Second-Line Myocardial Perfusion Imaging to Detect Obstructive Stenosis
Bibliographic record
Abstract
BACKGROUND: Guidelines recommend verification of myocardial ischemia by selective second-line myocardial perfusion imaging (MPI) following a coronary computed tomography angiography (CTA) with suspected obstructive coronary artery disease (CAD). Head-to-head data on the diagnostic performance of different MPI modalities in this setting are sparse. OBJECTIVES: rubidium positron emission tomography (RbPET) in patients with suspected obstructive stenosis at coronary CTA using invasive coronary angiography (ICA) with fractional flow reserve (FFR) as reference. METHODS: Consecutive patients (n = 1,732, mean age: 59.1 ± 9.5 years, 57.2% men) referred for coronary CTA with symptoms suggestive of obstructive CAD were included. Patients with suspected stenosis were referred for both CMR and RbPET and subsequently ICA. Obstructive CAD was defined as FFR ≤0.80 or >90% diameter stenosis by visual assessment. RESULTS: In total, 445 patients had suspected stenosis on coronary CTA. Of these, 372 patients completed both CMR, RbPET and subsequent ICA with FFR. Hemodynamically obstructive CAD was identified in 164 of 372 (44.1%) patients. Sensitivities for CMR and RbPET were 59% (95% CI: 51%-67%) and 64% (95% CI: 56%-71%); P = 0.21, respectively, and specificities 84% (95% CI: 78%-89%) and 89% (95% CI: 84%-93%]); P = 0.08, respectively. Overall accuracy was higher for RbPET compared with CMR (73% vs 78%; P = 0.03). CONCLUSIONS: In patients with suspected obstructive stenosis at coronary CTA, CMR, and RbPET show similar and moderate sensitivities but high specificities compared with ICA with FFR. This patient group represents a diagnostic challenge with frequent mismatch between advanced MPI tests and invasive measurements. (Danish Study of Non-Invasive Diagnostic Testing in Coronary Artery Disease 2 [Dan-NICAD 2]; NCT03481712).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".