ArticleFrontiers in endocrinology2025
Monocyte-to-lymphocyte ratio as a predictor of left ventricular aneurysm in acute STEMI patients.
Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Bioenergetic failure in diabetic peripheral neuropathy: from glucotoxicity to multidimensional metabolic imbalance.Journal of neurology · 2026Review
- The CALLY index as a novel predictor of left ventricular aneurysm in patients with ST-segment elevation myocardial infarction.Frontiers in cardiovascular medicine · 2026Article
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8 authors.
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Abstract
Background: The monocyte-to-lymphocyte ratio (MLR) has emerged as a novel marker of inflammation. Nevertheless, its potential utility in predicting the development of left ventricular aneurysm (LVA) remains unexplored. This study aims to investigate the association between MLR and the risk of LVA in patients presenting with acute ST segment elevation myocardial infarction (STEMI). Methods: A total of 551 patients were enrolled in the first cohort, and 471 patients were included in the validation cohort. To evaluate the predictive value of MLR for LVA, multivariable logistic regression analysis, restricted cubic splines (RCS) analysis, and receiver operating characteristic (ROC) analysis were employed. Results: The prevalence of LVA was 14.5% in the first cohort and 13.6% in the validation cohort. The multivariable logistic regression analysis revealed that individuals in the highest quartile of MLR (Q4) exhibited a significantly increased risk of LVA formation compared to those in the lowest quartile (Q1) in both cohorts (first cohort: OR = 3.07, 95% CI = 1.33-7.08, Conclusion: An elevated MLR was independently linked to an increased risk of LVA development in patients with STEMI who received primary PCI. This readily available inflammatory index may offer supplementary prognostic information and could be considered for inclusion in future risk stratification models.
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