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Hierarchical management of chronic heart failure: a perspective based on the latent structure of comorbidities

Chu Zheng1, Linai Han2, Jing Tian2,3

  • 1Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.

ESC Heart Failure
|November 15, 2021
PubMed

Insights

Identifying distinct patient clusters in chronic heart failure (CHF) based on comorbidities is crucial. The high comorbidity burden cluster faces the highest mortality and readmission risks, highlighting the need for personalized management strategies.

Area of Science:

  • Cardiology and Geriatrics
  • Health Services Research
  • Biostatistics

Background:

  • Chronic heart failure (CHF) is increasingly associated with multiple comorbidities, complicating patient management and affecting clinical outcomes.
  • Existing research has limited focus on the clustering and hierarchical management of CHF patients according to their comorbidity profiles.
  • Understanding comorbidity patterns is essential for tailoring treatment and improving prognosis in CHF patients.

Purpose of the Study:

  • To identify distinct patient clusters within chronic heart failure (CHF) based on comorbidity patterns using latent class analysis.
  • To investigate the relationship between these identified comorbidity clusters and key clinical outcomes, including mortality and hospital readmission.
  • To provide evidence supporting a hierarchical management approach for CHF patients tailored to their specific comorbidity burden.

Main Methods:

  • Latent class analysis was applied to electronic health records of 4063 CHF patients hospitalized between January 2014 and April 2019, incorporating 12 common comorbidities.
  • Comorbidity networks were visualized using the Fruchterman-Reingold layout, with weighted degrees compared via analysis of variance.
  • Clinical outcomes were assessed using Kaplan-Meier curves, log-rank tests, and Cox proportional hazard models, with sensitivity analyses based on left ventricular ejection fraction.

Main Results:

  • Four distinct CHF patient clusters were identified: metabolic, ischaemic, high comorbidity burden, and elderly-atrial fibrillation.
  • The high comorbidity burden cluster exhibited the highest adjusted risks for combined outcomes (HR 1.67) and all-cause mortality (HR 2.87) compared to the metabolic cluster.
  • Patients in the high comorbidity burden, elderly-atrial fibrillation, and ischaemic clusters showed significantly increased adjusted readmission risks (HRs ranging from 1.35 to 1.42).

Conclusions:

  • The identified clusters and their differential adverse outcomes underscore the heterogeneity of chronic heart failure (CHF) and the necessity of hierarchical management strategies.
  • This study provides a data-driven foundation for personalized treatment and management plans for CHF patients, optimizing clinical decision-making.
  • The findings offer a novel perspective on stratifying CHF patients by comorbidity profiles to better predict and mitigate adverse clinical events.
Abstract

Keywords:
Chronic heart failureComorbidityLCAMortalityReadmission

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