Trans-stenotic pressure gradient as derived from CT improves patient management: ADVANCE registry
Bibliographic record
Abstract
Abstract Background The change in fractional flow reserve derived from CT (FFRCT) value across a coronary stenosis (ΔFFRCT) improves the physiological characterization of coronary artery disease (CAD). The role of ΔFFRCT in guiding risk-stratification and downstream testing in patients with stable CAD is unknown. Purpose To investigate the incremental value of ΔFFRCT at predicting early revascularization and improving efficacy of resource utilization. Methods Patients with CAD on CT coronary angiography (CTCA) were enrolled in an international multicenter registry. Patients with non-evaluable FFRCT analysis were excluded. The CTCA was assessed for: stenosis severity as per CAD-Reporting and Data System (CAD-RADS), lesion length and lesion-specific FFRCT measured 2 cm distal to stenosis. Risk factors and actual treatment (revascularization vs medical therapy) at 90-day follow-up were recorded. Multivariable logistic regression analysis for early revascularization was conducted. The incremental discrimination for revascularization prediction was compared among 3 models (model 1: risk factors + lesion length and location + CAD-RADS; model 2: model 1 + lesion-specific FFRCT; model 3: model 2 + ΔFFRCT). Simulating ICA referral for patients with CAD-RADS ≥3 and lesion-specific FFRCT ≤0.8, the potential impact of ΔFFRCT at reducing ICA referral and improving the ratio of subsequent revascularization was assessed. Results Of 4730 patients (66±10 years; 34% female), 2092 (42.7%) underwent ICA and 1168 (24.7%) underwent early revascularization. With increasing ΔFFRCT, a higher incidence of revascularization (Figure 1A) and an increase in the revascularization to ICA ratio was observed (Figure 1B). ΔFFRCT >0.13 was the optimal cut-off for predicting revascularization as determined by the Youden index. ΔFFRCT remained an independent predictor for early revascularization (odds ratio per 0.05 increase with 95% CI, 1.31 [1.26–1.35]; p<0.0001) after adjusting for risk factors, CAD-RADS, lesion length and location, and FFRCT. Among the 3 models, model 3, which included ΔFFRCT showed the highest AUC and improved discrimination power compared to model 2 (0.87 [0.86–0.88] vs 0.85 [0.84–0.86]; p<0.0001] (Figure 2), with the greatest incremental value for ΔFFRCT observed in patients with lesion-specific FFRCT between 0.71–0.80. In patients with CAD-RADS ≥3 and lesion-specific FFRCT ≤0.8, a diagnostic strategy incorporating ΔFFRCT >0.13 would potentially reduce ICA referral by 32.2% (1638 to 1110) and improve the revascularization to ICA ratio from 65.2% [1068/1638] to 73.1% [811/1110]. Conclusions The characterization of CAD with ΔFFRCT improves the identification of patients requiring early revascularization as compared to a standard diagnostic strategy of CTCA with FFRCT, particularly for those with lesion-specific FFRCT of 0.71–0.80. ΔFFRCT has the potential to aid decision making for ICA referral and improve the efficiency of resource utilization. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): HeartFlow, Inc., Redwood City, CA, USA ΔFFRCT and actual treatmentROC curve for early revascularization
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".