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Prediction model for early left ventricular systolic dysfunction progression in hypertrophic cardiomyopathy
Yu Li1, Ziqi Duan2, Jinlei Li1
1Department of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Insights
This study developed a predictive model to assess the risk of early left ventricular systolic dysfunction progression (ELVSDP) in hypertrophic cardiomyopathy (HCM) patients. The model accurately predicts ELVSDP, aiding in early risk stratification and personalized management.
Area of Science:
- Cardiology
- Medical Prediction Models
- Biostatistics
Background:
- Hypertrophic cardiomyopathy (HCM) patients exhibit heterogeneous risk for early left ventricular systolic dysfunction progression (ELVSDP).
- Current tools for predicting short- and medium-term ELVSDP in HCM are insufficient.
- Accurate prediction is crucial for timely intervention and management.
Purpose of the Study:
- To develop and validate a predictive model for ELVSDP risk in HCM patients.
- To establish prediction accuracy at 6, 12, and 18 months post-evaluation.
- To facilitate early risk stratification and individualized patient management.
Main Methods:
- A cohort of 314 HCM patients without baseline ELVSDP was analyzed.
- LASSO-Cox regression identified independent predictors of ELVSDP.
- A nomogram and interactive dynamic prediction tool were constructed and validated.
Main Results:
- Age, smoking history, BNP levels, and LVOTO were significant predictors of ELVSDP.
- The predictive model demonstrated high performance (AUC > 0.88, C-index ~0.94).
- The model showed good calibration and clinical utility, with higher ELVSDP incidence in the high-risk group.
Conclusions:
- A validated nomogram accurately predicts short-term ELVSDP risk in HCM patients.
- The model supports early risk stratification and personalized treatment strategies.
- This tool can improve management of HCM patients at risk for ELVSDP.
Background:
The risk of early left ventricular systolic dysfunction progression (ELVSDP) among patients with hypertrophic cardiomyopathy (HCM) is heterogeneous, and accurate short-and medium-term prediction tools are lacking. This study aimed to develop and validate a predictive model for the risk of ELVSDP in HCM patients at 6, 12, and 18 months.
Methods:
A total of 314 HCM patients without ELVSDP at baseline were included and randomly divided into training and validation sets. LASSO-Cox regression was employed to select variables and identify independent predictors, based on which a nomogram and an interactive dynamic prediction tool were constructed. Model performance was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Risk stratification was assessed using the Kaplan-Meier method.
Results:
Age [hazard ratio (HR) = 1.17], smoking history (HR = 2.79), B-type natriuretic peptide (BNP) level (HR = 1.002), and left ventricular outflow tract obstruction (HR = 2.24) were independent predictors of ELVSDP. The model demonstrated strong performance in both the training and validation sets. The time-dependent area under the curve (AUC) exceeded 0.88 at 6, 12, and 18 months, with C-indices of 0.94 and 0.93, respectively. Bootstrap validation confirmed model stability. Calibration curves showed good agreement between predicted and observed outcomes, and DCA indicated a net clinical benefit. The incidence of ELVSDP was significantly higher in the high-risk group compared to the low-risk group (P < 0.0001).
Conclusion:
The nomogram developed in this study accurately predicts short-term ELVSDP risk in HCM patients, facilitating early risk stratification and individualized management.
Clinical Trial Registration:
This study was registered at the Chinese Clinical Trial Registry http://www.chictr.org.cn/, registration number ChiCTR2500106648 [Registration Date: 2025-07-28].
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