The Lead Episode 67: A Discussion of Optimizing Patient Selection for Primary Prevention Implantable Cardioverter-Defibrillator Implantation: Utilizing Multimodal Machine Learning to Assess Risk of Implantable Cardioverter-Defibrillator Non-Benefit

Discussion Topic: Optimizing Patient Selection for Primary Prevention Implantable Cardioverter-Defibrillator Implantation: Utilizing Multimodal Machine Learning to Assess Risk of Implantable Cardioverter-Defibrillator Non-Benefit

Deepthy Varghese, MSN, ACNP, FNP, Northside Hospital is joined by Tina Baykaner, MD, MPH Stanford University, and Gurukripa N Kowlgi, MBBS, MSci,  Mayo Clinic–Rochester to discuss; the multicenter study investigated the potential of machine learning (ML) models to improve risk stratification for implantable cardioverter-defibrillator (ICD) implantation in patients at risk of sudden cardiac death (SCD). By combining clinical variables with 12-lead electrocardiogram (ECG) time-series features, the models aimed to predict non-arrhythmic mortality within three years after device implantation. Results showed that ML models identified patients at risk with high accuracy, demonstrating robust performance in both the development and external validation cohorts. This suggests that ML-based approaches could enhance risk assessment for SCD prevention in primary prevention populations.

Host: Deepthy Varghese, MSN, ACNP, FNP
Guests: Tina Baykaner, MD, MPH and Gurukripa N Kowlgi, MBBS, MSci

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