Abstract 19526: Fractional Flow Reserved Derived from Computed Tomographic Angiography (FFRCT) to Discriminate Individuals with versus without Ischemia: Results from the DeFACTO Trial (Determination of Fractional Flow Reserve by Anatomic Computed TOmographic Angiography)
Bibliographic record
Abstract
Background Non-invasive fractional flow reserve (FFR) derived from coronary computed tomographic (CT) angiography (FFRCT) is a novel method that employs computational fluid dynamics to diagnose coronary lesions that cause ischemia. To date, the per-patient diagnostic performance of FFRCT versus obstructive CAD by CT (>50% stenosis) to discriminate ischemia has been inadequately studied. Methods A total of 407 vessels were evaluated in 252 patients from 17 centers in 4 countries who underwent CT, FFRCT, invasive coronary angiography and invasive FFR. The area under the receiver operator characteristics curve (AUC) for detecting abnormal FFR on a per-patient basis was examined considering: (1) all vessels interrogated by FFR (n=252 patients), (2) only vessels >2 mm interrogated by FFR (n=149 patients); 3) a decision rule which included all vessels interrogated by FFR, as well as vessels with maximal stenosis 90% which were considered negative and positive, respectively, for ischemia (n=252 patients); and 4) a decision rule for >2 mm vessels (n=229 patients). Per-patient AUCs were compared for FFRCT alone, CT stenosis >50% alone, and the combination of FFRCT and CT stenosis >50%. FFRCT was superior to CT stenosis for discrimination of individuals with ischemia (AUC 0.81 vs. 0.69, p2 mm vessels (AUC 0.82 vs. 0.74, p=0.0007), when employing the decision rule (0.83 vs. 0.67, p2 mm vessels (0.92 vs. 0.68, p<0.0001). The combination of FFRCT and CT stenosis was similarly superior to CT stenosis alone when considering all vessels interrogated by FFR (0.81 vs. 0.69, p2 mm vessels (0.82 vs. 0.74, p<0.001), when applying the decision rule (0.83 vs. 0.67, p2 mm vessels (AUC 0.91 vs. 0.68, p<0.0001). In contrast, no added discrimination was noted when CT stenosis was added to FFRCT, as compared to FFRCT alone (p=NS). Conclusion FFRCT improves discrimination for identification of individuals who manifest ischemia. The addition of CT stenosis findings to FFRCT does not improve discrimination of individuals with ischemia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".