van de Veerdonk, MC;
Savarese, G;
Handoko, ML;
Beulens, JWJ;
Asselbergs, F;
Uijl, A;
(2023)
Multimorbidity in Heart Failure: Leveraging Cluster Analysis to Guide Tailored Treatment Strategies.
Current Heart Failure Reports
10.1007/s11897-023-00626-w.
(In press).
Preview |
Text
Multimorbidity in Heart Failure.pdf - Accepted Version Download (798kB) | Preview |
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.
Type: | Article |
---|---|
Title: | Multimorbidity in Heart Failure: Leveraging Cluster Analysis to Guide Tailored Treatment Strategies |
Location: | United States |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1007/s11897-023-00626-w |
Publisher version: | https://doi.org/10.1007/s11897-023-00626-w |
Language: | English |
Additional information: | © 2023 Springer Nature. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Clustering, Heart failure, Machine learning, Phenotyping, Precision medicine, Treatment response |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology |
URI: | https://discovery.ucl.ac.uk/id/eprint/10178537 |
Archive Staff Only
View Item |