Abstract 12626: Predictors of Initial Revascularization versus Medical Therapy 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 (NSTE-ACS), there is wide variation in the proportion of such patients who undergo revascularization despite early angiography. We sought to identify the predictors of early revascularization versus medical therapy alone in NSTE-ACS patients undergoing an invasive strategy and contrast their clinical outcomes. Methods: We assessed revascularization status (PCI or CABG within 7 days of the index angiogram) in all NSTE-ACS patients treated with an invasive strategy at 18 hospitals in Ontario, Canada from October 1st 2008 to October 31st, 2013, using the Cardiac Care Network registry. Follow-up was until December 31st, 2014. The primary outcome was death. Multivariable, hierarchical logistic models were used to identify predictors of revascularization. Multivariable Cox models with treatment strategy as a 3-level time-varying covariate were developed to understand the relationship between revascularization status and clinical outcomes. Results: We identified 50,302 NSTE-ACS patients of whom 34,288 (68.2%) underwent early revascularization (28,011 by PCI; 6,277 by CABG). There was a twofold variation in revascularization rates (Figure). High risk based on the TIMI/GRACE score was a significant predictor of revascularization (OR 1.26; 95% CI 1.18-1.35) as was having the angiogram by an interventional cardiologist (OR 1.76, 95% CI 1.57-1.98) or at a diagnostic only hospital (OR 1.22, 95% CI 1.01-1.48). Compared to patients treated medically, those who were revascularized with either PCI (HR 0.64, 95% CI 0.60-0.69) or CABG (HR 0.53, 95% CI 0.47-0.60) had improved survival. Conclusions: While more than 2/3rds of NSTE-ACS patients who underwent an early invasive approach did receive revascularization, there was wide variation. Those who underwent revascularization had significantly improved clinical outcomes.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".