Fractional flow reserve derived from coronary computed tomography angiography: diagnostic performance in hypertensive and diabetic patients
Bibliographic record
Abstract
AIMS: Fractional flow reserve (FFR) derived from coronary computed tomography (FFRCT) has high diagnostic performance in stable coronary artery disease (CAD). The diagnostic performance of FFRCT in patients with hypertension (HTN) and diabetes (DM), who are at risk of microvascular impairment, is not known. METHODS AND RESULTS: We analysed the diagnostic performance of FFRCT, in patients (vessels) with DM (n = 16), HTN (n = 186), DM + HTN (n = 58) vs. controls (n = 107) with or with suspected CAD. Patients (vessels) were further divided according to left ventricular mass index (LVMI) tertiles. Reference standard was invasively measured FFR ≤0.80. Per-patient diagnostic accuracy (95% CI) in control patients was 71.7% (61.6-81.8) vs. 79.3 (74.0-85.0) (P = 0.12), 75.0% (47.6-92.7) (P = 0.52), and 75.9% (62.8-86.1) (P = 0.39) in patients with HTN, DM, and HTM + DM, respectively. There was no difference in discrimination of ischaemia by FFRCT between groups. On a per-vessel level, there was no significant difference in diagnostic performance or discrimination of ischaemia by FFRCT between groups. There was a decline in both per-patient and -vessel diagnostic specificity of FFRCT in the upper LVMI tertile when compared with lower tertiles; however, discrimination of ischaemia by FFRCT was unaltered across LVMI tertiles. CONCLUSION: The diagnostic performance of FFRCT is independent of the presence of HTN and DM. FFRCT is a robust method in a broad stable CAD population, including patients at high risk for microvascular disease.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".