1268A comparison between the diagnostic performance of quantitative flow ratio and non-invasive imaging modalities for diagnosing myocardial ischemia defined by FFR, a PACIFIC-trial interim analysis
Bibliographic record
Abstract
Abstract Background Quantitative flow ratio (QFR) uses fast computational algorithms based on 3-dimensional quantitative coronary angiography and estimation of contrast flow velocity during invasive coronary angiography (ICA) to obtain QFR values equivalent to fractional flow reserve (FFR). Objective To compare the diagnostic performance of QFR with coronary computed tomography angiography (CCTA), single-photon emission tomography (SPECT), and positron emission tomography (PET) for diagnosing myocardial ischemia defined by FFR. Method QFR computation was attempted in 109 patients (286 vessels without a subtotal/total lesion) of the 208 patients included in the PACIFIC-trial. Patients underwent 256-slice CCTA, Tetrofosmin SPECT, and [15O]H2O PET prior to ICA in conjunction with 3 vessel FFR measurements. ICA images were obtained without the use of a dedicated QFR acquistion protocol. QFR was calculated using a fixed empiric hyperemic flow velocity (fQFR) as well as using a patient specific flow velocity based on contrast passage through the coronary (cQFR). All analysis were performed on a per vessel level. Results Fixed QFR computation succeeded in 152 (53%) vessels while cQFR analysis was successful in 140 (49%) vessels. A good correlation between FFR and fQFR/cQFR was observed (R=0.774, p<0.001/R=0.790, p<0.001). The diagnostic performance in terms of sensitivity, specificity, negative predictive value, positive predictive value, and accuracy is presented in table 1. In total, 133 vessels with matched FFR, fQFR, cQFR, CCTA, SPECT, and PET results were available for the comparative C-statistic analysis, figure 1. The diagnostic performance of fQFR and cQFR was comparable (p=0.451) and superior to CCTA (p=0.004/p=0.003), SPECT (p<0.001/p<0.001), and PET (p=0.008/p=0.006), figure 1. CCTA, and PET performed alike (p=0.568) and outperformed SPECT (p=0.023, p=0.002). Table 1 % (95% Confidence Interval) fQFR n=152 cQFR (n=140) CCTA (n=152) SPECT (n=150) PET (n=149) Sensitivity 76 (59–89) 71 (53–86) 70 (51–84) 30 (16–49) 76 (58–89) Specificity 94 (88–98) 93 (86–97) 73 (64–81) 96 (90–99) 80 (72–87) Negative Predictive Value 93 (88–96) 92 (86–95) 90 (84–94) 83 (79–86) 92 (86–96) Positive Predictive Value 79 (64–89) 74 (59–85) 42 (33–51) 67 (42–84) 52 (42–62) Accuracy 90 (84–94) 88 (81–93) 72 (65–79) 81 (74–87) 79 (72–85) Figure 1. Conclusion Fixed QFR and cQFR correlate well with FFR with a high diagnostic accuracy as result. QFR outperformed CCTA, SPECT, and PET for the diagnosis of myocardial ischemia on a per vessel basis with the important footnote that fQFR and cQFR could only be computed in 53%, and 49% of the vessels.
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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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".