Machine Learning in Hypertrophic Cardiomyopathy: Nonlinear Model From Clinical and CMR Features
Kankan Zhao1, Yanjie Zhu2, Xiuyu Chen3
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, China.
Insights
Machine learning accurately predicts major adverse cardiovascular events (MACEs) in hypertrophic cardiomyopathy (HCM) patients using cardiac magnetic resonance (CMR) imaging and clinical data. This approach surpasses traditional risk models, offering improved risk stratification for HCM management.
Area of Science:
- Cardiology
- Medical Imaging
- Machine Learning
Background:
- Hypertrophic cardiomyopathy (HCM) poses a significant clinical burden, with 10-15% of patients experiencing major adverse cardiovascular events (MACEs) annually.
- Existing risk stratification models for HCM have limited accuracy in predicting sudden cardiac death (SCD) and do not incorporate cardiac magnetic resonance (CMR) parameters.
Purpose of the Study:
- To develop and validate a machine learning (ML) framework for predicting MACEs in HCM patients.
- Integrate CMR imaging features and clinical characteristics into an ML model for enhanced risk prediction.
Main Methods:
- Utilized data from 758 HCM patients across four medical centers (2010-2017).
- Developed an ML model using light gradient-boosting machine on an internal cohort (533 patients) and validated it externally on 225 patients.
- Incorporated 14 CMR imaging features (including strain and late gadolinium enhancement [LGE]) and 23 clinical variables.
Main Results:
- The ML model achieved areas under the curve (AUCs) of 0.830 (internal) and 0.812 (external), outperforming the classic HCM Risk-SCD model by 22.7% (P < 0.001).
- MACEs occurred in 25% of patients over a median follow-up of 109 months.
- Nonlinear correlations were found between LGE extent, global radial strain (GRS), global circumferential strain (GCS), and MACEs.
Conclusions:
- ML-based risk stratification integrating CMR and clinical data provides accurate MACE prediction in HCM, exceeding traditional models.
- Identified nonlinear relationships between CMR features (LGE, left ventricular pressure gradient) and MACEs, offering valuable clinical insights for HCM assessment and management.
Background:
The cumulative burden of hypertrophic cardiomyopathy (HCM) is significant, with a noteworthy percentage (10%-15%) of patients with HCM per year experiencing major adverse cardiovascular events (MACEs). A current risk stratification scheme for HCM had only limited accuracy in predicting sudden cardiac death (SCD) and failed to account for a broader spectrum of adverse cardiovascular events and cardiac magnetic resonance (CMR) parameters.
Objectives:
This study sought to develop and evaluate a machine learning (ML) framework that integrates CMR imaging and clinical characteristics to predict MACEs in patients with HCM.
Methods:
A total of 758 patients with HCM (67% male; age 49 ± 14 years) who were admitted between 2010 and 2017 from 4 medical centers were included. The ML model was built on the internal discovery cohort (533 patients with HCM, admitted to Fuwai Hospital, Beijing, China) by using the light gradient-boosting machine and internally evaluated using cross-validation. The external test cohort consisted of 225 patients with HCM from 3 medical centers. A total of 14 CMR imaging features (strain and late gadolinium enhancement [LGE]) and 23 clinical variables were evaluated and used to inform the ML model. MACEs included a composite of arrhythmic events, SCD, heart failure, and atrial fibrillation-related stroke.
Results:
MACEs occurred in 191 (25%) patients over a median follow-up period of 109.0 months (Q1-Q3: 73.0-118.8 months). Our ML model achieved areas under the curve (AUCs) of 0.830 and 0.812 (internally and externally, respectively). The model outperformed the classic HCM Risk-SCD model, with significant improvement (P < 0.001) of 22.7% in the AUC. Using the cubic spline analysis, the study showed that the extent of LGE and the impairment of global radial strain (GRS) and global circumferential strain (GCS) were nonlinearly correlated with MACEs: an elevated risk of adverse cardiovascular events was observed when these parameters reached the high enough second tertiles (11.6% for LGE, 25.8% for GRS, -17.3% for GCS).
Conclusions:
ML-empowered risk stratification using CMR and clinical features enabled accurate MACE prediction beyond the classic HCM Risk-SCD model. In addition, the nonlinear correlation between CMR features (LGE and left ventricular pressure gradient) and MACEs uncovered in this study provides valuable insights for the clinical assessment and management of HCM.
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