Development of a machine learning-derived model to predict unplanned ICU admissions after major non-cardiac surgery.

backgroundUnplanned postoperative intensive care unit admissions (UIAs) are rare events that cause significant challenges to perioperative workflow. We describe the development of a machine-learning derived model to predict UIAs using only widely used preoperative variables.

methodsThis was a 3-year retrospective review of all adult surgeries under the General, Vascular, and Thoracic surgical services with anticipated length of greater than 180 minutes at a single institution. A UIA was defined as any post-operative patient recovering in the post-anesthesia care unit (PACU) requiring direct transfer to the intensive care unit (ICU) for higher level of care. We developed our prediction model with a gradient-boosting decision tree algorithm (XGBoost). The model incorporated sixteen generalizable predictor variables that were derived from the demographics and surgical booking details. Validation and evaluation were performed with 10-fold cross validation, and model performance was evaluated using the area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, and likelihood ratio.

resultsA total of 4658 patients were included for analysis. The incidence of UIAs was 2.3%. With 10-fold cross validation, the area under the ROC curve was 0.80 (95% CI 0.74-0.86). Two decision thresholds were used, which achieved the best specificity of 94% (95% CI 92-96%), best positive likelihood ratio of 4.22 (95% CI 0.99-8.79), and best sensitivity of 82% (95% CI 58-100%).

conclusionsOur machine learning-derived model is a reliable tool for the perioperative clinician to predict a rare outcome in high-risk patients using only preoperative variables. Future studies will include prospective validation of this model at other institutions and real-time incorporation for improvement in perioperative workflow.