Key result
AI-augmented ECG predicts LV systolic dysfunction and mortality in cardiac intensive care patients.
Why the study?
There is a lack of robust mortality prediction tools specifically for cardiac intensive care unit patients despite many tools existing for other settings.
Systematic Review
Artificial intelligence and machine learning applied to ECG interpretation offer promising avenues to develop dynamic and more accurate mortality prediction models for cardiac intensive care unit patients.
CICU mortality prediction lacks robust tools; extends the rationale for targeted AI/ML development in high-acuity cardiac settings.
The medical complexity and high acuity of patients in the cardiac intensive care unit make for a unique patient population with high morbidity and mortality. While there are many tools for predictions of mortality in other settings, there is a lack of robust mortality prediction tools for cardiac intensive care unit patients. The ongoing advances in artificial intelligence and machine learning also pose a potential asset to the advancement of mortality prediction. Artificial intelligence algorithms have been developed for application of electrocardiogram interpretation with promising accuracy and clinical application. Additionally, artificial intelligence algorithms applied to electrocardiogram interpretation have been developed to predict various variables such as structural heart disease, left ventricular systolic dysfunction, and atrial fibrillation. These variables can be used and applied to new mortality prediction models that are dynamic with the changes in the patient's clinical course and may lead to more accurate and reliable mortality prediction. The application of artificial intelligence to mortality prediction will fill the gaps left by current mortality prediction tools.
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Rafie et al. (2022) conducted a systematic review in Cardiac Intensive Care Unit (CICU) patients. Artificial intelligence-augmented electrocardiogram (AI-ECG) vs. Traditional mortality prediction models was evaluated. Artificial intelligence-augmented electrocardiogram algorithms demonstrate the ability to predict left ventricular systolic dysfunction and mortality in cardiac intensive care unit patients, offering a novel approach to risk stratification.
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