1185Head-to-head comparison of FFR-CT against coronary CT angiography and myocardial perfusion imaging for the diagnosis of ischaemia
Bibliographic record
Abstract
Background: Non-invasive fractional flow reserve (FFR) computation from coronary computed tomography (CT) angiography datasets (FFR-CT) has recently emerged as a promising non-invasive test to assess hemodynamic severity of coronary artery disease (CAD). However, to date, comparative studies are lacking regarding the diagnostic accuracy in comparison with more traditional myocardial perfusion imaging with single-photon emission computed tomography (SPECT) and positron emission tomography (PET) Methods: This sub analysis from the PACIFIC trial involved 208 prospectively included patients with suspected stable CAD, who underwent 256-slice coronary CT angiography, 99mTc-tetrofosmin SPECT, [15O]H2O PET, and routine three-vessel invasive FFR measurements. FFR-CT values were retrospectively derived from the coronary CT angiography images. Images from each modality were interpreted in a blinded fashion by independent core laboratories. The diagnostic performance of FFR-CT was compared with coronary CT angiography, SPECT, and PET, using invasively measured FFR ≤0.80 as the reference standard for ischaemia. Results: In total, 505 out of 612 (83%) vessels could be evaluated with FFR-CT. On a per-vessel basis, sensitivity, specificity, and diagnostic accuracy were 90, 86, and 87% for FFR-CT versus 68, 83, and 79% for coronary CT angiography, versus 42, 97, and 82% for SPECT, versus 81, 76, and 80% for PET, respectively. Sensitivity, but not specificity, was significantly higher for FFR-CT in comparison with all other modalities. Consequently, diagnostic accuracy was significantly higher in comparison with coronary CT angiography (p=0.002) and PET (p=0.004), but only a favourable trend was found in comparison with SPECT (p=0.084). The diagnostic performance as assessed by the area under the receiver-operating characteristics curve was significantly greater for FFR-CT (0.94) as compared to coronary CT angiography (0.83, p<0.001), SPECT (0.70, p<0.001), and PET (0.87, p<0.001, figure 1).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".