Simplifying the assessment of coronary artery stenosis by enhancing instantaneous wave free ratio
Bibliographic record
Abstract
Background: Instantaneous wave free ratio (iFR) does not require adenosine, but has a relatively wide intermediate range where functional assessment remains inconclusive. In this pilot study, we sought to enhance iFR through with the use of intracoronary (IC) saline (iFRs) and contrast media (iFRc) and determine whether these techniques correlated well with fractional flow reserve (FFR). Methods: Patients with coronary artery stenosis (CAS) associated with an iFR in the intermediate zone (≥0.86 and ≤0.93) were prospectively assessed with resting distal coronary pressure/aorta pressure (Pd/Pa), iFR, iFRs, iFRc and FFR. Results: A total of 40 coronary lesions were studied (40 patients). Pearson correlation coefficients for FFR and iFR, FFR and iFRs, FFR and iFRc were respectively: 0.57 (P=0.0002), 0.80 (P<0.0001) and 0.77 (P<0.0001). Receiver-operating characteristic (ROC) curve analysis showed similar area under the curve (AUC) of iFRs and iFR [0.90 (95% CI: 0.76–1) vs. 0.89 (95% CI: 0.79–0.99), P=0.89]. Youden’s index established cut-off values of ≤0.90 for iFR (sensitivity =91%, specificity =74%) and ≤0.78 for iFRs (sensitivity =73%, specificity =100%). In contrast, the AUC of iFRc was superior to the AUC of iFR [0.99 (95% CI: 0.98–1), P=0.049]. iFRc showed excellent accuracy and established cut-off values of ≤0.81 in predicting an FFR value of ≤0.80 (sensitivity =100%, specificity =93%). Conclusions: When iFR is in the intermediate zone, functional assessment of CAS by iFR is enhanced with the use of contrast media but not saline. This pilot study could be hypothesis generating for further study to enhance iFR specificity and sensibility.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".