Related Experiment Video
Updated: Oct 5, 2025
In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with
Ryan P O'Hara1, Edem Binka2, Adityo Prakosa1
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States.
Insights
Hypertrophic cardiomyopathy (HCM) patients at risk of sudden cardiac death can be better identified using computational heart models. This approach analyzes fibrosis to predict ventricular arrhythmias (VA) and improve prevention strategies.
Area of Science:
- Cardiology
- Computational Biology
- Medical Imaging
Background:
- Hypertrophic cardiomyopathy (HCM) increases sudden cardiac death (SCD) risk due to ventricular arrhythmias (VA).
- Current clinical risk stratification methods are insufficient for identifying patients needing primary VA prevention.
- Fibrosis proliferation in the heart is a key factor in HCM-related arrhythmogenesis.
Purpose of the Study:
- To analyze how HCM-specific cardiac remodeling promotes arrhythmogenesis using computational modeling.
- To develop a personalized strategy for forecasting VA risk in HCM patients.
- To evaluate the role of diffuse fibrosis in VA propensity.
Main Methods:
- Utilized mechanistic computational modeling of the heart.
- Combined contrast-enhanced cardiac magnetic resonance imaging and T1 mapping data.
- Constructed patient-specific digital heart replicas incorporating focal and diffuse fibrosis distribution.
Main Results:
- Diffuse fibrosis, often unassessed, significantly increases arrhythmogenic propensity in HCM.
- The imaging-based computational heart approach achieved 84.6% sensitivity, 76.9% specificity, and 80.1% accuracy in forecasting VA events.
- This novel approach outperformed existing clinical risk predictors for VA events.
Conclusions:
- Computational modeling integrating fibrosis imaging offers a personalized strategy for VA risk assessment in HCM.
- This method has the potential to prevent SCD by enabling appropriate deployment of primary VA prevention.
- Accurate identification of VA risk is crucial for managing HCM patients.
Abstract:
Hypertrophic cardiomyopathy (HCM) is associated with risk of sudden cardiac death (SCD) due to ventricular arrhythmias (VAs) arising from the proliferation of fibrosis in the heart. Current clinical risk stratification criteria inadequately identify at-risk patients in need of primary prevention of VA. Here, we use mechanistic computational modeling of the heart to analyze how HCM-specific remodeling promotes arrhythmogenesis and to develop a personalized strategy to forecast risk of VAs in these patients. We combine contrast-enhanced cardiac magnetic resonance imaging and T1 mapping data to construct digital replicas of HCM patient hearts that represent the patient-specific distribution of focal and diffuse fibrosis and evaluate the substrate propensity to VA. Our analysis indicates that the presence of diffuse fibrosis, which is rarely assessed in these patients, increases arrhythmogenic propensity. In forecasting future VA events in HCM patients, the imaging-based computational heart approach achieved 84.6%, 76.9%, and 80.1% sensitivity, specificity, and accuracy, respectively, and significantly outperformed current clinical risk predictors. This novel VA risk assessment may have the potential to prevent SCD and help deploy primary prevention appropriately in HCM patients.
