Prediction of ventricular arrhythmias and sudden cardiac death by quantification and location of late gadolinium enhancement on cardiac magnetic resonance: a systematic review and meta-analysis
Bibliographic record
Abstract
AIMS: In non-ischaemic cardiomyopathy (NICM), late gadolinium enhancement (LGE) detected by cardiovascular magnetic resonance is related to ventricular arrhythmia (VA) and sudden cardiac death (SCD) risk. The incremental prognostic value of quantifying LGE volume or mass beyond its mere presence, however, remains unresolved. The aim was to evaluate whether LGE quantification improves the prediction of SCD. METHODS AND RESULTS: PubMed, Embase, and Web of Science were searched on 20 November 2024 for observational studies that related quantified LGE burden to ventricular arrhythmia (VA)/SCD in NICM. Forty-one studies met prespecified criteria. Hazard ratios (HRs) were pooled with random-effects models, and quantification information was depicted in figures. Presence of any LGE was associated with a three-fold increase in VA/SCD risk (pooled HR 3.31, 95% confidence interval: 2.58-4.24). Beyond this binary marker, every additional 1% (or 1 g) of LGE was associated with a 12% relative risk increase (range 10-20%), independent of left ventricular ejection fraction and consistent across eight semi-automated thresholding techniques. This included 2-6 standard deviations above the reference myocardium and the full-width half-maximum method. Additionally, results were prone to substantial methodological heterogeneity (τ² = 1.49) and small-study bias. Once the presence of LGE was accounted for, scar quantification and location conferred minimal additional prognostic value. CONCLUSION: Quantitative LGE assessment provides little incremental prognostic utility over dichotomous LGE detection. Consensus imaging standards and prospective validation are requisite before LGE burden can guide primary implantable cardioverter defibrillator allocation in NICM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".