Improvement in angina pectoris after percutaneous coronary interventions in focal and diffuse coronary artery disease
Bibliographic record
Abstract
Abstract Objective To investigate the effect of PCI on patient-reported outcomes in focal and diffuse coronary artery disease (CAD) as defined by the pullback pressure gradient (PPG). Background Improvements in fractional flow reserve (FFR) following PCI are associated with freedom from angina. CAD patterns influence the FFR change after stenting. Therefore, CAD patterns might be essential to assess the likelihood of PCI success in terms of angina relief. Methods This is a sub-analysis of the TARGET-FFR randomized clinical trial (NCT03259815). The 7-item Seattle Angina Questionnaire (SAQ-7) and EuroQol five-level EQ-5D questionnaire (EQ-5D-5L) were administered at baseline and three months after PCI. The PPG index was calculated from manual pre-PCI FFR pullbacks and the median PPG value was used to define focal and diffuse CAD. Results 103 patients (51 with focal and 52 with diffuse disease) were analyzed. There were no differences in baseline characteristics between patients with focal and diffuse CAD. Patients with focal disease had larger increases in FFR with PCI than those with diffuse disease (0.30±0.14 units vs 0.19±0.12 units, p<0.001). Patients who underwent PCI to focal CAD had significantly higher SAQ-7 summary scores at follow-up compared to those with diffuse CAD (87.1±20.3 vs. 75.6±24.4, mean difference 11.5 [95% CI 2.8 to 20.3], p=0.01). Following PCI, residual angina was present in 39.8% of all patients but was significantly lower among those with treated focal CAD (27.5% vs 51.9%, p-value=0.020). Conclusion Persistent angina after PCI was almost twice as common in patients with diffuse CAD as defined by the pre-PCI PPG. Patients with focal disease reported greater improvement in angina and quality of life with PCI. The likelihood of successful angina relief from PCI can be predicted by the baseline pattern of CAD. Funding Acknowledgement Type of funding sources: None.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 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".