Procedural Outcomes After Percutaneous Coronary Interventions in Focal and Diffuse Coronary Artery Disease
Bibliographic record
Abstract
Background Coronary artery disease (CAD) patterns play an essential role in the decision‐making process about revascularization. The pullback pressure gradient (PPG) quantifies CAD patterns as either focal or diffuse based on fractional flow reserve (FFR) pullbacks. The objective of this study was to evaluate the impact of CAD patterns on acute percutaneous coronary intervention (PCI) results considered surrogates of clinical outcomes. Methods and Results This was a prospective, multicenter study of patients with hemodynamically significant CAD undergoing PCI. Motorized FFR pullbacks and optical coherence tomography (OCT) were performed before and after PCI. Post‐PCI FFR >0.90 was considered an optimal result. Focal disease was defined as PPG >0.73 (highest PPG tertile). Overall, 113 patients (116 vessels) were included. Patients with focal disease were younger than those with diffuse CAD (61.4±9.9 versus 65.1±8.7 years, P =0.042). PCI in vessels with high PPG (focal CAD) resulted in higher post‐PCI FFR (0.91±0.07 in the focal group versus 0.86±0.05 in the diffuse group, P <0.001) and larger minimal stent area (6.3±2.3 mm 2 in focal versus 5.3±1.8 mm 2 in diffuse CAD, P =0.015) compared withvessels with low PPG (diffuse CAD). The PPG was associated with the change in FFR after PCI ( R 2 =0.51, P <0.001). The PPG significantly improved the capacity to predict optimal PCI results compared with an angiographic assessment of CAD patterns (area under the curve PPG 0.81 [95% CI, 0.73–0.88] versus area under the curve angio 0.51 [95% CI, 0.42–0.60]; P <0.001). Conclusions PCI in vessels with focal disease defined by the PPG resulted in greater improvement in epicardial conductance and larger minimal stent area compared with diffuse disease. PPG, but not angiographically defined CAD patterns, distinguished patients attaining superior procedural outcomes. Registration URL: https://clinicaltrials.gov/ct2/show/NCT03782688
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".