Impact of Pullback Pressure Gradient on Clinical Outcomes after Percutaneous Coronary Interventions
Bibliographic record
Abstract
BACKGROUND: Impaired flow following percutaneous coronary intervention (PCI) is a known predictor of adverse outcomes. The pullback pressure gradient (PPG) is a novel physiological metric that differentiates focal from diffuse disease and enables prediction of post-PCI fractional flow reserve (FFR). This post hoc analysis of the PPG Global (NCT04789317) study aimed to evaluate the prognostic performance of a PPG model for predicting post-PCI FFR and to determine whether the predicted physiological outcome is associated with adverse events following PCI. METHODS: Prospective and multicenter study including patients with hemodynamically significant coronary artery disease undergoing PCI. A prediction model based on FFR and PPG was used to estimate post-PCI FFR. Based on the predicted values, vessels were classified as having either optimal or suboptimal post-PCI physiology. The primary end point was target vessel failure at 1 year. Target vessel failure was defined as a composite of cardiac death, target-vessel myocardial infarction, and ischemia-driven target vessel revascularization. RESULTS: A total of 855 patients (890 vessels) were analyzed. The mean difference between predicted and measured post-PCI FFR was 0.001 (limits of agreement, –0.10 to 0.10). There was a strong correlation between predicted and measured delta FFR ( r =0.92 [95% CI, 0.91–0.93]; P <0.001). Vessels with predicted suboptimal post-PCI physiology had a significantly higher incidence of target vessel failure (adjusted hazard ratio, 1.97 [95% CI, 1.24–3.15]; P =0.004). Predicted suboptimal physiology was independently associated with adverse clinical outcomes. CONCLUSIONS: PPG-predicted post-PCI physiology was associated with target vessel failure at 1 year. These findings extend the role of coronary physiology beyond diagnostic assessment to include risk stratification and outcome prediction following PCI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.020 |
| 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 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".