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Updated: Jan 9, 2026
Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
Published on: February 12, 2011
A machine Learning-Based risk stratification using CMR volumetric and strain parameters in patients with hypertrophic
Hoyoung Kim1, Meen Joo Song1, Jihoon Kim2
1Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Insights
Machine learning models integrating cardiac MRI (CMR) and left atrial (LA) strain improve hypertrophic cardiomyopathy (HCM) risk prediction. LA strain, particularly conduit function, emerged as a key predictor for long-term outcomes in HCM patients.
Area of Science:
- Cardiology
- Medical Imaging
- Machine Learning
Background:
- Hypertrophic cardiomyopathy (HCM) is an inherited heart muscle disease with variable prognosis, often involving sudden cardiac death, atrial fibrillation (AF), and stroke.
- Accurate long-term risk stratification in HCM is challenging, as traditional clinical and echocardiographic markers may not fully capture disease complexity.
- Left atrial (LA) function is increasingly recognized as crucial for cardiac performance and prognosis.
Purpose of the Study:
- To develop and evaluate a machine learning model integrating clinical, echocardiographic, and cardiac magnetic resonance imaging (CMR) parameters for improved prognostic risk stratification in HCM patients.
- To assess the predictive performance of CMR-derived parameters, particularly LA strain, compared to conventional markers.
Main Methods:
- A cohort of 223 HCM patients with comprehensive demographic, clinical, echocardiographic, and CMR data was analyzed.
- Five machine learning methodologies were compared for their ability to predict a composite clinical outcome (all-cause mortality, ICD shock, HF hospitalization, new AF, ischemic stroke).
- Penalized logistic regression incorporating LA strain parameters was evaluated using AUC and C-index.
Main Results:
- Machine learning models incorporating CMR-derived parameters significantly outperformed those using only clinical and echocardiographic data.
- The penalized logistic regression model integrating clinical, echocardiographic, and LA strain parameters achieved the highest predictive performance (AUC = 0.840, C-index = 0.795).
- This model effectively stratified patients into low (0%), intermediate (27.3%), and high-risk (77.8%) groups for adverse outcomes. LA strain related to conduit function was identified as the most significant predictor.
Conclusions:
- CMR-derived parameters, especially LA strain, provide significant supplementary predictive value for long-term risk stratification in HCM.
- Machine learning approaches effectively integrate diverse data types for enhanced prognostic assessment in HCM.
- LA strain analysis offers a robust tool for identifying high-risk individuals with HCM.
Abstract:
Hypertrophic cardiomyopathy (HCM) is an inherited myocardial disorder associated with sudden cardiac death, atrial fibrillation (AF), and stroke, with prognosis varying widely among patients. Accurate long-term risk prediction requires comprehensive assessment of cardiac function, including left atrial performance, but conventional clinical and echocardiographic markers may not fully reflect the complexity of disease progression. This study aimed to develop a machine learning-based model that integrates clinical, echocardiographic, and CMR-derived parameter to improve prognostic risk stratification in patients with HCM. Between June 2008 and January 2016, 223 patients with complete demographic, clinical, echocardiographic, and CMR data were enrolled in the analysis. The composite of clinical outcome included of all-cause mortality, implantable cardioverter defibrillator shock, heart failure-related hospitalization, new-onset AF, and new-onset ischemic stroke. Five machine learning methodologies were evaluated. Models incorporating CMR-derived parameters outperformed those based solely on clinical and echocardiographic data. Among all models, penalized logistic regression integrating clinical, echocardiographic, and left atrial (LA) strain parameters demonstrated the highest predictive performance (AUC = 0.840, C-index = 0.795), and effectively stratified the long-term risk of outcome (low-risk group: 0%, intermediate-risk group: 27.3%, high-risk group: 77.8%, p = 0.002). Additionally, the LA strain model showed robust predictive performance across individual outcome components. Shapley additive explanations (SHAP) value analysis identified LA strains related to conduit function as the most significant predictor. CMR-derived parameters provide supplementary predictive impact in patients with HCM, with LA strain consistently outperforming other variables. Machine learning-based methodologies can effectively incorporate multiple variables and offer an effective approach for long-term risk stratification.
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