Coronary CT-based FFR in patients with acute myocardial infarction might predict follow-up invasive FFR: The XPECT-MI study
Bibliographic record
Abstract
BackgroundWe 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(FFRCT) following the index event in predicting follow-up FFRi.MethodsWe 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 FFRCT (within ≤10 days after STEMI). Follow-up FFRi was performed at 45–60 days (FFRi and FFRCT value of ≤0.8 was considered positive).ResultsFFRi 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 FFRCT was 0.81 [0.68–0.93]. In total, 20 lesions were positive on FFRCT. A stronger correlation and smaller bias were found between FFRCT 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 FFRCT, 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 FFRCT.ConclusionFFRCT 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 FFRCT 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".