Clinical Validation of a Virtual Planner for Coronary Interventions Based on Coronary CT Angiography
Bibliographic record
Abstract
Low fractional flow reserve (FFR) values after percutaneous coronary intervention (PCI) carry a worse prognosis than high post-PCI FFR values. Therefore, the ability to predict post-PCI FFR might play an important role in procedural planning. Post-PCI FFR values can now be computed from pre-PCI coronary computed tomography angiography (CTA) using the fractional flow reserve derived from coronary computed tomography angiography revascularization planner (FFRCT Planner). The aim of this study was to validate the accuracy of the FFRCT Planner. In this multicenter, investigator-initiated, prospective study, patients with chronic coronary syndromes and significant lesions based on invasive FFR ≤0.80 were recruited. The FFRCT Planner was applied to the fractional flow reserve derived from coronary computed tomography angiography (FFRCT) model, simulating PCI. The primary objective was the agreement between the predicted post-PCI FFR by the FFRCT Planner and measured post-PCI FFR. Accuracy of the FFRCT Planner’s luminal dimensions was assessed by using post-PCI optical coherence tomography as the reference. Overall, 259 patients were screened, with 120 patients (123 vessels) included in the final analysis. The mean patient age was 64 ± 9 years, and 24% had diabetes. Measured FFR post-PCI was 0.88 ± 0.06, and the FFRCT Planner FFR was 0.86 ± 0.06 (mean difference: 0.02 ± 0.07 FFR unit; limits of agreement: –0.12 to 0.15). Optical coherence tomography minimal stent area was 5.60 ± 2.01 mm2, and FFRCT Planner minimal stent area was 5.0 ± 2.2 mm2 (mean difference: 0.66 ± 1.21 mm2; limits of agreement: –1.7 to 3.0). The accuracy and precision of the FFRCT Planner remained high in cases with focal and diffuse disease and with low and high calcium burden. The FFRCT-based technology was accurate and precise for predicting FFR after PCI. (Precise Percutaneous Coronary Intervention Plan Study [P3]; NCT03782688)
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".