Abstract 9: Clinical Effectiveness of Statin Therapy after Ischemic Stroke: Primary Results from the PROSPER Study
Bibliographic record
Abstract
Background: Evidence for statin use comes primarily from select clinical trial populations that are often younger without comorbidities. Stroke patients and their caregivers are in need of real-world effectiveness data to better inform decision-making on statin use after stroke. Methods: PROSPER is a PCORI-funded research program designed with stroke survivors to evaluate the effectiveness of therapies post-stroke. We linked data from Get With The Guidelines-Stroke patients >65 years of age to Medicare claims to capture post-discharge outcomes. Primary outcomes prioritized by patients were: 1) Home time (days alive and out of acute or post-acute care) and 2) Major adverse cardiovascular events (MACE). Secondary outcomes included all-cause mortality, all-cause readmission, CV readmission, and hemorrhagic stroke. We used negative binomial and Cox models to evaluate discharge statins and outcomes with inverse probability weighting (IPW) to adjust for baseline differences by treatment group. Results: Of 77,468 statin-naïve ischemic stroke patients hospitalized from 2007-2011, n=54,991 (71%) were discharged on statin therapy. Compared with those not receiving a statin, patients receiving a statin were younger and more likely to be smokers. Unadjusted rates of MACE, mortality and CV readmission within 2 years were lower for statin patients compared with those not receiving a statin. After IPW adjustment, statin therapy was associated with 28 more days of home time in the 2-year post-discharge period (P <.001), 9% lower hazard of MACE (P <.001), 16% lower hazard of mortality (P <.001), and 7% lower hazard of readmission (P <.001). Statin use was not associated with increased risk of hemorrhagic stroke (P=0.56). Conclusions: In a real-world population of older statin-naïve ischemic stroke patients, discharge statin therapy was associated with more days spent at home during the 2-year period after hospitalization and lower risk of both MACE and all-cause 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 | 0.027 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".