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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".