Predicting Postoperative Nausea And Vomiting Using Machine Learning: A
  Model Development and Validation Study
Maxim Glebov Teddy Lazebnik Boris Orkin Haim Berkenstadt Svetlana Bunimovich-Mendrazitsky
Background: Postoperative nausea and vomiting (PONV) is a frequently observed
complication in patients undergoing surgery under general anesthesia. Moreover,
it is a frequent cause of distress and dissatisfaction during the early
postoperative period. The tools used for predicting PONV at present have not
yielded satisfactory results. Therefore, prognostic tools for the prediction of
early and delayed PONV were developed in this study with the aim of achieving
satisfactory predictive performance.
  Methods: The retrospective data of adult patients admitted to the
post-anesthesia care unit after undergoing surgical procedures under general
anesthesia at the Sheba Medical Center, Israel, between September 1, 2018, and
September 1, 2023, were used in this study. An ensemble model of machine
learning algorithms trained on the data of 54848 patients was developed. The
k-fold cross-validation method was used followed by splitting the data to train
and test sets that optimally preserve the sociodemographic features of the
patients, such as age, sex, and smoking habits, using the Bee Colony algorithm.
  Findings: Among the 54848 patients, early and delayed PONV were observed in
2706 (4.93%) and 8218 (14.98%) patients, respectively. The proposed PONV
prediction tools could correctly predict early and delayed PONV in 84.0% and
77.3% of cases, respectively, outperforming the second-best PONV prediction
tool (Koivuranta score) by 13.4% and 12.9%, respectively. Feature importance
analysis revealed that the performance of the proposed prediction tools aligned
with previous clinical knowledge, indicating their utility.
  Interpretation: The machine learning-based tools developed in this study
enabled improved PONV prediction, thereby facilitating personalized care and
improved patient outcomes.