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