A global registry of fractional flow reserve (FFR)–guided management during routine care: Study design, baseline characteristics and outcomes of invasive management
Bibliographic record
Abstract
BACKGROUND: The use and clinical outcomes of fractional flow reserve (FFR)-guided revascularization in patients presenting with either stable coronary artery disease (CAD) or an acute coronary syndrome (ACS) in daily clinical practice are uncertain. OBJECTIVE: To prospectively characterize the frequency of the change in treatment plan when FFR is performed compared to the initial decision based on angiography alone and procedure-related outcomes. METHODS: We undertook a prospective, multicenter, multinational, open-label, observational study of coronary physiologic measurements during clinically indicated coronary angiography. The treatment plan, including medical therapy, PCI or CABG, was prospectively recorded before and after performing FFR. Adverse events were pre-defined and prospectively recorded per local investigators (PRESSUREwire; ClinicalTrials.gov identifier: NCT02935088). RESULTS: Two thousand two hundred and seventeen subjects were enrolled in 70 hospitals across 15 countries between October 2016-February 2018. The mean FFR (all measurements) was 0.84. The treatment plan based on angiography-alone changed in 763/2196 subjects (34.7%) and 872/2931 lesions (29.8%) post-FFR. In the per-patient analysis, the initial treatment plan based on angiography versus the final treatment plan post-FFR were medical management 1,350 (61.5%) versus 1,470 (66.9%) (p = .0017); PCI 717 (32.7%) versus 604 (27.5%) (p = .0004); CABG 119 (5.4%) versus 121 (5.5%) (p = .8951). The frequency of intended revascularization changed from 38.1 to 33.0% per patient (p = .0005) and from 35.5 to 29.6% per lesion (p < .0001) following FFR. CONCLUSIONS: On an individual patient basis, use of FFR in everyday practice changes the treatment plan compared to angiography in more than one third of all-comers selected for physiology-guided managements. FFR measurement is safe, providing incremental information to guide revascularization decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".