Trial reportBMC gastroenterology2025
Development and validation of a machine learning-based prediction model for prolonged length of stay after laparoscopic gastrointestinal surgery: a secondary analysis of the FDP-PONV trial.
Trial report in BMC gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04853147 (Triple Combination of Fosaprepitant, Dexamethasone and Palonosetron Versus Combination of Dexamethasone and Palonosetron for the Prevention of Postoperative Nausea and Vomiting in Patients Undergoing Laparoscopic Gastrointestinal Surgery), which is not on this map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Triple Combination of Fosaprepitant, Dexamethasone and Palonosetron Versus Combination of Dexamethasone and Palonosetron for the Prevention of Postoperative Nausea and Vomiting in Patients Undergoing Laparoscopic Gastrointestinal Surgery
Who cites it
2 citing papers in PubMed.
- Interpretable machine learning for postoperative nausea and vomiting prediction in elderly orthopedic patients: a comparative study.BMC medical informatics and decision making · 2026Article
- Explainable machine learning for predicting postoperative length of stay after gastrectomy: a nationwide study using XGBoost and SHAP.Frontiers in medical technology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProlonged postoperative length of stay (PLOS) is associated with several clinical risks and increased medical costs. This study aimed to develop a prediction model for PLOS based on clinical features throughout pre-, intra-, and post-operative periods in patients undergoing laparoscopic gastrointestinal surgery.
methodsThis secondary analysis included patients who underwent laparoscopic gastrointestinal surgery in the FDP-PONV randomized controlled trial. This study defined PLOS as a postoperative length of stay longer than 7 days. All clinical features prospectively collected in the FDP-PONV trial were used to generate the models. This study employed six machine learning algorithms including logistic regression, K-nearest neighbor, gradient boosting machine, random forest, support vector machine, and extreme gradient boosting (XGBoost). The model performance was evaluated by numerous metrics including area under the receiver operating characteristic curve (AUC) and interpreted using shapley additive explanations (SHAP).
resultsCases on or before 2021–12-18 formed the training cohort (802 patients) and those after that date formed the validation cohort (339 patients). Among the models evaluated, the XGBoost model demonstrated the best performance in the validation cohort, achieving an AUC of 0.756. After five times repeated tenfold cross-validation, the XGBoost model demonstrated a mean AUC of 0.816. SHAP plots were generated based on the predictors: education level; preoperative hypoalbuminemia and insufficient sleep; surgery type and duration; blood loss; postoperative hypotension; postoperative albumin infusion; highest pain score during 73–120 h after surgery; time to first flatus and defecation; postoperative infection; and postoperative acute kidney injury.
conclusionsThis study developed a prediction model using machine learning to identify patients at high risk of PLOS after laparoscopic gastrointestinal surgery.
trial registrationThe FDP-PONV trial was registered at clinicaltrials.gov (NCT04853147) on 2021–04-27.
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