Utility of Noninvasive Testing Before Invasive Coronary Angiography in the Assessment for Revascularization
Bibliographic record
Abstract
Objective: To examine the role of noninvasive testing (NIT) before invasive coronary angiography (ICA) by evaluating the association between a positive myocardial perfusion imaging (MPI) or computed tomography angiography (CTA) result and the decision to perform coronary revascularization. Patients and Methods: We screened all patients who received ICA between August 1, 2015, and July 31, 2019, and identified those who received MPI or CTA within the preceding 12 months. We considered MPI to be a positive result if it found moderate or severe ischemia in a specific coronary territory and CTA to be a positive result if it identified a stenosis greater than 50% in any major coronary artery. Results: Of the 17,181 individual procedures, 2183 were included. Positive CTA had an odds ratio (OR) of 2.68 (95% CI, 1.82-3.94) for revascularization and positive MPI an OR of 1.29 (95% CI, 1.07-1.56). Overall sensitivity for CTA in the prediction of revascularization was 80.4% (95% CI, 75.7%-84.6%), with vessel-level sensitivity ranging from 57.3% (95% CI, 47.5%-66.7%) to 71.8% (95% CI, 65.8%-77.4%). Overall sensitivity of MPI was 48.2% (95% CI, 44.7%-51.7%), with territory-specific sensitivity ranging from 33.7% (95% CI, 29.9%-37.7%) to 36.5% (95% CI, 32.6%-40.6%). Overall specificity for CTA was low, at 39.5% (32.9%-46.3%), but higher when evaluating at the vessel level, ranging from 60.3% (95% CI, 54.5%-66.0%) to 83.5% (95% CI, 79.6%-86.9%). Overall specificity for MPI was 58.1% (95% CI, 54.9%-61.3%), with territory-specific specificity ranging from 78.6% (95% CI, 76.1%-80.9%) to 78.9% (95% CI, 76.5%-81.3%). Conclusion: In this population of patients referred for ICA, positive CTA was more closely associated with revascularization than MPI. Further studies are necessary to determine the role of NIT before ICA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".