Geographical Variation in Patient Characteristics and Outcomes in Heart Failure with Mildly Reduced and Preserved Ejection Fraction
Bibliographic record
Abstract
AIMS: Compared to heart failure (HF) with reduced ejection fraction, HF with preserved ejection fraction (HFpEF), and HF with mildly reduced ejection fraction (HFmrEF) are increasing in prevalence, yet little is known about the geographic variation in patient characteristics, treatments and outcomes among these two HF phenotypes. The aim of this study was to investigate geographic differences in HFpEF and HFmrEF. METHODS AND RESULTS: We conducted an individual patient analysis of five clinical trials enrolling patients with HFpEF or HFmrEF from North America (NA), Latin America (LA), Western Europe (WE), Central/Eastern Europe and Russia (CEER), and Asia-Pacific (AP). We compared regions using descriptive statistics and multivariable regression models. Among the 19 959 patients included, 4066 (23.1%) had HFmrEF and 15 353 (76.9%) HFpEF. Regardless of HF phenotype, patients from WE were oldest, and those in CEER youngest. LA had the largest portion of females and NA most black patients. Obesity and diabetes were most prevalent in NA and hypertension and coronary heart disease most common in CEER. Self-reported health status varied strikingly and was the worst in NA and best in AP. Among patients with HFmrEF, rates of the primary composite endpoint (cardiovascular death or HF hospitalization) were: NA 12.56 per 100 patient-years (/100py), AP 11.67/100py, CEER 10.12/100py, LA 8.90/100py, and WE 8.43/100py, driven by differences in the rate of HF hospitalization. The corresponding values in HFpEF were 11.47/100py, 7.80/100py, 5.47/100py, 5.92/100py, and 7.80/100py, respectively. CONCLUSIONS: There is substantial geographic variation in patient characteristics, treatment and outcomes among patients with HFpEF and HFmrEF. These findings have implications for interpretation and generalizability of trial results, design and conduct of future trials, and optimization of care for these patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".