Multi-vessel Coronary Function Testing increases diagnostic yield in INOCA
Bibliographic record
Abstract
Abstract Background Coronary vasomotor disorders (CVDs), comprising endotypes of coronary spasm and/or microcirculatory impairment, are common amongst patients with ischaemia and no obstructive coronary arteries (INOCA). Invasive coronary function testing (CFT) is the gold standard for diagnosing CVDs [1]. Most institutions recommend only testing the left coronary circulation focusing on the left anterior descending (LAD) artery as it typically subtends the largest myocardial territory [2]. Current practice is based on consensus, and it is unknown whether testing multiple coronary territories will increase diagnostic yield. Purpose To evaluate the diagnostic yield of multi-vessel, compared to single-vessel CFT in patients with INOCA. Methods Multi-vessel CFT was systematically performed in patients with suspected CVD. Vasoreactivity testing was performed through acetylcholine provocation in the left (20-200mcg) and right (20-80mcg) coronary artery. Incremental doses were manually injected via a guiding catheter over 20 seconds. A pressure-temperature sensor guidewire was used for coronary physiology assessment in all three epicardial vessels (figure 1). Microvascular or vasospastic angina was diagnosed according to previously published international consensus. Results A total of 50 patients (57.5+/-12.8years with 60% females) and 147 vessels were included from 2 tertiary referral institutions. Compared to single-vessel CFT, multi-vessel testing resulted in more patients diagnosed with coronary vasomotor dysfunction (80% vs 62%, p = <0.0001), vasospastic angina (50% vs 38%, p = 0.04) and microvascular angina (58% vs 38%, p = 0.004) (figure 2). Epicardial vasospasm (n=25) predominated in the left coronary system (n = 19), though isolated right coronary spasm was noted in 20.7% (n=6).Over half the patients (n=28) had coronary microvascular dysfunction (CMD), 50% had 1-vessel CMD, 35.7% had 2-vessel CMD, 14.3% had 3-vessel CMD. CMD was observed at a similar rate in the territories supplied by all three major coronary vessels (LAD = 36%, LCX = 28%, RCA = 28%, p = 0.648). Conclusion Multi-vessel CFT resulted in an increased diagnostic yield in patients with INOCA when compared to single-vessel testing. The results of this study suggest that multi-vessel CFT has a role in the management of patients with INOCA.Multi-vessel CFT algorithmDiagnostic yield of multi-vessel CFT
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".