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Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
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Model-based comorbidity clusters in patients with heart failure: association with clinical outcomes and healthcare

Claudia Gulea1,2, Rosita Zakeri3,4, Jennifer K Quint5,6,4

  • 1Department of Population Health, National Heart and Lung Institute, Imperial College London, London, UK. c.gulea18@imperial.ac.uk.

BMC Medicine
|January 18, 2021
PubMed

Insights

Heart failure (HF) patient groups based on comorbidities show varied outcomes. The metabolic-vascular group faces the highest risk of hospitalization and death, highlighting the need for better comorbidity management.

Area of Science:

  • Cardiology
  • Medical Informatics
  • Public Health

Background:

  • Current heart failure (HF) classifications do not adequately reflect the impact of comorbidities on patient outcomes.
  • Understanding the complex interplay of comorbidities is crucial for improving HF management and prognosis.

Purpose of the Study:

  • To classify heart failure (HF) patients into distinct groups based on patterns of comorbidities.
  • To investigate the association between these identified comorbidity groups and key outcomes such as hospital admission and mortality.

Main Methods:

  • Latent class analysis (LCA) was applied to administrative claims data of 318,384 HF patients in the USA (2008-2018).
  • Twelve common comorbidities were analyzed to identify distinct patient clusters.
  • Cox regression and negative binomial regression were used to assess associations with hospital admission, mortality, and healthcare utilization.

Main Results:

  • Five distinct comorbidity clusters were identified: low-burden, metabolic-vascular, anemic, ischemic, and metabolic.
  • The metabolic-vascular group exhibited the highest risk for hospital admission (HR 2.21) and mortality (HR 1.87) compared to the low-burden group.
  • The anemic group showed increased outpatient visits (IRR 2.11), while metabolic-vascular and ischemic groups had higher admission rates and healthcare costs.

Conclusions:

  • Latent class analysis (LCA) is a feasible method for classifying heart failure (HF) patients based solely on their comorbidity profiles.
  • These findings support the development of multidimensional approaches to comorbidity management in HF.
  • Targeted management strategies for specific comorbidity clusters may reduce hospital admission and mortality risks in heart failure patients.
Abstract

Keywords:
ComorbidityHospitalizationMortalityResource use

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