Regional differences in precipitating factors of hospitalization for acute heart failure: insights from the <scp>REPORT‐HF</scp> registry
Bibliographic record
Abstract
Aims Few prior studies have investigated differences in precipitants leading to hospitalizations for acute heart failure (AHF) in a cohort with global representation. Methods and results We analysed the prevalence of precipitants and their association with outcomes in 18 553 patients hospitalized for AHF in REPORT‐HF (prospective international REgistry to assess medical Practice with lOngitudinal obseRvation for Treatment of Heart Failure) according to left ventricular ejection fraction subtype (reduced [HFrEF] and preserved ejection fraction [HFpEF]) and presentation (new‐onset vs. decompensated chronic heart failure [DCHF]). Patients were enrolled from 358 centres in 44 countries stratified according to Latin America, North America, Western Europe, Eastern Europe, Eastern Mediterranean and Africa, Southeast Asia, and Western Pacific. Precipitants were pre‐with mutually exclusive categories and selected according to the local investigator's discretion. Outcomes included in‐hospital and 1‐year mortality. The median age was 67 (interquartile range 57–77) years, and 39% were women. Acute coronary syndrome (ACS) was the most common precipitant in patients with new‐onset heart failure in all regions except for North America and Western Europe, where uncontrolled hypertension and arrhythmia, respectively, were the most common precipitants, independent of confounders. In patients with DCHF, non‐adherence to diet/medication was the most common precipitant regardless of region. Uncontrolled hypertension was a more likely precipitant in HFpEF, non‐adherence to diet/medication, and ACS were more likely precipitants in HFrEF. Patients admitted due to worsening renal function had the worst in‐hospital (5%) and 1‐year post‐discharge (30%) mortality rates, regardless of region, heart failure subtype and admission type ( p interaction >0.05 for all). Conclusion Data on global differences in precipitants for AHF highlight potential regional differences in targets for preventing hospitalization for AHF and identifying those at highest risk for early mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".