Derivation and validation of a novel functional FFRCT score incorporating the burden of coronary stenosis severity and flow impairment to predict clinical events
Bibliographic record
Abstract
BackgroundA score combining the burden of stenosis severity on coronary computed tomography angiography (CCTA) and flow impairment by fractional flow reserve derived from computed tomography (FFRCT) may be a better predictor of clinical events than either parameter alone.MethodsThe Functional FFRCT Score (FFS) combines CCTA and FFRCT parameters in an allocated point-based system. The feasibility of the FFS was assessed in cohort of 72 stable chest pain patients with matched CCTA and FFRCT datasets. Validation was performed using 2 cohorts: (a) 4468 patients from the ADVANCE Registry to define its association with revascularization and major adverse cardiovascular events (MACE); (b) 212 patients from the FORECAST trial to determine predictors of MACE.ResultsThe median calculation time for the FFS was 10 (interquartile range 6–17) seconds, with strong intra-operator and inter-operator agreement (Cohen's Kappa 0.89 (±0.37, p < 0.001) and 0.83 (±0.04, p < 0.001, respectively). The FFS correlated strongly with both the CT-SYNTAX and the Functional CT-SYNTAX scores (rS = 0.808 for both, p < 0.001).In the ADVANCE cohort the FFS had good discriminatory abilities for revascularization with an area under the curve of 0.82, 95 % confidence interval (CI) 0.81–0.84, p < 0.001. Patients in the highest FFS tertile had significantly higher rates of revascularization (61 % vs 5 %, p < 0.001) and MACE (1.9 % vs 0.5 %, p = 0.001) compared with the lowest FFS tertile.In the FORECAST cohort the FFS was an independent predictor of MACE at 9-month follow-up (hazard ratio 1.04, 95 % CI 1.01–1.08, p < 0.01).ConclusionThe FFS is a quick-to-calculate and reproducible score, associated with revascularization and MACE in two distinct populations of stable symptomatic patients.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".