Application of machine learning for the prediction of post-operative nausea and vomiting in adult surgical patients – A systematic review

作者：Santosh Patel, Franklin Dexter · 发表于：Indian Journal of Anaesthesia · 年份：2026 · DOI：10.4103/ija.ija_38_26 · 被引用次数：1 · 研究领域：Nausea and vomiting management、Enhanced Recovery After Surgery、Anesthesia and Pain Management

Background and Aims: The clinical prediction of post-operative nausea and vomiting (PONV) is mainly based on scoring systems developed more than 2 decades ago. We systematically reviewed machine learning studies of PONV risk prediction. Methods: We searched databases including PubMed, Scopus, Web of Science, and Google Scholar for studies published till 14 September 2025. Using the area under the receiver operating characteristic curve and its standard error, we compared predictive performance with Apfel's original 4-parameter pre-operative scoring system [area under curve (AUC) 0.68]. We assessed the quality of reporting of the studies using the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis + Artificial Intelligence (TRIPOD+AI) framework. Results: Of 21 eligible studies, 16 were conducted in Asian countries. Three studies of mixed surgical populations reported an estimated AUC (0.714-0.814) numerically exceeding Apfel's (AUC 0.68). These models included not only pre-operative but also intra-operative variables (e.g., anaesthetic drugs) for model development. None of the studies provided their models sufficient for implementation (e.g., computer code with estimated parameters or a web page for calculations). Furthermore, none specified how the standard errors were calculated, for assessment of their reliability compared with Apfel's logistic regression model. Secondary analyses found that models for specific surgical populatio...