Predictors of symptom improvement in patients with chronic coronary syndrome after percutaneous coronary intervention
Bibliographic record
Abstract
BACKGROUND: Decreases in symptom load and improvements in quality of life are important goals in the invasive treatment of symptomatic chronic coronary syndrome (CCS). To date, it is not known which patients profit most from the invasive treatment. METHODS: This sub-analysis of the prospective, multi-centre PLA-pCi-EBO trial includes 145 patients with symptomatic CCS and successful PCI. The prespecified endpoints angina pectoris and quality of life (Seattle Angina Questionnaire-SAQ) were assessed 1 and 6 months after PCI. Predictors of symptom improvement were analyzed by logistic regression analysis. RESULTS: Quality of life, physical limitation, and angina frequency markedly improved 6 months after PCI. Worse baseline health status (i.e., low SAQ subscales) was the best predictor of highly clinically relevant improvements (≥ 20 points in SAQ subscales) in symptom load and quality of life. Demographic factors (age, sex, body-mass index) and cardiovascular disease severity (number of involved vessels, ejection fraction) did not predict relevant improvements after PCI. The influence of psychologic traits has not previously been assessed. We found that neither optimism nor pessimism had a relevant effect on symptomatic outcome. However, patients who exercised more after PCI had a much larger improvement in quality of life despite no differences in physical limitation or angina frequency. CONCLUSION: PCI effectively reduces symptom load and improves quality of life in patients with symptomatic CCS. Reduced baseline health status (symptom load, quality of life) are the only relevant predictors for improvements after PCI. Physical activity after PCI is associated with greater benefits for quality of life. TRIAL REGISTRY: The German Clinical Trials Register registration number is DRKS0001752.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".