Predictors of Initial Revascularization Versus Medical Therapy Alone in Patients With Non–ST-Segment–Elevation Acute Coronary Syndrome Undergoing an Invasive Strategy
Bibliographic record
Abstract
BACKGROUND: Although an invasive strategy is a class I clinical practice guideline for non-ST-segment-elevation acute coronary syndromes, there is wide variation in the proportion of patients who undergo revascularization despite early angiography. We sought to identify the predictors of early revascularization versus medical therapy alone in patients with non-ST-segment-elevation acute coronary syndrome undergoing an invasive strategy and to assess their clinical outcomes. METHODS AND RESULTS: We assessed revascularization status by percutaneous coronary intervention or coronary artery bypass grafting within 7 days of the index angiogram in all patients with non-ST-segment-elevation acute coronary syndrome who underwent an invasive strategy in Ontario, Canada, from October 1, 2008, to October 31, 2013, with follow-up through December 31, 2014. The primary outcome was mortality. Multivariable hierarchical logistic models identified predictors of revascularization, and multivariable Cox models with treatment strategy as a 3-level time-varying covariate assessed the relationship between revascularization status and clinical outcomes. We identified 50 302 patients of whom 34 288 (68.2%) underwent revascularization (percutaneous coronary intervention: 28 011 and coronary artery bypass grafting: 6277). There was a 2-fold variation in revascularization rates across hospitals. A higher risk presentation significantly predicted revascularization (odds ratio, 1.26; 95% confidence interval, 1.18-1.35), as did having the angiogram by an interventional cardiologist (odds ratio, 1.76; 95% confidence interval, 1.57-1.98). Revascularized patients with either percutaneous coronary intervention (hazard ratio, 0.64; 95% confidence interval, 0.60-0.69) or coronary artery bypass grafting (hazard ratio, 0.53; 95% confidence interval, 0.47-0.60) had improved survival compared with medically treated patients. CONCLUSIONS: Although the majority of patients with non-ST-segment-elevation acute coronary syndrome who underwent an early invasive approach received revascularization, there was wide variation. Revascularization was associated with significantly improved survival.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".