Coronary computed tomography angiography derived fractional flow reserve and risk of recurrent angina: A 3-year follow-up study
Bibliographic record
Abstract
BackgroundThe association between coronary computed tomography angiography (CTA) derived fractional flow reserve (FFRCT) and risk of recurrent angina in patients with new onset stable angina pectoris (SAP) and stenosis by CTA is uncertain.MethodsMulticenter 3-year follow-up study of patients presenting with symptoms suggestive of new onset SAP who underwent first-line CTA evaluation and subsequent standard-of-care treatment. All patients had at least one ≥30 % coronary stenosis. A per-patient lowest FFRCT-value ≤0.80 represented an abnormal test result. Patients with FFRCT ≤0.80 who underwent revascularization were categorized according to completeness of revascularization: 1) Completely revascularized (CR-FFRCT), all vessels with FFRCT ≤0.80 revascularized; or 2) incompletely revascularized (IR-FFRCT) ≥1 vessels with FFRCT ≤0.80 non-revascularized. Recurrent angina was evaluated using the Seattle Angina Questionnaire.ResultsAmongst 769 patients (619 [80 %] stenosis ≥50 %, 510 [66 %] FFRCT ≤0.80), 174 (23 %) reported recurrent angina at follow-up. An FFRCT ≤0.80 vs > 0.80 associated to increased risk of recurrent angina, relative risk (RR): 1.82; 95 % CI: 1.31–2.52, p < 0.001. Risk of recurrent angina in CR-FFRCT (n = 135) was similar to patients with FFRCT >0.80, 13 % vs 15 %, RR: 0.93; 95 % CI: 0.62–1.40, p = 0.72, while IR-FFRCT (n = 90) and non-revascularized patients with FFRCT ≤0.80 (n = 285) had increased risk, 37 % vs 15 % RR: 2.50; 95 % CI: 1.68–3.73, p < 0.001 and 30 % vs 15 %, RR: 2.03; 95 % CI: 1.44–2.87, p < 0.001, respectively. Use of antianginal medication was similar across study groups.ConclusionIn patients with SAP and coronary stenosis by CTA undergoing standard-of-care guided treatment, FFRCT provides information regarding risk of recurrent angina.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".