Percutaneous Coronary Intervention Outcomes in Patients with Stable Coronary Disease and Left Ventricular Systolic Dysfunction
Bibliographic record
Abstract
AIMS: We sought to better understand the role of percutaneous coronary intervention (PCI) in patients with stable coronary artery disease (CAD) and moderate or severe left ventricular systolic dysfunction. METHODS AND RESULTS: Using data from the Duke Databank for Cardiovascular Disease, we analysed patients who underwent coronary angiography at Duke University Medical Center (1995-2012) that had stable CAD amenable to PCI and left ventricular ejection fraction ≤35%. Patients with acute coronary syndrome or Canadian Cardiovascular Society class III or IV angina were excluded. We used propensity-matched Cox proportional hazards to evaluate the association of PCI with mortality and hospitalizations. Of 901 patients, 259 were treated with PCI and 642 with medical therapy. PCI propensity scores created from 24 variables were used to assemble a matched cohort of 444 patients (222 pairs) receiving PCI or medical therapy alone. Over a median follow-up of 7 years, 128 (58%) PCI and 125 (56%) medical therapy alone patients died [hazard ratio 0.87 (95% confidence interval 0.68, 1.10)]; there was also no difference in the rate of a composite endpoint of all-cause mortality or cardiovascular hospitalization [hazard ratio 1.18 (95% confidence interval 0.96, 1.44)] between the two groups. CONCLUSIONS: In this well-profiled, propensity-matched cohort of patients with stable CAD amenable to PCI and moderate or severe left ventricular systolic dysfunction, the addition of PCI to medical therapy did not improve long-term mortality, or the composite of mortality or cardiovascular hospitalization. The impact of PCI on other outcomes in these high-risk patients requires further study.
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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) | 0.000 | 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".