Cardiac MRI Workflow Improves Prediction of Sudden Cardiac Death in Dilated Cardiomyopathy: Study Shows
China: A new cardiac MRI-based workflow has shown promise in improving the prediction of sudden cardiac death (SCD) among individuals with nonischemic dilated cardiomyopathy (DCM), offering clinicians refined tools for risk stratification and personalized treatment strategies. By establishing specific cutoff values for late gadolinium enhancement (LGE) and extracellular volume fraction (ECV), the study demonstrates a more precise approach to identifying both high-risk and low-risk patient groups.
The findings were published on September 9 in Radiology by Di Zhou, MD, and colleagues from the Department of Magnetic Resonance Imaging, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. Their work highlights how quantitative cardiac MRI markers can outperform traditional visual assessments when estimating the risk of SCD in patients with DCM.
Dilated cardiomyopathy leads to enlargement of the heart chambers and, without timely management, can result in sudden death. While cardiac MRI with LGE is already recognized as a marker of myocardial scarring, its clinical use has been constrained by qualitative interpretation, especially in cases where LGE appears with intermediate signal intensity. To address this gap, the research team investigated whether quantitative thresholds could offer greater prognostic accuracy.
The study analyzed data from 1,105 patients with DCM who underwent cardiac MRI across four centers. The development cohort included 837 individuals enrolled between 2012 and 2019, while an external validation cohort of 268 patients was added from 2019 to 2021. Patients were followed for a median of 58.3 months, with primary endpoints including SCD, implantable cardioverter-defibrillator (ICD) shocks, and resuscitated cardiac arrest. Secondary endpoints consisted of death related to heart failure, heart transplantation, or the use of a ventricular assist device.
The study revealed the following findings:
- An LGE extent of at least 7.2% of left ventricular mass was identified as a strong and independent predictor of sudden cardiac death (SCD)-related events, with a hazard ratio of 4.75.
- This quantitative cutoff offered superior predictive performance compared to qualitative distribution assessments (C statistic: 0.783 vs. 0.732).
- In patients with LGE below 7.2%, an extracellular volume fraction (ECV) threshold of 31.8% provided additional discriminatory value.
- Individuals with preserved ejection fraction (>35%), LGE less than 7.2%, and ECV under 31.8% showed a very low annual SCD event rate of 0.2%.
- Patients with LGE ≥7.2% demonstrated a significantly higher annual risk of 4.65%, independent of ECV or LGE distribution pattern.
By incorporating LGE, ECV, and ejection fraction into a single workflow, the investigators proposed a practical model for clinicians to tailor preventive interventions. “The proposed comprehensive workflow empowers clinicians to implement personalized strategies, which may ultimately lead to improved patient outcomes in this vulnerable cohort,” Zhou and colleagues wrote.
In an editorial accompanying the study, Hajime Sakuma, MD, PhD, of Mie University in Japan, emphasized the clinical value of these thresholds. He noted that they can help physicians identify not only patients who require aggressive intervention, such as ICD implantation, but also those for whom such measures may be unnecessary. Sakuma added that the straightforward design of the workflow makes it feasible for real-world practice.
The study’s insights advance the role of cardiac MRI in guiding precision care for nonischemic DCM, bridging the gap between imaging biomarkers and individualized patient management.
Reference:
Risk Stratification of Sudden Cardiac Death in Nonischemic Dilated Cardiomyopathy: Arrhythmogenic Substrate Assessment in Cardiac MRI Di Zhou, Huaying Zhang, Wenjing Yang, Yining Wang, Leyi Zhu, Mengdi Jiang, Jing Xu, Fei Teng, Xinxiang Zhao, Shaocheng Zhu, Doudou Liu, Qiang Zhang, Arlene Sirajuddin, Andrew E. Arai, Shihua Zhao, and Minjie Lu. Radiology 2025 316:3
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