Predictors of Quality-of-Life Benefit After Percutaneous Coronary Intervention
Bibliographic record
Abstract
BACKGROUND: Improving patients' quality of life is a primary indication for percutaneous coronary intervention (PCI), yet little is known about patient characteristics associated with greater quality-of-life improvement from the procedure. This study was conducted to identify patient characteristics associated with quality-of-life benefit after PCI. METHODS AND RESULTS: A consecutive series of 1518 patients undergoing PCI in nonacute myocardial infarction settings were prospectively enrolled into an observational study documenting their postprocedural health status. We examined univariate and multivariable associations between baseline patient characteristics and quality of life 1 year after the procedure using the disease-specific Seattle Angina Questionnaire (SAQ) to quantify the impact of patients' coronary disease on their quality of life. Baseline angina frequency and physical function were the strongest predictors of quality-of-life improvement 1 year after PCI. In comparing patients without angina to those experiencing monthly, weekly, and daily angina, the quality-of-life improvements (mean+/-SEM) were 21.4+/-2.1, 30.7+/-2.2, and 34.6+/-2.6 points greater (P<0.001). Patients with mild, moderate, and severe physical limitation improved 13.8+/-1.9, 20.0+/-2.1, and 13.5+/-3.5 points more than those with minimal baseline physical limitation (P<0.001). These findings were maintained in multivariable models correcting for baseline differences in demographic, clinical, disease-severity, and health-status variables. CONCLUSIONS: Preprocedural angina frequency is the most important prognostic indicator of quality-of-life improvement after PCI. Although substantial quality-of-life benefits are attained in most patients with preprocedural angina, more careful consideration of the potential benefits and risks of the procedure are needed in asymptomatic patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".