Improving Risk Stratification in Sudden Cardiac Death Using Interpretable Machine Learning: A Clinical Perspective.

Area of Science:

• Cardiology
• Medical Informatics
• Machine Learning

Background:

• Sudden cardiac death (SCD) is a significant cause of cardiovascular mortality.
• Current implantable cardioverter-defibrillator (ICD) selection lacks precision, leading to suboptimal patient outcomes.
• Interpretable machine learning (ML) offers a potential solution for improved risk stratification.

Purpose of the Study:

• To develop and validate an interpretable ML model for predicting appropriate ICD activation.
• To refine patient selection for ICD implantation using diverse clinical data.
• To enhance the transparency of clinical reasoning in SCD risk assessment.

Main Methods:

• Retrospective analysis of 607 patients undergoing ICD or CRT-D implantation.
• Collection of comprehensive baseline data: demographic, clinical, echocardiographic, laboratory, and device-related variables.
• Development of a logistic regression (LR) model to predict appropriate ICD activation, using patient follow-up data.

Main Results:

• The LR model achieved strong predictive performance (AUC-ROC 0.74, sensitivity 86.50%).
• Key predictors identified include ventricular tachycardia (VT) burden, sustained VT, longer follow-up, and secondary prevention.
• Combination therapy with furosemide and spironolactone was associated with lower predicted SCD risk.

Conclusions:

• ML applied to routine clinical data can significantly enhance SCD risk stratification.
• This approach complements existing guideline criteria by confirming known predictors and identifying novel associations.
• Interpretable ML models can support clinical decision-making for ICD implantation, improving patient selection and outcomes.