Effect of Elevated Neutrophil-to-Lymphocyte Ratio on Adverse Outcomes in Patients With Myocardial Infarction: A Systematic Review and Meta-Analysis

Abstract

Myocardial infarction (MI), a leading cause of morbidity and mortality globally, is characterized by an underlying inflammatory process driven by atherosclerosis. The neutrophil-to-lymphocyte ratio (NLR), a readily available and cost-effective marker of systemic inflammation, has emerged as a potential predictor of adverse outcomes in patients with MI. This meta-analysis aimed to evaluate the association between elevated NLR and the risk of major adverse cardiovascular events (MACE) and all-cause mortality in patients with MI. A comprehensive literature search was conducted across multiple databases, including Embase, Web of Science, PubMed, and OVID Medicine, to identify relevant studies published from January 1, 2011, onward. Studies reporting the effect of NLR values on MACE and mortality in adult patients with MI, including both ST-elevation (STEMI) and non-ST-elevation (NSTEMI) subtypes, were included. Data extraction and quality assessment were performed independently by multiple authors. The meta-analysis included 37 studies, comprising a total of 18 studies evaluating the risk of MACE and 30 studies assessing all-cause mortality. The pooled analysis revealed a significantly increased risk of MACE (odds ratio [OR] 1.86, 95% confidence interval [CI] 1.53-2.28, P < 0.01) and all-cause mortality (OR 2.29, 95% CI 1.94-2.70, P < 0.01) in patients with elevated NLR compared to those without elevated NLR. Subgroup analyses stratified by follow-up duration and study design further supported the consistent association between elevated NLR and adverse outcomes. In conclusion, this meta-analysis demonstrates a significant association between elevated NLR and an increased risk of MACE and all-cause mortality in patients with MI. These findings highlight the potential clinical utility of NLR as a prognostic marker and underscore the importance of further research to validate its predictive value and establish optimal cutoff values for risk stratification in this patient population.