Predicting Acute Myocardial Infarction in Patients with Critical Coronary Artery Narrowing: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Acute myocardial infarction (AMI) remains a global health burden. Early identification of individuals at high risk, particularly those with ≥70% coronary artery stenosis, can facilitate preventive strategies. This systematic review and meta-analysis aimed to evaluate the risk of AMI among patients with critical coronary artery narrowing. Methods: A comprehensive literature search of PubMed, Scopus, Embase, and Cochrane Library was conducted for stud ies published from January 2000 to December 2023. Eligible studies included adults (≥18 years) with angiographically confirmed ≥70% stenosis in at least one major coronary artery and reported AMI incidence. Data were extracted and pooled using a random-effects model. Heterogeneity was assessed using the I² statistic, and risk of bias was evaluated with the Newcastle-Ottawa Scale and the Cochrane RoB tool. PROSPERO Registration: CRD420251075342. Results: Eighteen studies (n=22,456 participants) were included. The pooled relative risk (RR) of developing AMI in patients with ≥70% stenosis was 3.45 (95% CI: 2.88–4.13), with moderate heterogeneity (I² = 48%). Subgroup analysis showed higher risk among patients with multi-vessel disease and diabetics. Funnel plot and Egger’s test (p=0.22) showed no significant publication bias. Conclusion: Patients with critical coronary stenosis are at substantially elevated risk for subsequent AMI. These findings emphasize the need for vigilant monitoring, risk stratification, and aggressive therapeutic in terventions in this high-risk cohort. Future studies should focus on novel biomarkers and predictive models to enhance early detection.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".