ArticleAnnals of medicine2025
Prediction of postoperative nausea and vomiting in patients undergoing sedated gastrointestinal endoscopy based on machine learning.
Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Regarding: 'prediction of postoperative nausea and vomiting in patients undergoing sedated gastrointestinal endoscopy based on machine learning'.Annals of medicine · 2026Article
- The impact of frailty and sarcopenia index on postoperative nausea and vomiting in elderly patients undergoing sedation-assisted gastrointestinal endoscopy.BMC anesthesiology · 2026Article
- Construction and Validation of a Risk Prediction Model for Postoperative Nausea and Vomiting in Patients with Liver Cancer.Journal of hepatocellular carcinoma · 2026Article
- Determination of the median effective dose (ED₅₀) of oliceridine combined with propofol for inhibiting gastroscope insertion responses in adult patients undergoing painless gastroscopy.Frontiers in medicine · 2026Article
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Authors and funding
12 authors.
Funding
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Abstract
objectiveThis multicentre study aimed to develop and validate a machine learning (ML) model to predict postoperative nausea and vomiting (PONV) in patients undergoing sedated gastrointestinal endoscopy. We compared multiple algorithms, applied SHAP for feature interpretability, and translated the optimized model into a web-based tool.
methodsA total of 745 patients were prospectively enrolled from four tertiary hospitals in China, including a development cohort of 428 patients from the First Affiliated Hospital of Zhengzhou University (July-December 2023) and an external validation cohort of 317 patients from three institutions (June-August 2024). Eligible patients were aged 18-80 years with ASA I-III. Exclusions included severe cardiopulmonary comorbidities, >30% missing data, complications or withdrawal. Eleven ML algorithms were trained using demographic, clinical and procedural variables. Model performance was assessed via AUC, accuracy, precision, recall,
resultsThis study enrolled 745 patients (428 in internal training and 317 in external validation cohorts). While the incidence of PONV showed no significant inter-cohort difference (29.0% vs. 29.6%,
conclusionsLDA demonstrated superior generalizability and was implemented as a web-based risk prediction tool, enabling real-time PONV assessment and supporting individualized perioperative management.
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