Cardiovascular Events and Long‐Term Risk of Sudden Death Among Stabilized Patients After Acute Coronary Syndrome: Insights From IMPROVE‐IT
Bibliographic record
Abstract
Background Unlike patients with low ejection fraction after an acute coronary syndrome (ACS), little is known about the long-term incidence and influence of cardiovascular events before sudden death among stabilized patients after ACS. Methods and Results A total of 18 144 patients stabilized within 10 days after ACS in IMPROVE-IT (Improved Reduction of Outcomes: Vytorin Efficacy International Trial) were studied. Cumulative incidence rates (IRs) and IRs per 100 patient-years of sudden death were calculated. Using Cox proportional hazards, the association of ≥1 additional postrandomization cardiovascular events (myocardial infarction, stroke, and hospitalization for unstable angina or heart failure) with sudden death was examined. Early (≤1 year after ACS) and late sudden deaths (>1 year) were compared. Of 2446 total deaths, 402 (16%) were sudden. The median time to sudden death was 2.7 years, with 109 early and 293 late sudden deaths. The cumulative IR was 2.47% (95% CI, 2.23%-2.73%) at 7 years of follow-up. The risk of sudden death following a postrandomization cardiovascular event (150/402 [37%] sudden deaths; median 1.4 years) was greater (IR/100 patient-years, 1.45 [95% CI, 1.23-1.69]) than the risk with no postrandomization cardiovascular event (IR/100 patient-years, 0.27 [95% CI, 0.24-0.30]). Postrandomization myocardial infarction (hazard ratio [HR], 3.64 [95% CI, 2.85-4.66]) and heart failure (HR, 4.55 [95% CI, 3.33-6.22]) significantly increased future risk of sudden death. Conclusions Patients stabilized within 10 days of an ACS remain at long-term risk of sudden death with the greatest risk in those with an additional cardiovascular event. These results refine the long-term risk and risk effectors of sudden death, which may help clinicians identify opportunities to improve care. Registration: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00202878.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".