Stroke in patients with heart failure and reduced or preserved ejection fraction
Bibliographic record
Abstract
AIMS: Stroke is an important problem in patients with heart failure (HF), but the intersection between the two conditions is poorly studied across the range of ejection fraction. The prevalence of history of stroke and related outcomes were investigated in patients with HF. METHODS AND RESULTS: Individual patient meta-analysis of seven clinical trials enrolling patients with HF with reduced (HFrEF) and preserved ejection fraction (HFpEF). Of the 20 159 patients with HFrEF, 1683 (8.3%) had a history of stroke, and of the 13 252 patients with HFpEF, 1287 (9.7%) had a history of stroke. Regardless of ejection fraction, patients with a history of stroke had more vascular comorbidity and worse HF. Among those with HFrEF, the incidence of the composite of cardiovascular death, HF hospitalization, stroke, or myocardial infarction was 18.23 (16.81-19.77) per 100 person-years in those with prior stroke vs. 13.12 (12.77-13.48) in those without [hazard ratio 1.37 (1.26-1.49), P < 0.001]. The corresponding rates in patients with HFpEF were 14.16 (12.96-15.48) and 9.37 (9.06-9.70) [hazard ratio 1.49 (1.36-1.64), P < 0.001]. Each component of the composite was more frequent in patients with stroke history, and the risk of future stroke was doubled in patients with prior stroke. Among patients with prior stroke, 30% with concomitant atrial fibrillation were not anticoagulated, and 29% with arterial disease were not taking statins; 17% with HFrEF and 38% with HFpEF had uncontrolled systolic blood pressure (≥140 mmHg). CONCLUSION: Heart failure patients with a history of stroke are at high risk of subsequent cardiovascular events, and targeting underutilization of guideline-recommended treatments might be a way to improve outcomes in this high-risk population.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".