Fractional Flow Reserve Derived from Coronary Computed Tomography Angiography Safely Defers Invasive Coronary Angiography in Patients with Stable Coronary Artery Disease
Bibliographic record
Abstract
Objectives: In the United States, the real-world feasibility and outcome of using fractional flow reserve from coronary computed tomography angiography (FFRCT) is unknown. We sought to determine whether a strategy that combined coronary computed tomography angiography (CTA) and FFRCT could safely reduce the need for invasive coronary angiography (ICA), as compared to coronary CTA alone. Methods: The study included 387 consecutive patients with suspected CAD referred for coronary CTA with selective FFRCT and 44 control patients who underwent CTA alone. Lesions with 30–90% diameter stenoses were considered of indeterminate hemodynamic significance and underwent FFRCT. Nadir FFRCT ≤ 0.80 was positive. The rate of patients having ICA, revascularization and major adverse cardiac events were recorded. Results: Using coronary CTA and selective FFRCT, 121 patients (32%) had at least one vessel with ≥50% diameter stenosis; 67/121 (55%) patients had at least one vessel with FFRCT ≤ 0.80; 55/121 (45%) underwent ICA; and 34 were revascularized. The proportion of ICA patients undergoing revascularization was 62% (34 of 55). The number of patients with vessels with 30–50% diameter of stenosis was 90 (23%); 28/90 (31%) patients had at least one vessel with FFRCT ≤ 0.80; 8/90 (9%) underwent ICA; and five were revascularized. In our institutional practice, compared to coronary CTA alone, coronary CTA with selective FFRCT reduced the rates of ICA (45% vs. 80%) for those with obstructive CAD. Using coronary CTA with selective FFRCT, no major adverse cardiac events occurred over a mean follow-up of 440 days. Conclusion: FFRCT safely deferred ICA in patients with CAD of indeterminate hemodynamic significance. A high proportion of those who underwent ICA were revascularized.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".