Introduction: The heterogeneity of procedures and the variety of comorbidities of the patients undergoing surgery in an emergency setting makes perioperative risk stratification, planning, and risk mitigation crucial. In this optic, Machine Learning has the capability of deriving data-driven predictions based on multivariate interactions of thousands of instances. Our aim was to cross-validate and test interpretable models for the prediction of post-operative mortality after any surgery in an emergency setting on elderly patients.

Methods: This study is a secondary analysis derived from the FRAILESEL study, a multi-center (N = 29 emergency care units), nationwide, observational prospective study with data collected between 06-2017 and 06-2018 investigating perioperative outcomes of elderly patients (age≥65 years) undergoing emergency surgery. Demographic and clinical data, medical and surgical history, preoperative risk factors, frailty, biochemical blood examination, vital parameters, and operative details were collected and the primary outcome was set to the 30-day mortality.

Results: Of the 2570 included patients (50.66% males, median age 77 [IQR = 13] years) 238 (9.26%) were in the non-survivors group. The best performing solution (MultiLayer Perceptron) resulted in a test accuracy of 94.9% (sensitivity = 92.0%, specificity = 95.2%). Model explanations showed how non-chronic cardiac-related comorbidities reduced activities of daily living, low consciousness levels, high creatinine and low saturation increase the risk of death following surgery.

Conclusions: In this prospective observational study, a robustly cross-validated model resulted in better predictive performance than existing tools and scores in literature. By using only preoperative features and by deriving patient-specific explanations, the model provides crucial information during shared decision-making processes required for risk mitigation procedures.