Machine learning model predicts mortality risk for individual cardiac surgery patients

The new data-driven algorithm, built on a stockpile of electronic health records (EHR), is said to be the first institution-specific model for assessing a cardiac patient’s risk prior to surgery, which will allow health care providers to pursue the best course of action for individual patients. The team’s work is detailed in The Journal of Thoracic and Cardiovascular Surgery (JTCVS) Open.

“The standard-of-care risk models used today are limited by their applicability to specific types of surgeries, leaving out significant numbers of patients undergoing complex or combination procedures for which no models exist,” senior author Ravi Iyengar, PhD, said in a statement. “Our team rigorously combined electronic health record data and machine learning methods to demonstrate for the first time how individual institutions can build their own risk models for post-cardiac surgery mortality.”

Prediction models based on machine learning algorithms have been generated across diverse fields of medicine, and some have shown improved results over their standard-of-care counterparts.