Coronary microcirculation assessment using functional angiography: Development of a wire‐free method applicable to conventional coronary angiograms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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 | 0.000 |
| 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 it