日间宫腔镜手术患者术后恶心呕吐风险的基于网络的动态预测模型的构建与验证

Construction and validation of a web-based dynamic predictive model for the risk of postoperative nausea and vomiting in patients undergoing day-case hysteroscopic surgery.

作者信息

Liu Jiang, Han Lifang, Zhang Fengxian, Jiang Yan, Cheng Lin, Qin Sifan, Fang Shirong

机构信息

Xiangya School of Nursing, Central South University, Changsha, China.

Department of Human Resources, Second People's Hospital of Weifang, Weifang, China.

出版信息

Front Med (Lausanne). 2025 Jul 23;12:1582546. doi: 10.3389/fmed.2025.1582546. eCollection 2025.

DOI:10.3389/fmed.2025.1582546

PMID:40771469

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12325351/

Abstract

OBJECTIVES

This study aimed to develop and validate a predictive risk model for postoperative nausea and vomiting (PONV) in patients undergoing day-case hysteroscopic surgery.

METHODS

The candidate predictors were identified by systematic literature review. Patients who met the study criteria were divided into training group and validation group. The time-period validation was used for the external validation of the model. The candidate predictors with statistical significance through lasso regression analyses were included in multifactor logistic regression analyses. The calibration and receiver operating characteristic (ROC) curves were utilized to assess the accuracy of model. Decision curve analysis (DCA) was used to assess the clinical benefit of Nomogram. All statistical analyses were constructed by RStudio software (version 4.2.1).

RESULTS

A total of five predictors were included in the PONV risk prediction model: (1) motion sickness (OR, 8.53; 95% CI, 6.21-11.81), (2) anesthesia time (OR, 4.20; 95% CI, 2.09-8.65), (3) fasting time (OR, 1.17; 95% CI, 1.13-1.22), (4) anxiety score (OR, 1.10; 95% CI, 1.08-1.12), and (5) artificial airway (OR, 0.54; 95% CI, 0.39-0.74). The area under the ROC curve for the training cohort and validation cohort was 85.0% (95% CI: 82.6-87.5%) and 80.3% (76.2-84.3%), respectively.

CONCLUSION

The predictive model demonstrated potential in predicting the risk of PONV in patients undergoing day-case hysteroscopic surgery.

摘要

目的

本研究旨在开发并验证日间宫腔镜手术患者术后恶心呕吐（PONV）的预测风险模型。

方法

通过系统文献回顾确定候选预测因素。符合研究标准的患者分为训练组和验证组。采用时间区间验证对模型进行外部验证。通过套索回归分析具有统计学意义的候选预测因素纳入多因素逻辑回归分析。利用校准曲线和受试者工作特征（ROC）曲线评估模型的准确性。决策曲线分析（DCA）用于评估列线图的临床获益。所有统计分析均使用RStudio软件（版本4.2.1）构建。

结果

PONV风险预测模型共纳入五个预测因素：（1）晕动病（OR，8.53；95%CI，6.21 - 11.81），（2）麻醉时间（OR，4.20；95%CI，2.09 - 8.65），（3）禁食时间（OR，1.17；95%CI，1.13 - 1.22），（4）焦虑评分（OR，1.10；95%CI，1.08 - 1.12），以及（5）人工气道（OR，0.54；95%CI，0.39 - 0.74）。训练队列和验证队列的ROC曲线下面积分别为85.0%（95%CI：82.6 - 87.5%）和80.3%（76.2 - 84.3%）。