An ensemble machine learning approach to predict postoperative mortality in older patients undergoing emergency surgery

Abstract

Background Prediction of preoperative frailty risk in the emergency setting is a challenging issue because preoperative evaluation cannot be done sufficiently. In a previous study, the preoperative frailty risk prediction model used only diagnostic and operation codes for emergency surgery and found poor predictive performance. This study developed a preoperative frailty prediction model using machine learning techniques that can be used in various clinical settings with improved predictive performance. Methods This is a national cohort study including 22,448 patients who were older than 75 years and visited the hospital for emergency surgery from the cohort of older patients among the retrieved sample from the Korean National Health Insurance Service. The diagnostic and operation codes were one-hot encoded and entered into the predictive model using the extreme gradient boosting (XGBoost) as a machine learning technique. The predictive performance of the model for postoperative 90-day mortality was compared with those of previous frailty evaluation tools such as Operation Frailty Risk Score (OFRS) and Hospital Frailty Risk Score (HFRS) using the receiver operating characteristic curve analysis. Results The predictive performance of the XGBoost, OFRS, and HFRS for postoperative 90-day mortality was 0.840, 0.607, and 0.588 on a c-statistics basis, respectively. Conclusions Using machine learning techniques, XGBoost to predict postoperative 90-day mortality, using diagnostic and operation codes, the prediction performance was improved significantly over the previous risk assessment models such as OFRS and HFRS.

Effect of Machine Learning on Anaesthesiology Clinician Prediction of Postoperative Complications: The Perioperative ORACLE Randomised Clinical Trial

“…Researchers have proposed using machine learning (ML) models to mitigate the known limitations in anaesthesiology clinician risk assessment. Currently, ML models are available that predict postoperative mortality, [11][12][13][14][15] AKI, [16][17][18] and other postoperative complications 19,20 with moderate-to-high discrimination. However, it has not been ascertained whether anaesthesiology clinicians would incorporate such models into their clinical practice to identify more accurately which patients are at risk for complications and therefore might benefit from risk mitigation strategies or enhanced monitoring.…”