Key result
AI and ML show potential to enhance sudden cardiac arrest risk stratification over conventional tools.
Why the study?
Most sudden cardiac arrest events occur unexpectedly in individuals not identified as high-risk due to inadequate current risk stratification tools.
Artificial intelligence-based prediction models have the potential to enhance risk stratification for lethal ventricular arrhythmias and sudden cardiac arrest beyond current LVEF-based guidelines.
May enhance SCA risk stratification beyond LVEF; leaves open need for prospective validation before practice change.
Sudden cardiac arrest due to lethal ventricular arrhythmias is a major cause of mortality worldwide and results in more years of potential life lost than any individual cancer. Most of these sudden cardiac arrest events occur unexpectedly in individuals who have not been identified as high-risk due to the inadequacy of current risk stratification tools. Artificial intelligence tools are increasingly being used to solve complex problems and are poised to help with this major unmet need in the field of clinical electrophysiology. By leveraging large and detailed datasets, artificial intelligence-based prediction models have the potential to enhance the risk stratification of lethal ventricular arrhythmias. This review presents a synthesis of the published literature and a discussion of future directions in this field.
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Holmström et al. (2023) conducted a review in Ventricular Arrhythmias and Sudden Cardiac Arrest. Artificial Intelligence and Machine Learning vs. Conventional risk stratification tools was evaluated. Artificial intelligence and machine learning models demonstrate potential to enhance risk stratification for sudden cardiac arrest and lethal ventricular arrhythmias compared to conventional tools.
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