A Machine Learning Prediction Model for Delayed CIPONV

Postoperative nausea and vomiting (PONV) can lead to serious postoperative complications, but most symptoms are mild. Clinically important PONV (CIPONV) refers to PONV symptoms that have a significant impact on the patient's well-being and recovery. Present predictive systems for PONV are mainly concentrated on early PONV. However, there is currently no suitable prediction model for delayed PONV, particularly delayed CI-PONV. This study aims to develop and validate a prediction model for delayed CI-PONV using machine learning algorithms utilizing perioperative data from patients undergoing laparoscopic gastrointestinal surgery. All 1154 patients in the FDP-PONV trial will be enrolled in this study. Delayed CIPONV is defined as experiencing CIPONV between 25-120 hours after surgery. After selecting the modeling variables from 81 perioperative clinical features, six machine learning models are established to generate the risk prediction models for delayed CIPONV. The area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score and Brier score are used to evaluate the model performance. Shape Additive explanation analysis was conducted to evaluate feature importance.