Modern epidemiology of heart failure with reduced and preserved ejection fraction in population-wide linked electronic health records: a study of 208815 heart failure patients in England
Notice bibliographique
Résumé
Abstract Background The modern epidemiology of heart failure (HF) with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF) is yet to be studied on a population-wide scale in the United Kingdom, particularly in the post-COVID-19 era. Purpose To explore the post-pandemic epidemiology and outcomes of HFrEF and HFpEF in population-wide electronic health records (EHRs). Methods We accessed data in the National Health Service England’s Secure Data Environment for England via the British Heart Foundation Data Science Centre CVD-COVID-UK/COVID-IMPACT Consortium. We used EHRs for 57 million individuals to identify patients aged ≥18 years, of known sex, who were diagnosed with HF as the primary condition in any inpatient hospital stay from 01 Jan 2020 to 27 Feb 2023. By linking HF admissions with the National Heart Failure Audit, six other National Institute for Cardiovascular Outcomes Research audits, and the General Practice Extraction Service Data for Pandemic Planning and Research, we classified HF into HFrEF and HFpEF based on recorded left ventricular ejection fraction (LVEF; ≤40% and >40% respectively) or documented diagnosis. We compared characteristics of these groups and used Cox proportional hazards models, adjusted for key confounders, to examine differences in cause-specific mortality and hospitalisation outcomes. Results Over 3.2 years, we identified 208815 patients with a HF hospitalisation. 86110 (41%) patients had HFrEF, 65735 (31%) had HFpEF, and 56970 (27%) could not be categorised. Compared to patients with HFrEF, patients with HFpEF were older (mean [SD], 80.5 [10.9] vs 75.6 [13.6] years), more often female (n [%], 35990 [54.8] vs 30835 [35.8]), and less deprived (n [%] in the least-deprived Index of Multiple Deprivation 2019 quintile, 12275 [18.7] vs 15055 [17.5]; Table). Over a median follow-up of 11 months among patients with HFrEF and HFpEF, 99740 patients were re-hospitalised (66%) and 71075 (47%) died. Risks of all-cause, cardiovascular, non-cardiovascular, and heart-failure re-hospitalisations were all higher in patients with HFpEF compared with patients with HFrEF (Figure). The risk of all-cause mortality was modestly higher in patients with HFpEF (HR 1.04, 95% CI 1.02–1.06; Figure). Risks of death due to cardiovascular disease overall, fatal myocardial infarction, and fatal HF were higher among patients with HFrEF. In contrast, risks of death due to non-cardiovascular causes were higher in patients with HFpEF. There were no differences in risk of death due to COVID-19. Conclusions Unlike previous epidemiological surveys of HF, patients with HFpEF appear to face worse prognosis and risk-adjusted disease trajectories compared with those with HFrEF after hospitalisation in this population-wide study. Excess mortality in HFpEF appears driven by higher risk of non-cardiovascular mortality. However, post-discharge survival is poor regardless of LVEF and represents an ongoing target for quality improvement efforts.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».