Prevalence, outcomes and costs of a contemporary, multinational population with heart failure
Bibliographic record
Abstract
OBJECTIVE: Digital healthcare systems could provide insights into the global prevalence of heart failure (HF). We designed the CardioRenal and Metabolic disease (CaReMe) HF study to estimate the prevalence, key clinical adverse outcomes and costs of HF across 11 countries. METHODS: Individual level data from a contemporary cohort of 6 29 624 patients with diagnosed HF was obtained from digital healthcare systems in participating countries using a prespecified, common study plan, and summarised using a random effects meta-analysis. A broad definition of HF (any registered HF diagnosis) and a strict definition (history of hospitalisation for HF) were used. Event rates were reported per 100 patient years. Cumulative hospital care costs per patient were calculated for a period of up to 5 years. RESULTS: The prevalence of HF was 2.01% (95% CI 1.65 to 2.36) and 1.05% (0.85 to 1.25) according to the broad and strict definitions, respectively. In patients with HF (broad definition), mean age was 75.2 years (95% CI 74.0 to 76.4), 48.8% (40.9-56.8%) had ischaemic heart disease and 34.5% (29.4-39.6%) had diabetes. In 51 442 patients with a recorded ejection fraction (EF), 39.1% (30.3-47.8%) had a reduced, 18.8% (13.5-24.0%) had a mildly reduced and 42.1% (31.5-52.8%) had a preserved left ventricular EF. In 1 69 518 patients with recorded estimated glomerular filtration rate, 49% had chronic kidney disease (CKD) stages III-V. Event rates were highest for cardiorenal disease (HF or CKD) and all cause mortality (19.3 (95% CI 11.3 to 27.1) and 13.1 (11.1 to 15.1), respectively), and lower for myocardial infarction, stroke and peripheral artery disease. Hospital care costs were highest for cardiorenal diseases. CONCLUSIONS: We estimate that 1-2% of the contemporary adult population has HF. These individuals are at significant risk of adverse outcomes and associated costs, predominantly driven by hospitalisations for HF or CKD. There is considerable public health potential in understanding the contemporary burden of HF and the importance of optimising its management.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".