Aller au contenu principal
2025 article

Machine-learning informed subtypes of heart failure and their palliative care trajectories: a population-based cohort study

0Citations signalées — pas une note de qualité
7Institutions déclarées
4Pays d’affiliation déclarés

Résumé fourni par la source

Abstract Introduction Heart failure (HF) significantly contributes to global morbidity and mortality, yet the provision of palliative care (PC) remains suboptimal in this population. Despite its anticipated benefits in symptom management and quality of life, the patterns of PC engagement across diverse HF subgroups are not well-defined. This study aims to identify HF subtypes and evaluate their PC trajectories to inform targeted strategies for improving PC access and delivery. Methods Using linked electronic health records from the Clinical Practice Research Datalink (CPRD) in England, we identified individuals aged ≥18 years with incident HF who died during follow-up. A previously developed machine learning-informed analytical framework was applied to define distinct HF subgroups based on demographic characteristics, biomarkers, hospitalizations, and comorbidities. Kaplan-Meier (KM) estimates were used to assess survival and PC trajectories across these subgroups. Results Among 51,332 individuals with incident HF, 15,376 (30%) received PC. Five distinct HF subgroups were identified: Cancer-Associated (9.3%), Cardiometabolic (12.7%), Metabolic (42.9%), AF-Associated (5.9%), and Late-Onset (29.2%). The median time to PC initiation (in those receiving it) following the diagnosis of HF was 4.3 years (IQR: 1.8–8.5). The Cancer-Associated subtype received PC earliest (median 2.6 years post-HF), while the Late-Onset subtype had the latest initiation (2.9 years) and the shortest median PC duration before death (0.35 years). Approximately 30% of patients had limited PC access. KM analysis demonstrated significant differences in PC receipt across these subtypes (p < 0.0001). Interpretation: Despite the high symptom burden and end-of-life needs in HF, receipt of PC remains limited, particularly among ‘late-onset’ HF patients. These findings underscore the inequity of PC provision even for high-risk groups in whom the early integration of wholistic care might benefit quality of life.Heart failure/palliative care subtypes Percentage of subtypes of HF/PC

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.

Contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
Machine-learning informed subtypes of heart failure and their palliative care trajectories: a population-based cohort study
Date Crossref
01/11/2025
Éditeur
Oxford University Press (OUP)
Type
journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.

Institutions déclarées

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Sujets associés

Heart Failure Treatment and ManagementCardiovascular Function and Risk FactorsCardiac Fibrosis and Remodeling

BNTIC News n’est pas le producteur de ces données. Recherche à la demande dans Crossref et Europe PMC, sans clé ; OpenAlex reste optionnel. Aucun service payant requis, aucune réponse conservée. Sources et limites.