Automated prediction of adverse post-surgical outcomes

Abstract

Patients undergoing surgery can experience a range of adverse events, such as renal and cardiac injury, respiratory failure, and death. This study focuses on discovering relationships between perioperative physiological data and adverse post-surgical outcomes, with the goal of developing strategies to reduce the severity and frequency of these conditions. Analyzing the patient's preoperative demographic data, such as age and race, and perioperative physiologic data, such as blood pressure and anesthesia dosage, we use statistical models to predict whether a patient under anesthesia will develop renal or cardiac injury, respiratory failure, or death. Specifically, we compare generalized linear models, random forest models, and L1 regularized logistic regression models in predicting these adverse events. For each event, the random forest model generally outperformed its competitors, as shown in receiver operating characteristic (ROC) curves and evidenced by the higher area under the curve (AUC) values of 0.85, 0.86, 0.85, and 0.82 for death, renal injury, respiratory failure, and cardiac injury, respectively. However, score tables indicate that at certain thresholds, the L1 regularized logistic regression predicts fewer false negatives than the random forest models. In general, our findings show the existence of a relationship between perioperative predictors and post-surgical complications. This relationship could provide the foundation for a surveillance and alert system.