The Role of Late Gadolinium Enhancement on Cardiac MRI in Predicting Arrhythmic Events in Non-Ischemic Cardiomyopathy: A Meta-Analysis
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.057 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".