Non‐invasive procedural planning using computed tomography‐derived fractional flow reserve
Bibliographic record
Abstract
Abstract Objectives This study aimed to investigate the performance of computed tomography derived fractional flow reserve based interactive planner (FFR CT planner) to predict the physiological benefits of percutaneous coronary intervention (PCI) as defined by invasive post‐PCI FFR. Background Advances in FFR CT technology have enabled the simulation of hyperemic pressure changes after virtual removal of stenoses. Methods In 56 patients (63 vessels) invasive FFR measurements before and after PCI were obtained and FFR CT was calculated using pre‐PCI coronary CT angiography. Subsequently, FFR CT and invasive coronary angiography models were aligned allowing virtual removal of coronary stenoses on pre‐PCI FFR CT models in the same locations as PCI was performed. Relationships between invasive FFR and FFR CT , between post‐PCI FFR and FFR CT planner, and between delta FFR and delta FFR CT were evaluated. Results Pre PCI, invasive FFR was 0.65 ± 0.12 and FFR CT was 0.64 ± 0.13 ( p = .34) with a mean difference of 0.015 (95% CI: −0.23–0.26). Post‐PCI invasive FFR was 0.89 ± 0.07 and FFR CT planner was 0.85 ± 0.07 ( p < .001) with a mean difference of 0.040 (95% CI: −0.10–0.18). Delta invasive FFR and delta FFR CT were 0.23 ± 0.12 and 0.21 ± 0.12 ( p = .09) with a mean difference of 0.025 (95% CI: −0.20–0.25). Significant correlations were found between pre‐PCI FFR and FFR CT (r = 0.53, p < .001), between post‐PCI FFR and FFR CT planner (r = 0.41, p = .001), and between delta FFR and delta FFR CT (r = 0.57, p < .001). Conclusions The non‐invasive FFR CT planner tool demonstrated significant albeit modest agreement with post‐PCI FFR and change in FFR values after PCI. The FFR CT planner tool may hold promise for PCI procedural planning; however, improvement in technology is warranted before clinical application.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".