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AI Tool Predicts Patient Outcomes After Surgeries and Procedures

An artificial intelligence (AI) tool developed by researchers at the Smidt Heart Institute at Cedars-Sinai, in collaboration with Stanford University and Columbia University, has shown promising results in accurately predicting patient outcomes after surgeries and procedures. This groundbreaking study, published in The Lancet Digital Health, utilized data from patients across three healthcare systems, including Cedars-Sinai.

The AI model was trained on pre-operative electrocardiograms (ECGs), which have been in use for over a century. ECGs involve placing electrodes on the skin to measure the heart’s electrical activity and assess its functioning. By analyzing patterns in the ECG waveforms, the algorithm was able to identify associations and predict post-operative mortality, making it the first electrocardiogram-based AI algorithm for this purpose.

“This is the first electrocardiogram-based AI algorithm that predicts post-operative mortality. Previously, algorithms have been used to assess long-term mortality as well as individual disease states, but determining post-surgical outcomes helps inform the actual decision to do surgery.”

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– Dr. David Ouyang, cardiologist at the Smidt Heart Institute at Cedars-Sinai

During the study, the algorithm classified most patients as low risk, while those identified as high risk had a nearly 9-fold increased probability of post-operative mortality. This novel approach has the potential to significantly improve clinicians’ ability to predict patient outcomes, enabling more informed decisions regarding surgical interventions.

Current clinical risk prediction tools have limitations, and doctors often rely on guidelines from medical societies to assess individual risk. The AI model provides a better understanding of risk by utilizing a commonly obtained diagnostic test, the ECG, which can inform important medical decisions.

Dr. Christine M. Albert, chair of the Department of Cardiology in the Smidt Heart Institute, emphasizes the importance of accurately assessing risk in cardiology procedures. She states, “A better understanding of risk, particularly by using a commonly obtained diagnostic test, can inform important medical decisions.”

The research team is currently exploring the development of a web application that would make the AI algorithm widely accessible to physicians and patients. This would further enhance the integration of AI in healthcare and potentially improve patient outcomes.

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