A Machine Learning-based Prediction Model for Delayed Clinically Important Postoperative Nausea and Vomiting in High-risk Patients Undergoing Laparoscopic Gastrointestinal Surgery

The website https://mvansmeden.shinyapps.io/BeyondEPV/ was used for sample size calculation, considering 6 candidate predictors, an event fraction of 0.14, and a criterion value for reduced mean predictive squared error of 0.03. The calculated sample size is 1080, with a minimally required expected event per variable of 25.1. Therefore, a sample size of 1154 patients is deemed sufficient to support the inclusion of 6 predictors in the development of the predictive model.

A total of 81 variables, including demographics, comorbidities, laboratory findings, as well as information related to anesthesia and surgery, are prospectively collected in the FDP-PONV trial and considered as potential predictive factors in this study. The least absolute shrinkage and selection operator method is used to identify clinically significant variables. Further selection of the final predictors is performed using stepwise regression based on the Akaike Information Criterion.

The entire dataset is randomly divided into a training set and a validation set in a ratio of 7:3. Six machine learning models, namely logistic regression, random, extreme gradient boosting, k-nearest neighbor, gradient boosting decision, and multi-layer perceptron, were developed to create risk prediction models for delayed CIPONV. The performance of the models is assessed by comparing the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, Brier score and calibration curve. Bootstrap resamples is conducted 1000 times on the training cohort to evaluate the predictive model's performance. Decision curve analysis is conducted to assess the clinical applicability of the model. The SHapley Additive Explanations library (SHAP) is used to interpret the prediction model.