Multimorbidity in Heart Failure: Leveraging Cluster Analysis to Guide Tailored Treatment Strategies

Mariëlle C van de Veerdonk, Gianluigi Savarese, M Louis Handoko, Joline W J Beulens, Folkert Asselbergs, Alicia Uijl*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

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Abstract

Review Purpose: This review summarises key findings on treatment effects within phenotypical clusters of patients with heart failure (HF), making a distinction between patients with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF). Findings: Treatment response differed among clusters; ACE inhibitors were beneficial in all HFrEF phenotypes, while only some studies show similar beneficial prognostic effects in HFpEF patients. Beta-blockers had favourable effects in all HFrEF patients but not in HFpEF phenotypes and tended to worsen prognosis in older, cardiorenal patients. Mineralocorticoid receptor antagonists had more favourable prognostic effects in young, obese males and metabolic HFpEF patients. While a phenotype-guided approach is a promising solution for individualised treatment strategies, there are several aspects that still require improvements before such an approach could be implemented in clinical practice. Summary: Stronger evidence from clinical trials and real-world data may assist in establishing a phenotype-guided treatment approach for patient with HF in the future.

Original languageEnglish
Pages (from-to)461-470
Number of pages10
JournalCurrent Heart Failure Reports
Volume20
Issue number5
DOIs
Publication statusPublished - Oct 2023

Keywords

  • Clustering
  • Heart failure
  • Machine learning
  • Phenotyping
  • Precision medicine
  • Treatment response

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