ArticleBMC anesthesiology2025
Predicting postoperative nausea and vomiting using machine learning: a model development and validation study.
Article in BMC anesthesiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- GPT-4o-Powered Preanesthetic AI: Development and Validation Study.JMIR medical informatics · 2026Article
- Artificial intelligence for predicting perioperative anaesthetic complications and supporting clinical decision-making: a scoping review.Journal of clinical monitoring and computing · 2026Review
- Review
- Prediction of Postoperative Vomiting Within 24 Hours Using Machine Learning With Large Language Model-Enhanced Interpretability: Development and Validation Study.JMIR medical informatics · 2026Article
- Machine Learning-Based Prediction of Early Patient-Controlled Analgesia Discontinuation After Total Knee Arthroplasty: A Retrospective Cohort Study.Journal of clinical medicine · 2026Article
- Chronic Stress, Immune Suppression, and Cancer Occurrence: Unveiling the Connection Using Survey Data and Predictive Models.Medical sciences (Basel, Switzerland) · 2026Article
- Interpretable machine learning for postoperative nausea and vomiting prediction in elderly orthopedic patients: a comparative study.BMC medical informatics and decision making · 2026Article
- DSPONVNet: a multimodal deep learning model integrating intraoperative monitoring and clinical features for predicting postoperative nausea and vomiting risk.BMC medical research methodology · 2026Article
- Application of machine learning for the prediction of post-operative nausea and vomiting in adult surgical patients - A systematic review.Indian journal of anaesthesia · 2026Article
- A dynamic nomogram for predicting postoperative nausea and vomiting after laparoscopic surgery: a prospective study.BMC anesthesiology · 2026Article
- Leveraging transformer-based artificial intelligence for enhanced anesthetic decision-making in orthopedic surgery.Frontiers in medicine · 2026Article
- Advancing a Hybrid Decision-Making Model in Anesthesiology: Applications of Artificial Intelligence in the Perioperative Setting.Healthcare (Basel, Switzerland) · 2025Review
- Preventing postoperative nausea and vomiting after laparoscopic cholecystectomy: toward personalization.Journal of minimally invasive surgery · 2025Article
- Intraoperative hypotension trajectories and their predictive value for major postoperative complications: a retrospective cohort study.Frontiers in medicine · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundPostoperative 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.
methodsThe 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.
resultsAmong 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.
conclusionsThe machine learning-based models developed in this study enabled improved PONV prediction, thereby facilitating personalized care and improved patient outcomes.
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