Development and validation of the fractional flow reserve (FFR) angiographic scoring tool (FAST) to improve the angiographic grading and selection of intermediate lesions that require FFR assessment
Bibliographic record
Abstract
BACKGROUND: Visual angiographic assessment of intermediate coronary lesions is poor at determining the functional significance. We sought to identify independent clinical and angiographic parameters associated with stenosis functional significance and applied them in a weighted fractional flow reserve angiographic scoring tool (FAST) to improve intermediate lesion selection for fractional flow reserve (FFR) assessment. METHODS AND RESULTS: Data from 100 patients with intermediate lesions previously assessed by FFR were retrospectively analyzed, and four independent variables that predicted FFR of less than or equal to 0.8 were identified: quantitative coronary angiography percent diameter stenosis [odds ratio (OR) 1.22, P<0.001], length more than 20 mm (OR 7.6, P=0.004), stenosis haziness (OR 16.6, P=0.005), and multivessel disease (OR 7.8, P=0.019). Applying these variables into the FAST score, we prospectively assessed a further 109 intermediate lesions (prevalence of FFR ≤0.8 was 29% in this validation cohort) and found that FAST was highly discriminative, predicting an FFR of less than or equal to 0.8 with a c-statistic of 0.865 (95% confidence interval 0.793-0.937, P<0.0001). At the optimal cutoff value, FAST score of more than 2 had a negative predictive value of 96.5% and a sensitivity of 93.8%. It would have reduced the pressure wire usage in the validation cohort by 52.3% (57 out of 109 cases), with only two false negatives and associated cost savings. CONCLUSION: The FAST score is a simple angiographic assessment tool for intermediate lesions that comprises four angiographic variables. A score of 2 or lower indicates low likelihood of lesion hemodynamic significance.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".