The Lead Episode 67: Optimizing patient selection for primary prevention implantable cardioverter-defibrillator implantation-The Lead Episode 67 Speaker Information

The Lead Episode 67 Speaker Information

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The article discussed in the session revolves around optimizing patient selection for implantable cardioverter-defibrillator (ICD) implantation for primary prevention. The focus is on utilizing multimodal machine learning to evaluate the risk of ICD non-benefit. The authors of the article include Maarten Z H Kolk, Samuel Ruipérez-Campillo, Brototo Deb, and other medical professionals. The discussion was hosted by Deepthy Varghese, who is associated with Northside Hospital, with contributions from Tina Baykaner from Stanford University and Gurukripa N Kowlgi from Mayo Clinic-Rochester.<br /><br />Dr. Baykaner has disclosed honoraria, speaking engagements, and consulting work with Medtronic Inc. and Pacemate, as well as research funding from NIH. On the other hand, Dr. Kowlgi has stated that he has nothing to disclose. The host, Deepthy Varghese, also declared no disclosures.<br /><br />The session aimed to explore advanced methods of assessing the suitability of patients for ICD implantation to ensure that the treatment offers a benefit to the individuals receiving it. By incorporating machine learning techniques and evaluating various factors, including patient characteristics and medical history, the goal is to refine the patient selection process and enhance the effectiveness of primary prevention ICD implantation.

Keywords

Implantable cardioverter-defibrillator

ICD implantation

Primary prevention

Multimodal machine learning

Risk assessment

Patient selection

Maarten Z H Kolk

Samuel Ruipérez-Campillo

Tina Baykaner

Gurukripa N Kowlgi