A Machine Learning Approach in Predicting Mortality Following Emergency General Surgery
Jeff Gao, Aziz M. Merchant
Abstract
BACKGROUND: There is a significant mortality burden associated with emergency general surgery (EGS) procedures. The objective of this study was to develop and validate the use of a machine learning approach to predict mortality following EGS. METHODS: The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent EGS between 2012 and 2017. We developed a machine learning algorithm to predict mortality following EGS and compared its performance with existing risk-prediction models of American Society of Anesthesiologists (ASA) classification, American College of Surgeon Surgical Risk Calculator (ACS-SRC), and the modified frailty index (mFI) using the area under receiver operative curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: The machine learning algorithm had a very high performance for predicting mortality following EGS, and it had superior performance compared to the ASA classification, ACS-SRC, and the mFI, as measured by the AUC, sensitivity, specificity, PPV, and NPV. DISCUSSION: Machine learning approaches may be a promising tool to predict outcomes for EGS, aiding clinicians in surgical decision-making and counseling of patients and family, improving clinical outcomes by identifying modifiable risk factors than can be optimized, and decreasing treatment costs through resource allocation.