Researchers show that a machine learning model can improve mortality risk prediction for cardiac surgery patients
A machine learning-based model that enables medical institutions to predict the mortality risk for individual cardiac surgery patients has been developed by a Mount Sinai research team, providing a significant performance advantage over current population-derived models.
The new data-driven algorithm, built on troves of electronic health records (EHR), is one of the first institution-specific models for assessing a cardiac patient’s risk prior to surgery, thus allowing healthcare providers to pursue the best course of action for that individual. The team’s work was described in a study published May 17, in The Journal of Thoracic and Cardiovascular Surgery (JTCVS) Open.
Researchers created a rigorous machine learning framework using routinely collected EHR data to develop a risk prediction model for postsurgical mortality that is both personalized to the patient and specific to the hospital—implicitly incorporating important information about Mount Sinai’s patient population, such as demographics, socioeconomic factors, and health characteristics. This is in contrast to population-derived models like STS, which are based on data from diverse health systems in different parts of the country. Further driving the performance of this methodology was a highly effective open-source prediction algorithm known as XGBoost, which builds an ensemble of decision trees by progressively focusing on harder-to-predict subsets of training data.
These researchers used XGBoost to model 6,392 cardiac surgeries performed at The Mount Sinai Hospital from 2011 to 2016, including heart valve procedures; coronary artery bypass graft; aortic resection, replacement, or anastomosis; and reoperative cardiac surgeries, which have been shown to appreciably increase mortality risk. The team then compared the performance of its model to STS models for the same patient sets.
The study showed that the XGBoost model outperformed STS risk scores for mortality in all commonly conducted categories of cardiac surgery for which STS scores were designed. Prediction performance of the XGBoost model across all surgery types was also high, demonstrating the potential of machine learning and EHR data for building effective institution-specific models.
