2436Epicardial conductance beyond myocardial ischemia: five-year prognostic value of cumulative FFR measurements in patients without ischemia
Bibliographic record
Abstract
Background: The absence of ischemia or of hemodynamically significant stenosis (FFR>0.80) triggers a conservative management. Yet, in patients with coronary atherosclerosis but no significant stenosis, the event rate is far from negligible. Accordingly, we hypothesized that in patients with diffuse 3-vessel disease but no FFR value ≤0.80, the sum of the FFR values which reflect maximal epicardial conductance might correlate with clinical outcome. Methods and results: In a total of 1,122 patients without any significant lesion (n=275) or with at least one significant lesion (n=847) successfully treated by percutaneous coronary intervention (PCI), the complete 5-year FU was obtained. The patients were classified into high, mid or low total FFR according to the the 3 tertiles of total FFR (≤2.80; 2.80–2.88; ≥2.88). The primary endpoint was major adverse cardiac events (MACE, composite of death, myocardial infarction and any revascularization) at 5 years. The patients from the low total FFR group showed a higher risk of 5-year MACE than those in the mid and high group [(27.5% vs. 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 total FFR group (16.4% vs. 11.3 and 11.8%, respectively, log-rank p=0.038). In a multivariable model adjusted for baseline differences, cumulative FFR was an independent predictor of MACE (per increase of total FFR of 0.1, HR 0.882 [0.798, 0,975]; p=0.015), myocardial infarction (per increase of total FFR of 0.1, HR 0.823 [0.704, 0,962]; p=0.014), and revascularization (per increase of total FFR of 0.1, HR 0.868 [0.760, 0,990]; p=0.035),
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".