Assessing Risk in Patients with Stable Coronary Disease: When Should We Intensify Care and Follow-Up? Results from a Meta-Analysis of Observational Studies of the COURAGE and FAME Era
Bibliographic record
Abstract
Background. A large number of clinical and laboratory markers have been appraised to predict prognosis in patients with stable angina, but uncertainty remains regarding which variables are the best predictors of prognosis. Therefore, we performed a meta-analysis of studies in patients with stable angina to assess which variables predict prognosis. Methods. MEDLINE and PubMed were searched for eligible studies published up to 2015, reporting multivariate predictors of major adverse cardiac events (MACE, a composite endpoint of death, myocardial infarction, and revascularization) in patients with stable angina. Study features, patient characteristics, and prevalence and predictors of such events were abstracted and pooled with random-effect methods (95% CIs). Major adverse cardiovascular event (MACE) was the primary endpoint. Results. 42 studies (104,559 patients) were included. After a median follow-up of 57 months, cardiovascular events occurred in 7.8% of patients with MI in 6.2% of patients and need for repeat revascularization (both surgical and percutaneous) in 19.5% of patients. Male sex, reduced EF, diabetes, prior MI, and high C-reactive protein were the most powerful predictors of cardiovascular events. Conclusions. We show that simple and low-cost clinical features may help clinicians in identifying the most appropriate diagnostic and therapeutic approaches within the broad range of outpatients presenting with stable coronary artery disease.
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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 | 0.048 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.040 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".