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 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".