Impact of timing of randomization after an acute coronary syndrome and subsequent events in patients with type 2 diabetes mellitus: an analysis of the EXAMINE trial
Bibliographic record
Abstract
Abstract Background The timing of enrolment following an acute coronary syndrome (ACS) may influence cardiovascular (CV) outcomes and potentially treatment effect in clinical trials. Using a large contemporary trial in patients with type 2 diabetes mellitus (T2DM) post-ACS, we examined the impact of timing of enrolment on subsequent CV outcomes. Methods EXAMINE was a randomized trial of alogliptin versus placebo in 5380 patients with T2DM and a recent ACS. The primary outcome was a composite of CV death, non-fatal myocardial infarction [MI], or non-fatal stroke. The median follow-up was 18 months. In this post hoc analysis, we examined the occurrence of subsequent CV events by timing of enrollment divided by tertiles of time from ACS to randomization: 8–34, 35–56, and 57–141 days. Results Patients randomized early (compared to the latest times) had less comorbidities at baseline including a history of heart failure (HF; 24.7% vs. 33.0%), prior coronary artery bypass graft (9.6% vs. 15.9%), or atrial fibrillation (5.9% vs. 9.4%). Despite the reduced comorbidity burden, the risk of the primary outcome was highest in patients randomized early compared to the latest time (adjusted hazard ratio [aHR] 1.47; 95% CI 1.21–1.74) (Figure 1). Similarly, patients randomized early had an increased risk of recurrent MI (aHR 1.51; 95% CI 1.17–1.96) and HF hospitalization (1.49; 95% CI 1.05–2.10). Conclusion In a contemporary cohort of T2DM with a recent ACS, early randomization following the ACS increases the risk of CV events including recurrent MI and HF hospitalization. This should be taken into account when designing future clinical trials. Figure 1 Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): Takeda Pharmaceutical
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".