Prognostic value of coronary CT angiography derived fractional flow reserve in patients with myocardial infarction
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Introduction We sought to analyze whether invasive fractional flow reserve (FFRi) values of non-infarction related (non-IRA) lesions change over time in ST Elevation Myocardial Infarction (STEMI) patients and to evaluate the diagnostic performance of coronary CT angiography derived FFR (FFRCT) close to the index event for defining follow-up FFRi. Methods We prospectively enrolled 38 multi-vessel STEMI patients (mean age 61.6±9 years, mean body mass index 28.8±4.8 kg/m2, 23.1 % female) who underwent non-IRA baseline and follow-up FFRi measurement and during the 10-day period after the index event FFRCT measurement. Follow-up FFRi was performed in 45–60 days after the index event and staged percutaneous coronary intervention (PCI) was carried out if FFRi was ≤0.8 at follow-up. Results Mean FFRi values showed significant difference between baseline and follow-up (0.83±0.11 vs. 0.80±0.13 p = 0.04, respectively). Mean FFR-CT was 0.79±0.14. Stronger correlation and smaller mean bias was found between FFR-CT and follow-up FFRi (ρ=0.86, p<0.001, bias: 0.01) as compared with baseline FFRi (ρ=0.68, p<0.001, bias: 0.04). The overall accuracy was 94.7% with 100.0% sensitivity and 90.0% specificity for identifying lesions ≤0.8 on follow-up FFRi. Accuracy, sensitivity and specificity were 81.5%, 93.3% 73.9%, respectively for identifying lesions on baseline FFRi using FFR-CT. Conclusion Non-invasive assessment of FFR at hospital discharge after the acute event lead to a better diagnostic performance for the identification of hemodynamically relevant non-IRA lesions as compared with baseline FFRi. FFRCT might improve decision making by selecting the patients for staged PCI in patients with multi-vessel STEMI.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".