Prevalence and non-invasive predictors of left main or three-vessel coronary disease: evidence from a collaborative international meta-analysis including 22 740 patients
Bibliographic record
Abstract
BACKGROUND: Left main disease (LMD) and three-vessel disease (3VD) have important prognostic value in patients with coronary artery disease. However, uncertainties still exist about their prevalence and predictors in patients with acute coronary syndrome (ACS) and also in patients with stable coronary disease. Thus the aim of this study was to perform an international collaborative systematic review and meta-analysis to appraise the prevalence and predictors of LMD and 3VD. METHODS: Medline/PubMed were systematically searched for eligible studies published up to 2010, reporting multivariate predictors of LMD or 3VD. Study features, patient characteristics, and prevalence and predictors of LMD and 3VD were abstracted and pooled with random-effect methods (95% CIs). RESULTS: 17 studies (22 740 patients) were included, 11 focusing on ACS (17 896 patients) and six on stable coronary disease (4844 patients). In the ACS subgroup, LMD or 3VD occurred in 20% (95% CI 7.2% to 33.4%), LMD in 12% (95% CI 10.5% to 13.5%), and 3VD in 25% (95% CI 23.1% to 27.0%). Heart failure at admission and extent of ST-segment elevation in lead aVR on 12-lead ECG were the most powerful predictors of LMD or 3VD. In the stable disease subgroup, LMD or 3VD was found in 36% (95% CI 18.5% to 48.8%), with the most powerful predictors being transient ischaemic dilation during the imaging stress test, extent of ST-segment elevation in aVR and V1 during the stress test, and hyperlipidaemia. CONCLUSIONS: This meta-analysis demonstrated that severe coronary disease-that is, LMD or 3VD-is more common in patients with ACS or stable coronary disease than generally perceived, and that simple and low-cost tools may help in the selection of the most appropriate therapeutic approach.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.050 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".