Adverse events while awaiting myocardial revascularization: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: The aim of the current study was to estimate adverse event rates while awaiting myocardial revascularization and review criteria for prioritizing patients. METHODS: A PubMed search was performed on 19 January 2015, to identify English-language, original, observational studies reporting adverse events while awaiting coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI). Rates of death, non-fatal myocardial infarction (MI) and emergency revascularization were calculated as occurrence rates per 1000 patient-weeks and pooled using random-effects models. RESULTS: The search yielded 1323 articles, of which 22 were included with 66 410 patients and 607 675 patient-weeks on the wait list. When awaiting CABG, rates per 1000 patient-weeks were 1.1 [95% confidence interval 0.9-1.3] for death, 1.0 [0.6-1.6] for non-fatal MI and 1.8 [0.8-4.1] for emergency revascularization. Subgroup analyses demonstrated consistent outcomes, and sensitivity analyses demonstrated comparable event rates with low heterogeneity. Higher urgency of revascularization was based primarily on angiographic complexity, angina severity, left ventricular dysfunction and symptoms on stress testing, and such patients with a semi-urgent status had a higher risk of death than patients awaiting elective revascularization (risk ratio at least 2.8). Individual studies identified angina severity and left ventricular dysfunction as most important predictors of death when awaiting CABG. Adverse rates per 1000 patient-weeks for patients awaiting PCI were 0.1 [95% confidence interval 0.0-0.4] for death, 0.4 [0.1-1.2] for non-fatal MI and 0.7 [0.4-1.4] for emergency revascularization but were based on only a few old studies. CONCLUSIONS: Rates of death, non-fatal MI and emergency revascularization when awaiting myocardial revascularization are infrequent but higher in specific patients. Countries that not yet have treatment recommendations related to waiting times should consider introducing a maximum to limit adverse events, particularly when awaiting CABG.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".