To develop and validate a predictive model for the risk of early left ventricular systolic dysfunction progression (ELVSDP) in patients with hypertrophic cardiomyopathy (HCM) at 6, 12, and 18 months, emphasizing its clinical significance.
Approach:
Key Findings:
Independent predictors of ELVSDP included age (HR = 1.17), smoking history (HR = 2.79), BNP level (HR = 1.002), and left ventricular outflow tract obstruction (HR = 2.24), highlighting their clinical significance.
The model showed strong performance with time-dependent AUC exceeding 0.88 at 6, 12, and 18 months, and C-indices of 0.94 and 0.93.
Bootstrap validation confirmed model stability, and calibration curves indicated good agreement between predicted and observed outcomes.
Interpretation:
The nomogram accurately predicts short-term ELVSDP risk in HCM patients, facilitating early risk stratification and individualized management, which can significantly impact clinical decision-making.
Limitations:
Subjectivity in symptom reporting may affect the classification of ELVSDP.
Measurement variability in LVEF via echocardiography could influence results.
The model's performance is contingent upon the definition of ELVSDP, which may vary, and potential biases inherent in retrospective studies.
Conclusion:
The developed nomogram provides a quantitative and visual tool for individualized risk management of HCM patients, enabling early intervention and suggesting avenues for future research.
