Arrhythmic Risk Stratification in Non-Ischaemic Cardiomyopathy: The Role of Cardiac Magnetic Resonance Imaging

Q: What is the clinical evidence from this study?

Study design: Review. Population: Non-ischaemic cardiomyopathy (specifically dilated cardiomyopathy and non-dilated left ventricular cardiomyopathy). Intervention: Cardiac magnetic resonance imaging (LGE, T1/ECV parametric mapping, strain imaging, and AI) vs. Left ventricular ejection fraction (LVEF)-based risk stratification or absence of myocardial fibrosis. Primary outcome: Ventricular arrhythmias and sudden cardiac death.

Q: What does this research mean for the field?

Late gadolinium enhancement on cardiac magnetic resonance imaging strongly predicts ventricular arrhythmias and sudden cardiac death in non-ischaemic cardiomyopathy, while its absence reliably identifies a low-risk cohort. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

Ryan Isaac Sia Zu Wern; Daniel J. Hammersley

doi:10.1007/s11886-026-02435-8

October 23, 2026Current Cardiology ReportsOpen Access

Arrhythmic Risk Stratification in Non-Ischaemic Cardiomyopathy: The Role of Cardiac Magnetic Resonance Imaging

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Key result

Late gadolinium enhancement on CMR strongly predicts ventricular arrhythmias and sudden cardiac death (HR 2.69) in non-ischaemic cardiomyopathy, while its absence has a 98% negative predictive value.

Why the study?

Left ventricular ejection fraction has been the cornerstone for guideline-based arrhythmic risk stratification in non-ischaemic cardiomyopathy despite limited precision, prompting evaluation of the evolving role of cardiac magnetic resonance imaging.

Does cardiac magnetic resonance imaging (including LGE, parametric mapping, strain imaging, and AI) improve arrhythmic risk stratification for ventricular arrhythmias and sudden cardiac death compared to LVEF-based assessment in patients with non-ischaemic cardiomyopathy?

Key Points

To review the evolving role of cardiac magnetic resonance imaging, including late gadolinium enhancement, parametric mapping, strain imaging, and artificial intelligence, in arrhythmic risk stratification for non-ischaemic cardiomyopathy.
Synthesized observational cohorts, multicentre investigations, and meta-analyses evaluating cardiac magnetic resonance parameters in dilated cardiomyopathy and non-dilated left ventricular cardiomyopathy.
Assessed replacement fibrosis via late gadolinium enhancement, interstitial fibrosis via native T1 mapping and extracellular volume fraction, myocardial deformation via strain imaging, and emerging multimodal artificial intelligence algorithms.
Late gadolinium enhancement absence identified low-risk cohorts, whereas its presence independently predicted ventricular arrhythmias and sudden cardiac death, even among patients with mild-to-moderate left ventricular systolic dysfunction.
In a meta-analysis, native T1 mapping was independently associated with ventricular arrhythmias (pooled HR 1.09, 95% CI 1.07–1.12 per 10 ms increase) and major adverse cardiovascular events (HR 1.07, 95% CI 1.04–1.09 per 10 ms increase; ECV: HR 1.37, 95% CI 1.29–1.44 per 1% absolute increase).
In 618 patients with non-ischaemic cardiomyopathy, an extracellular volume fraction ≥ 30% conferred a 14.1-fold increased risk of ventricular arrhythmias after adjusting for late gadolinium enhancement and ejection fraction ≤ 35%, reclassifying 32% of patients.

Structured PICO

Does cardiac magnetic resonance imaging (including LGE, parametric mapping, strain imaging, and AI) improve arrhythmic risk stratification for ventricular arrhythmias and sudden cardiac death compared to LVEF-based assessment in patients with non-ischaemic cardiomyopathy?

P

Population

Patients with non-ischaemic cardiomyopathy, specifically dilated cardiomyopathy and non-dilated left ventricular cardiomyopathy, undergoing cardiac magnetic resonance imaging for arrhythmic risk stratification.

E

Exposure

Cardiac magnetic resonance (CMR) imaging modalities, including late gadolinium enhancement (LGE), parametric mapping (native T1 and extracellular volume [ECV]), left ventricular strain imaging, artificial intelligence (AI), and digital twins

C

Comparator

Traditional guideline-based risk stratification using left ventricular ejection fraction (LVEF <=35%) and New York Heart Association (NYHA) class

O

Outcome

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD)hard clinical

This review highlights the transition in non-ischaemic cardiomyopathy arrhythmic risk stratification from an imprecise LVEF-based approach to an integrated, substrate-driven precision model anchored by cardiac magnetic resonance imaging (LGE, parametric mapping), genetics, and emerging AI tools.

Limitations

CMR is resource-intensive, requiring costly infrastructure, specialised scanning protocols, and trained clinicians, often restricting access to tertiary academic centres.
Evidence linking CMR-defined fibrosis to sudden cardiac death is predominantly derived from retrospective and prospective observational cohorts, with a critical absence of completed large-scale RCT data.
Lack of consensus regarding the standardisation of fibrosis quantification methodologies introduces substantial inter-observer, software, and inter-vendor variability.
Native T1 values are strongly dependent on magnetic field strength, mapping sequence, vendor, and acquisition parameters, limiting external generalisability across centres.
Lack of uniformity in scar quantification across different magnetic field strengths, contrast agents, and software platforms
Lack of research into the diagnostic and prognostic value of LGE in patients with mild DCM (LVEF > 40%) or recovered/improved DCM (HFimpEF)
Limited external generalisability of native T1 mapping due to dependence on magnetic field strength, mapping sequence, vendor, and acquisition parameters
Paucity of literature evaluating T2 mapping and lack of data supporting LV global longitudinal strain (GLS) for arrhythmic risk stratification in DCM and NDLVC
Conventional CMR analysis is constrained by inter-observer variability, time-intensive post-processing demands, and limitations of human visual assessment

Implication

LGE-CMR may refine arrhythmic risk stratification beyond LVEF in non-ischaemic cardiomyopathy; leaves open prospective validation before guideline adoption.

Abstract

Purpose of Review This review aims to evaluate the contemporary and evolving role of cardiac magnetic resonance (CMR) imaging in arrhythmic risk stratification for patients with non-ischaemic cardiomyopathy, specifically focussing on dilated cardiomyopathy (DCM) and non-dilated left ventricular cardiomyopathy (NDLVC). This review synthesises current data on late gadolinium enhancement (LGE), parametric mapping, strain imaging and artificial intelligence (AI), and contextualises these among emerging genetic risk stratification approaches. Recent Findings Left ventricular ejection fraction has been the cornerstone for guideline-based arrhythmic risk stratification in patients with DCM, despite limited precision. Observational data and meta-analyses have established the presence of replacement fibrosis through LGE-CMR as a powerful independent predictor of ventricular arrhythmias (VAs) and sudden cardiac death. Crucially, the absence of LGE identifies a low-risk cohort. Much recent focus has been on understanding whether more precise characterisation of scar can further enhance arrhythmic risk stratification, for example through scar quantification. Although data exists supporting a potential role for T1 mapping for arrhythmic risk prediction in this cohort, further research is required to support its widespread adoption. Emerging multimodal approaches using artificial intelligence and personalised computational “digital twins” are areas of important interest, offering scope for integration of imaging, electrocardiographic and genomic data layers. Summary Arrhythmic risk stratification in non-ischaemic cardiomyopathies remains challenging despite significant advances. Progressive refinement in precision has accompanied a shift from a restrictive, functional assessment towards an integrated, substrate-driven precision model. CMR represents the cornerstone of this evolution.

Discussion

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Cite This Study

Wern et al. (2026) conducted a review in Non-ischaemic cardiomyopathy (specifically dilated cardiomyopathy and non-dilated left ventricular cardiomyopathy). Cardiac magnetic resonance imaging (LGE, T1/ECV parametric mapping, strain imaging, and AI) vs. Left ventricular ejection fraction (LVEF)-based risk stratification or absence of myocardial fibrosis was evaluated on Ventricular arrhythmias and sudden cardiac death. Late gadolinium enhancement on CMR strongly predicts ventricular arrhythmias and sudden cardiac death (HR 2.69) in non-ischaemic cardiomyopathy, while its absence has a 98% negative predictive value.

synapsesocial.com/papers/6adc966ca704d151585a5e87 https://doi.org/10.1007/s11886-026-02435-8

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Arrhythmic Risk Stratification in Non-Ischaemic Cardiomyopathy: The Role of Cardiac Magnetic Resonance Imaging

Key Points

Structured PICO

Limitations

Implication

Abstract

Discussion

Cite This Study

Arrhythmic Risk Stratification in Non-Ischaemic Cardiomyopathy: The Role of Cardiac Magnetic Resonance Imaging

Key Points

Structured PICO

Limitations

Implication

Abstract

Discussion

Cite This Study

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