for improved methods of care for this large patient population.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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