History of stroke in patients with heart failure: prevalence, baseline characteristics and clinical outcomes
Bibliographic record
Abstract
Abstract Background Stroke is an important but neglected comorbidity in patients with heart failure (HF). Little is known about the characteristics and outcomes of HF patients with a history of stroke. Purpose To examine the prevalence of prior stroke in patients with HF and reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), the clinical characteristics of patients with a history of stroke, and the clinical outcomes in patients with prior stroke compared to those without. Methods Individual patient data analysis using three recent HFrEF trials (ATMOSPHERE, PARADIGM-HF, and DAPA-HF) and HFpEF trials (CHARM-Preserved, I-Preserve, TOPCAT-Americas, and PARAGON-HF). Cox regression was used to analyze clinical outcomes. Results Among 20159 HFrEF patients enrolled, 1683 (8.3%) had a history of stroke and among the 13252 patients with HFpEF 1287 (9.7%) had a prior stroke. Compared to patients without stroke, those with stroke were slightly older and more likely to have a history of hypertension, myocardial infarction, atrial fibrillation, diabetes, carotid artery disease, and peripheral artery disease (for both HFrEF and HFpEF). Patients with a history of stroke had worse NYHA class and KCCQ scores, and a higher rate of fatigue; they also had a higher median NT-proBNP level and lower eGFR than those without prior stroke (whether HFrEF or HFpEF). Systolic BP, pulse pressure and LVEF did not differ susbtantialy between patients with and without a history of stroke. The table shows outcomes according to history of stroke or not, stratified by LVEF phenotype. During follow-up, all fatal and non-fatal outcomes were significantly more common in patients with a history of stroke. The augmentation of risk tended to be greater in patients with HFpEF than HFrEF, but was not statistically different. Conclusion Approximately 1 in 11 patients in recent HF trials had a history of stroke and these patients were at higher risk of fatal and non-fatal events than those without prior stroke. HF hospitalization as well as atherothrombotic events (myocardial infarction and stroke) were more common among patients with prior stroke – patients with prior stroke had at least 30% higher risk of all events examined, regardless of LVEF, and more than double incidence of repeat stroke. Funding Acknowledgement Type of funding sources: Other.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".