Prediction model for early left ventricular systolic dysfunction progression in hypertrophic cardiomyopathy

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.