Unveiling Novel Insights into Myocardial Infarction Complications: A Meta-Analysis and Statistical Synthesis of Recent Literature

Abstract

Myocardial Infarction (MI) remains a leading cause of global morbidity and mortality, with its complications ranging from heart failure and arrhythmias to cardiogenic shock posing significant challenges to patient recovery and long-term cardiovascular health. Despite advances in clinical management, the evolving nature of MI complications, especially in the context of comorbidities and emerging biomarkers, necessitates an updated synthesis of recent evidence. This study presents a comprehensive meta-analytic evaluation of myocardial infarction complications using data from 11 primary studies published between 2019 and 2025. Data sources included PubMed, Scopus, Web of Science, and Google Scholar. Studies were selected based on strict inclusion criteria: English-language, peer-reviewed primary research with statistical or machine-learning analyses focused on MI-related complications. The synthesis applied fixed- or random-effects models depending on heterogeneity levels, with forest plots and I² statistics employed to evaluate achieved outcomes. Key findings reveal persistent trends in complication prevalence, particularly among diabetic and COVID-affected subgroups, and highlight novel risk markers such as long non-coding RNAs and myeloproliferative neoplasms. Furthermore, the integration of Artificial Intelligence (AI) in early complication detection and prediction represents a pivotal advancement in cardiology. Theoretically, this study contributes to understanding gene-environment interactions in cardiac pathology. Practically, it underscores the need for AI integration, personalized monitoring, and pharmacogenomic tools in clinical settings. These insights advocate for policy shifts, enhanced surveillance systems, and future longitudinal studies to improve outcomes and personalize care for post-MI patients.