Coronary CT-based FFR in patients with acute myocardial infarction might predict follow-up invasive FFR: The XPECT-MI study
Bibliographic record
Abstract
Background We aimed to evaluate whether invasive fractional flow reserve (FFRi) of non-infarction related (non-IRA) lesions changes over time in ST-elevation myocardial infarction (STEMI) patients. Moreover, we assessed the diagnostic performance of coronary CT angiography-derived FFR(FFR CT ) following the index event in predicting follow-up FFRi. Methods We prospectively enrolled 38 STEMI patients (mean age 61.6 ± 9 years, 23.1% female) who underwent non-IRA baseline and follow-up FFRi measurements and a baseline FFR CT (within ≤10 days after STEMI). Follow-up FFRi was performed at 45–60 days (FFRi and FFR CT value of ≤0.8 was considered positive). Results FFRi values showed significant difference between baseline and follow-up (median and interquartile range (IQR) 0.85 [0.78–0.92] vs. 0.81 [0.73–0.90] p = 0.04, respectively). Median FFR CT was 0.81 [0.68–0.93]. In total, 20 lesions were positive on FFR CT . A stronger correlation and smaller bias were 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). Comparing follow-up FFRi and FFR CT, no false negatives but two false positive cases were found. The overall accuracy was 94.7%, with sensitivity and specificity of 100.0% and 90.0% for identifying lesions ≤0.8 on FFRi. Accuracy, sensitivity, and specificity were 81.5%, 93.3%, and 73.9%, respectively, for identifying significant lesions on baseline FFRi using index FFR CT . Conclusion FFR CT in STEMI patients close to the index event could identify hemodynamically relevant non-IRA lesions with higher accuracy than FFRi measured at the index PCI, using follow-up FFRi as the reference standard. Early FFR CT in STEMI patients might represent a new application for cardiac CT to improve the identification of patients who benefit most from staged non-IRA revascularization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".