A machine learning-based prediction model for delayed clinically important postoperative nausea and vomiting in high-risk patients undergoing laparoscopic gastrointestinal surgery

Background: Delayed clinically important postoperative nausea and vomiting (CIPONV) could lead to significant consequences following surgery. We aimed to develop a prediction model for it using machine learning algorithms utilizing perioperative data from patients undergoing laparoscopic gastrointestinal surgery. Methods: All 1154 patients in the FDP-PONV trial were enrolled. The optimal features for model development were selected by least absolute shrinkage and selection operator and stepwise regression from 81 perioperative variables. The machine learning algorithm with the best area under the receiver operating characteristic curve (ROCAUC) was determined and assessed. The interpretation of the prediction model was performed by the SHapley Additive Explanations library. Results: Six important predictors were identified. The random forest model showed the best performance in predicting delayed CIPONV, achieving an ROCAUC of 0.737 in the validation cohort. Conclusion: This study developed an interpretable model predicting personalized risk for delayed CIPONV, aiding high-risk patient identification and prevention strategies.