Prognostic Value of Fractional Flow Reserve
Bibliographic record
Abstract
BACKGROUND: Fractional flow reserve (FFR) has become an established tool for guiding treatment, but its graded relationship to clinical outcomes as modulated by medical therapy versus revascularization remains unclear. OBJECTIVES: The study hypothesized that FFR displays a continuous relationship between its numeric value and prognosis, such that lower FFR values confer a higher risk and therefore receive larger absolute benefits from revascularization. METHODS: Meta-analysis of study- and patient-level data investigated prognosis after FFR measurement. An interaction term between FFR and revascularization status allowed for an outcomes-based threshold. RESULTS: A total of 9,173 (study-level) and 6,961 (patient-level) lesions were included with a median follow-up of 16 and 14 months, respectively. Clinical events increased as FFR decreased, and revascularization showed larger net benefit for lower baseline FFR values. Outcomes-derived FFR thresholds generally occurred around the range 0.75 to 0.80, although limited due to confounding by indication. FFR measured immediately after stenting also showed an inverse relationship with prognosis (hazard ratio: 0.86, 95% confidence interval: 0.80 to 0.93; p < 0.001). An FFR-assisted strategy led to revascularization roughly half as often as an anatomy-based strategy, but with 20% fewer adverse events and 10% better angina relief. CONCLUSIONS: FFR demonstrates a continuous and independent relationship with subsequent outcomes, modulated by medical therapy versus revascularization. Lesions with lower FFR values receive larger absolute benefits from revascularization. Measurement of FFR immediately after stenting also shows an inverse gradient of risk, likely from residual diffuse disease. An FFR-guided revascularization strategy significantly reduces events and increases freedom from angina with fewer procedures than an anatomy-based strategy.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".