一个基于网络的动态预测模型,用于手术后恶心和吐的患者接受妇科 laparoscopic 手术
Jiang Liu1, Shirong Fang2, Lin Cheng1
1School of Nursing, Shandong Second Medical University, Weifang, China.
The journal of obstetrics and gynaecology research
|April 21, 2024
概括
一种新的动态预测模型有助于识别在妇科腹腔镜手术后患有术后恶心和吐 (PONV) 风险的患者. 该工具有助于预测PONV风险,以更好地管理患者.
科学领域:
- 麻醉学和外科手术期间的医学.
- 手术瘤学手术瘤学
- 医疗信息学 医疗信息学
背景情况:
- 手术后恶心和吐 (PONV) 是妇科腹腔镜手术后的常见并发症,影响患者的康复和满意度.
- 准确的风险预测对于实施有针对性的预防策略和改善患者治疗结果至关重要.
研究的目的:
- 开发和验证基于Web的动态预测模型,用于估计在接受妇科腹腔镜手术的患者中PONV的风险.
主要方法:
- 一项观察性研究包括647名接受妇科腹腔镜手术的患者.
- 拉索回归从文献中确定了关键预测因素,随后进行了多变量物流建模.
- 开发了一个基于网络的动态名图,使用ROC曲线,校准曲线,Hosmer-Lemeshow测试和决策曲线分析 (DCA) 评估模型性能.
主要成果:
- 确定了PONV的五个显著预测因素:患者控制的止痛,运动性 motion sickness,血压变化,有怀孕史的吐和疼痛反应.
- 开发的模型显示出高准确性,曲线下的ROC面积为93.0% (95%CI,90.7%-95.3%).
- 该模型表现出良好的准确性,适合性和有利的临床实用性,这是校准曲线和DCA证实的.
结论:
- 一个经过验证的基于网络的动态预测模型有效地确定了在接受妇科腹腔镜手术的患者中PONV风险.
- 这种工具可以帮助临床医生进行个性化风险评估和管理PONV.
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