Influence of pathophysiological patterns of coronary artery disease on the safety and efficacy of percutaneous coronary intervention
Bibliographic record
Abstract
Abstract Background Impaired blood flow after PCI, reflected by low FFR, portends a worse prognosis. Prior to intervention, pressure gradient distributions offer insights into the likelihood of subsequent PCI success. The pullback pressure gradient (PPG) serves as a quantifiable measure of CAD patterns: values approximating 1 signify focal disease, while values approaching 0 suggest diffuse disease. This study aimed to assess the impact of pathophysiological CAD patterns on the safety and efficacy of PCI. Methods PPG Global was a prospective, investigator-initiated, multicenter study, single-arm that enrolled patients with at least one lesion with an FFR ≤ 0.80 scheduled to be treated by PCI. The study enrolled 1004 patients (1057 vessels). A standardised physiological assessment was performed, including online PPG calculation from manual FFR pullbacks. CAD patterns were defined as predominantly focal or diffuse disease based on the median PPG value. The study was powered to ascertain the predictive capacity of PPG for optimal PCI results (defined as FFR≥0.88 after PCI) as assessed by the AUC. Immediate PCI outcomes were assessed using post-PCI FFR and CFR. Following PCI, biomarkers (troponin) were collected. An independent clinical events committee adjudicated periprocedural myocardial infarction. The assessment of peri-procedural myocardial infarction (MI) adhered to the criteria outlined in the 4th Universal Definition of Myocardial Infarction. Target vessel failure (TVF) was defined as cardiac death, myocardial infarction and target vessel revascularisation. Results One thousand and four patients with 1057 vessels were included. The mean FFR was 0.68 ± 0.12, PPG 0.62 ± 0.17, post-PCI FFR 0.87 ± 0.07, and post-PCI CFR was 3.19 ± 1.93. PPG was significantly correlated with the change in FFR after PCI (r=0.65, 95% CI: 0.61-0.69, p<0.001) and showed an area under the curve of 0.82 (95% CI: 0.79-0.84) to predict optimal revascularisation. Suboptimal FFR (<0.88) after an angiographically successful PCI occurred in 471 vessels (53.5%) and was significantly higher in patients with diffuse disease (37.1% vs 74.0%, p<0.001). The change in CFR was sixfold higher in patients with high PPG (delta CFR focal 1.18 ± 1.94 and diffuse 0.19 ± 1.52, p<0.001). The rate of in-hospital TVF was similar between patients with focal vs diffuse disease (5.1% vs 8.5%, p=0.060). The incidence of periprocedural MI was significantly higher in patients with diffuse disease (5.9% vs 9.8%, p=0.050; OR 1.83, 95% CI 1.02 to 3.34). Conclusions Pathophysiological CAD patterns distinctly affect the safety and efficacy of PCI. PCI in focal disease was associated with improved physiological outcomes and a lower rate of periprocedural myocardial infarction compared to diffuse disease. Quantifying PPG before intervention reliably predicted post-PCI FFR. Further investigation through a randomised trial is warranted to explore the potential advantages of a PPG-guided PCI strategy.PPG and Revascularisation OutcomesPPG and FFR correlation Pre and Post-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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".