Using Machine Learning to Establish Predictors of Mortality in Patients Undergoing Laparotomy for Emergency General Surgical Conditions

Introduction Patients undergoing laparotomy for emergency general surgery (EGS) conditions, constitute a high-risk group with poor outcomes. These patients have a high prevalence of comorbidities. This study aims to identify patient factors, physiological and time-related factors, which place patients into a group at increased risk of mortality. Methodology In a retrospective analysis of all patients undergoing an emergency laparotomy at Greys Hospital from December 2012 to 2018, we used decision tree discrimination to identify high-risk groups. Results Our cohort included 1461 patients undergoing a laparotomy for an EGS condition. The mortality rate was 12.4% (181). Nine hundred and ten patients (62.3%) had at least one known comorbidity on admission. There was a higher rate of comorbidities among those that died (154; 85.1%). Patient factors found to be associated with mortality were the age of 46 years or greater ( p