Predicting postoperative nausea and vomiting using machine learning: a model development and validation study
Abstract 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 in the early postoperative period. Currently, the classical scores used for pre...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-03-01
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| Series: | BMC Anesthesiology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12871-025-02987-2 |
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| Summary: | Abstract 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 in the early postoperative period. Currently, the classical scores used for predicting PONV have not yielded satisfactory results. Therefore, prognostic models for the prediction of early and delayed PONV were developed in this study to achieve satisfactory predictive performance. Methods The retrospective data of inpatient 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 35,003 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. Results Among the 35,003 patients, early and delayed PONV were observed in 1,340 (3.82%) and 6,582 (18.80%) patients, respectively. The proposed PONV prediction models correctly predicted early and delayed PONV in 83.6% and 74.8% of cases, respectively, outperforming the second-best PONV prediction score (Koivuranta score) by 13.0% and 10.4%, respectively. Feature importance analysis revealed that the performance of the proposed prediction tools aligned with previous clinical knowledge, indicating their utility. Conclusions The machine learning-based models developed in this study enabled improved PONV prediction, thereby facilitating personalized care and improved patient outcomes. |
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| ISSN: | 1471-2253 |