Persistent Beta‐Blocker Therapy Reduces Long‐Term Mortality in Patients With Acute Ischemic Stroke With Elevated Heart Rates
Bibliographic record
Abstract
BACKGROUND: Elevated heart rate in patients with acute ischemic stroke is associated with increased risk of mortality. Beta-blocker therapy is well known to reduce heart rate. METHODS AND RESULTS: This study was a post hoc analysis of patients with acute ischemic stroke with maximum heart rates ≥100 bpm. Beta-blocker use, assessed on the eighth day after the index stroke, was categorized as persistent or nonpersistent based on usage up to 39 months. The primary outcome was a composite of stroke recurrence, myocardial infarction, and mortality within the first year. Long-term mortality, a secondary outcome, was tracked for up to 10 years. Among 5049 patients (women, 38%; mean age, 68.5 years), 32.1% were prescribed beta blockers by the eighth day after stroke, and 99% had prior beta-blocker use. One-year cumulative incidences of the primary outcome, stroke recurrence, and death were 27.8%, 3.5%, and 25.8%, respectively. Persistent beta-blocker use was associated with a significant reduction in the primary outcome (adjusted hazard ratio [HR], 0.81 [95% CI, 0.68-0.97]) and mortality (adjusted HR, 0.80 [95% CI, 0.69-0.94]) from 2 months to 1 year. Extended analysis of mortality for up to 10 years showed long-term benefits of beta-blocker use. Analyses subdividing patients into persistent users, discontinuers, and never-users suggested higher early mortality risk among discontinuers and potential late survival benefits for persistent users. Subgroup analyses demonstrated greater benefits in patients <75 years, and those with atrial fibrillation, coronary heart disease, and higher mean heart rates. CONCLUSIONS: Our study shows that continuation of beta-blocker therapy in patients with acute ischemic stroke with tachycardia significantly reduces long-term 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".