Global Fractional Flow Reserve Value Predicts 5‐Year Outcomes in Patients With Coronary Atherosclerosis But Without Ischemia
Bibliographic record
Abstract
Background Global fractional flow reserve (FFR) (ie, the sum of the FFR values in the 3 major coronary arteries) is a physiologic correlate of global atherosclerotic burden. The objective of the present study was to investigate the value of global FFR in predicting long‐term clinical outcome of patients with stable coronary artery disease but no ischemia‐inducing stenosis. Methods and Results We studied major adverse cardiovascular events (MACEs: all‐cause death, myocardial infarction, and any revascularization) after 5 years in 1122 patients without significant stenosis (all FFR >0.80; n=275) or with at least 1 significant stenosis successfully treated by percutaneous coronary intervention (ie, post–percutaneous coronary intervention FFR >0.80; n=847). The patients were stratified into low, mid, or high tertiles of global FFR (≤2.80, 2.80–2.88, and ≥2.88). Patients in the lowest tertile of global FFR showed the highest 5‐year MACE rate compared with those in the mid or high tertile of global FFR (27.5% versus 22.0% and 20.9%, respectively; log‐rank P =0.040). The higher 5‐year MACE rate was mainly driven by a higher rate of revascularization in the low global FFR group (16.4% versus 11.3% and 11.8%, respectively; log‐rank P =0.038). In a multivariable model, an increase in global FFR of 0.1 unit was associated with a significant reduction in the rates of MACE (hazard ratio [HR], 0.988; 95% CI, 0.977–0.998; P =0.023), myocardial infarction (HR, 0.982; 95% CI, 0.966–0.998; P =0.032), and revascularization (HR, 0.985; 95% CI, 0.972–0.999; P =0.040). Conclusions Even in the absence of ischemia‐producing stenoses, patients with a low global FFR, physiologic correlate of global atherosclerotic burden, present a higher risk of MACE at 5‐year follow‐up.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".