School of Nursing, Shandong Second Medical University, Weifang, China.
Weifang People's Hospital, Weifang, China.
J Obstet Gynaecol Res. 2024 Jul;50(7):1216-1228. doi: 10.1111/jog.15956. Epub 2024 Apr 21.
The aim of this study was to develop a web-based dynamic prediction model for postoperative nausea and vomiting (PONV) in patients undergoing gynecologic laparoscopic surgery.
The patients (N = 647) undergoing gynecologic laparoscopic surgery were included in this observational study. The candidate risk-factors related to PONV were included through literature search. Lasso regression was utilized to screen candidate risk-factors, and the variables with statistical significance were selected in multivariable logistic model building. The web-based dynamic Nomogram was used for model exhibition. Accuracy and validity of the experimental model (EM) were evaluated by generating receiver operating characteristic (ROC) curves and calibration curves. Hosmer-Lemeshow test was used to evaluate the goodness of fit of the model. Decision curve analysis (DCA) was used to evaluate the clinical practicability of the risk prediction model.
Ultimately, a total of five predictors including patient-controlled analgesia (odds ratio [OR], 4.78; 95% confidence interval [CI], 1.98-12.44), motion sickness (OR, 4.80; 95% CI, 2.71-8.65), variation of blood pressure (OR, 4.30; 95% CI, 2.41-7.91), pregnancy vomiting history (OR, 2.21; 95% CI, 1.44-3.43), and pain response (OR, 1.64; 95% CI, 1.48-1.83) were selected in model building. Assessment of the model indicates the discriminating power of EM was adequate (ROC-areas under the curve, 93.0%; 95% CI, 90.7%-95.3%). EM showed better accuracy and goodness of fit based on the results of the calibration curve. The DCA curve of EM showed favorable clinical benefits.
This dynamic prediction model can determine the PONV risk in patients undergoing gynecologic laparoscopic surgery.
本研究旨在开发一种用于预测妇科腹腔镜手术患者术后恶心呕吐（PONV）的基于网络的动态预测模型。
本观察性研究纳入了 647 例接受妇科腹腔镜手术的患者。通过文献检索纳入与 PONV 相关的候选风险因素。利用 Lasso 回归筛选候选风险因素，并在多变量逻辑模型构建中选择有统计学意义的变量。使用基于网络的动态列线图展示模型。通过生成受试者工作特征（ROC）曲线和校准曲线评估实验模型（EM）的准确性和有效性。Hosmer-Lemeshow 检验用于评估模型的拟合优度。决策曲线分析（DCA）用于评估风险预测模型的临床实用性。
最终，共筛选出包括患者自控镇痛（比值比 [OR]，4.78；95%置信区间 [CI]，1.98-12.44）、晕动病（OR，4.80；95% CI，2.71-8.65）、血压变化（OR，4.30；95% CI，2.41-7.91）、妊娠呕吐史（OR，2.21；95% CI，1.44-3.43）和疼痛反应（OR，1.64；95% CI，1.48-1.83）等五个预测因子，纳入模型构建。对模型的评估表明，EM 的判别能力足够（ROC 曲线下面积，93.0%；95% CI，90.7%-95.3%）。基于校准曲线的结果，EM 显示出更好的准确性和拟合优度。EM 的 DCA 曲线显示出良好的临床获益。
该动态预测模型可确定接受妇科腹腔镜手术患者的 PONV 风险。
