Trans-lesional fractional flow reserve gradient as derived from coronary CT improves patient management: ADVANCE registry
Bibliographic record
Abstract
Background The role of change in fractional flow reserve derived from CT (FFR CT ) across coronary stenoses (ΔFFR CT ) in guiding downstream testing in patients with stable coronary artery disease (CAD) is unknown. Objectives To investigate the incremental value of ΔFFR CT in predicting early revascularization and improving efficiency of catheter laboratory utilization. Materials Patients with CAD on coronary CT angiography (CCTA) were enrolled in an international multicenter registry. Stenosis severity was assessed as per CAD-Reporting and Data System (CAD-RADS), and lesion-specific FFR CT was measured 2 cm distal to stenosis. ΔFFR CT was manually measured as the difference of FFR CT across visible stenosis. Results Of 4730 patients (66 ± 10 years; 34% female), 42.7% underwent ICA and 24.7% underwent early revascularization. ΔFFR CT remained an independent predictor for early revascularization (odds ratio per 0.05 increase [95% confidence interval], 1.31 [1.26–1.35]; p < 0.001) after adjusting for risk factors, stenosis features, and lesion-specific FFR CT . Among the 3 models ( model 1 : risk factors + stenosis type and location + CAD-RADS; model 2 : model 1 + FFR CT ; model 3 : model 2 + ΔFFR CT ), model 3 improved discrimination compared to model 2 (area under the curve, 0.87 [0.86–0.88] vs 0.85 [0.84–0.86]; p < 0.001), with the greatest incremental value for FFR CT 0.71–0.80. ΔFFR CT of 0.13 was the optimal cut-off as determined by the Youden index. In patients with CAD-RADS ≥3 and lesion-specific FFR CT ≤0.8, a diagnostic strategy incorporating ΔFFR CT >0.13, would potentially reduce ICA by 32.2% (1638–1110, p < 0.001) and improve the revascularization to ICA ratio from 65.2% to 73.1%. Conclusions ΔFFR CT improves the discrimination of patients who underwent early revascularization compared to a standard diagnostic strategy of CCTA with FFR CT , particularly for those with FFR CT 0.71–0.80. ΔFFR CT has the potential to aid decision-making for ICA referral and improve efficiency of catheter laboratory utilization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".