Department of Anesthesia, The Sixth Affiliated Hospital, Sun Yat-sen University, No.26 Yuancunerheng Road, Guangzhou, 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, No.26 Yuancunerheng Road, Guangzhou, 510655, China.
Am J Surg. 2024 Nov;237:115912. doi: 10.1016/j.amjsurg.2024.115912. Epub 2024 Aug 20.
Delayed clinically important postoperative nausea and vomiting (CIPONV) could lead to significant consequences following surgery. We aimed to develop a prediction model for it using machine learning algorithms utilizing perioperative data from patients undergoing laparoscopic gastrointestinal surgery.
All 1154 patients in the FDP-PONV trial were enrolled. The optimal features for model development were selected by least absolute shrinkage and selection operator and stepwise regression from 81 perioperative variables. The machine learning algorithm with the best area under the receiver operating characteristic curve (ROCAUC) was determined and assessed. The interpretation of the prediction model was performed by the SHapley Additive Explanations library.
Six important predictors were identified. The random forest model showed the best performance in predicting delayed CIPONV, achieving an ROCAUC of 0.737 in the validation cohort.
This study developed an interpretable model predicting personalized risk for delayed CIPONV, aiding high-risk patient identification and prevention strategies.
术后迟发性临床相关恶心和呕吐（CIPONV）可能会给接受腹腔镜胃肠手术的患者带来严重后果。本研究旨在使用机器学习算法，基于患者围手术期数据，建立预测术后迟发性 CIPONV 的模型。
纳入 FDP-PONV 试验中的 1154 例患者。从 81 个围手术期变量中，采用最小绝对收缩和选择算子法和逐步回归法选择最优特征用于模型开发。确定并评估了具有最佳受试者工作特征曲线（ROC）下面积（AUC）的机器学习算法。使用 SHapley 加性解释库对预测模型进行解释。
确定了 6 个重要的预测因素。随机森林模型在预测术后迟发性 CIPONV 中的表现最佳，在验证队列中获得了 0.737 的 AUC。
本研究建立了一个可解释的模型，用于预测术后迟发性 CIPONV 的个体风险，有助于识别高危患者并制定预防策略。
