The Role of Late Gadolinium Enhancement on Cardiac MRI in Predicting Arrhythmic Events in Non-Ischemic Cardiomyopathy: A Meta-Analysis
DOI:
https://doi.org/10.70749/ijbr.v3i4.1109Keywords:
Pinworm Infection, Children, Prevalence, Mardan DistrictAbstract
Background: Non-ischemic cardiomyopathy (NICM) is a major cause of heart failure and sudden cardiac death (SCD), with significant heterogeneity in arrhythmic risk. While left ventricular ejection fraction (LVEF) has traditionally been used for risk stratification, it fails to capture all high-risk individuals. Late gadolinium enhancement (LGE) detected on cardiac magnetic resonance imaging (MRI) has emerged as a promising marker of myocardial fibrosis and arrhythmic vulnerability in NICM patients. Objective: This meta-analysis aims to evaluate the prognostic value of LGE on cardiac MRI in predicting arrhythmic events in patients with non-ischemic cardiomyopathy. Methods: A systematic search of PubMed, Embase, Web of Science, and Scopus databases was conducted through April 2024. Studies were eligible if they enrolled NICM patients, assessed LGE using cardiac MRI, and reported arrhythmic outcomes such as SCD or appropriate implantable cardioverter-defibrillator (ICD) therapy. Hazard ratios (HRs) were pooled using a random-effects model. Risk of bias was assessed using the Newcastle-Ottawa Scale (NOS). Results: Five high-quality cohort studies comprising 1,315 patients were included. LGE prevalence ranged from 29% to 48%, with follow-up durations between 2.3 and 5.3 years. The pooled analysis demonstrated that LGE was significantly associated with arrhythmic events, with a combined hazard ratio (HR) of 2.7 (95% CI: 1.94–3.75). No significant heterogeneity was observed (I² = 0%). All included studies showed a consistent direction of effect, reinforcing the predictive value of LGE for adverse arrhythmic outcomes. Conclusion: LGE on cardiac MRI is a strong and independent predictor of arrhythmic events in patients with NICM. Incorporating LGE assessment into clinical decision-making may enhance risk stratification, guide ICD therapy, and ultimately improve patient outcomes.
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