Coronary microcirculation assessment using functional angiography: Development of a wire‐free method applicable to conventional coronary angiograms
Bibliographic record
Abstract
OBJECTIVES: We aimed to develop a novel wire- and adenosine-free microcirculatory resistive index from functional angiography (angio-IMR) to estimate coronary microcirculatory resistance, and to investigate how this method can improve clinical interpretation of physiological stenosis assessment with quantitative flow ratio (QFR). BACKGROUND: Hyperemic index of coronary microcirculatory resistance (IMR) is a widely used tool to assess microcirculatory dysfunction. However, the need of dedicated intracoronary wire and hyperemia limits its adoption in clinical practice. METHODS: We performed our study in two separate stages: (1) development of a formula (angio-IMR) to estimate IMR from resting angiograms and aortic pressure (Pa), and (2) validation of the method in a clinical population using invasively measured IMR as reference. Additionally, QFR diagnostic performance was assessed considering angio-IMR values. RESULTS: We developed the formula: angio-IMR = (Pa-[0.1*Pa])*QFR*e-Tmn (where e-Tmn is an estimation of hyperaemic mean transit time) and validated it in 115 vessels (104 patients). Angio-IMR correlated well with IMR (Spearman's rho = 0.70, p < 0.001). Sensitivity, specificity, positive and negative predictive value, accuracy and area under the curve of angio-IMR to predict IMR were 87.5% (73.2-95.8), 85.3% (75.3-92.4), 76.1% (64.5-84.8), 92.8% (84.9-96.7), 85% and 0.90 (0.83-0.95), respectively. False positive QFR measurements decreased from 19.5% to 8.5% when angio-IMR was incorporated into the QFR interpretation workflow. CONCLUSIONS: Estimation of IMR without physiology wire and adenosine is feasible. Coronary microcirculatory dysfunction causing high IMR can be ruled-out with high confidence in vessels with low angio-IMR. Awareness of angio-IMR contributes to a better clinical interpretation of functional stenosis assessment with QFR.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".