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术后尿预测模型在接受完全外皮上沟修复的患者中

Chulhyo Jeon¹, Sanguk Hwang², Jinbeom Cho³

¹Department of Surgery, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.

|July 9, 2025

概括

一个机器学习模型预测在完全外皮上沟修复后的术后尿路保留. 关键预测因素包括年龄,BMI,手术时间和流体量,使风险分层成为改善患者管理的重要因素.

科学领域:

手术瘤学手术瘤学
医疗信息学医疗信息学
临床预测模型临床预测模型

背景情况:

手术后尿 (PUR) 是完全外皮 (TEP) 沟修复后的一个常见并发症.
PUR可能导致长时间住院和增加患者的不适.

研究的目的:

开发和验证基于机器学习的预测模型,用于分层PUR风险.
为选择性术后管理建立简化临床评分系统.

主要方法:

对接受选择性TEP修复的1,125名患者的回顾性分析.
后勤回归用于初始预测选,然后进行梯度增强分析.
基于梯度提升特征重要性的简化评分系统的开发.

主要成果:

在5.16%的患者中发生PUR.
确定了关键预测因素:晚年,较低的BMI,更长的手术时间和增加的手术内液体体积 (89%的预测贡献).
开发的评分系统显示了良好的区分 (AUC=0.7803) 和校准 (布赖尔分数=0.0465).

结论:

为了选择性术后管理,建立了三级风险分层 (低,中,高).
决策曲线分析支持了评分系统的临床实用性.
建议进行前性外部验证.

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