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在麻醉后护理单位预测急性术后疼痛:风险工具开发和内部验证

Nicholas Papadomanolakis-Pakis^1,2, Simon Haroutounian³, Johan K Sørensen¹

¹Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.

|September 8, 2025

概括

新的风险工具可以预测在麻醉后护理单元 (PACU) 中的中度至严重和严重的急性术后疼痛 (APSP). 这些工具利用护理点的数据来早期管理APSP.

科学领域:

麻醉学和疼痛管理
外科手术患者的结果
预测分析在医疗保健中的应用

背景情况:

中度至严重的急性术后疼痛 (APSP) 影响大约30%的手术患者.
有效的APSP早期管理对于患者的康复至关重要.
术前预测APSP可以促进及时干预.

研究的目的:

开发和验证中度至重度和重度APSP的护理点风险预测工具.
确定与APSP风险增加相关的关键预测因素.
评估这些预测模型的临床实用性.

主要方法:

一项针对1380名接受选择性手术的成年患者的多中心前性队列研究.
后勤回归模型是使用预先识别的候选预测器来开发的.
内部验证包括引导重新抽样和决策曲线分析.

主要成果:

模型预测中度至严重的APSP (45.1%的发病率) 和严重的APSP (12.4%的发病率),乐观度调整后AUC分别为0.75和0.72.
风险增加的预测因素包括年龄较小,女性性别,手术前疼痛,手术前使用阿片类药物和更长的手术持续时间.
整形外科手术和区域麻醉与降低风险有关.

结论:

护理点风险工具显示可接受的性能和临床实用性,用于早期APSP预测.
这些模型是根据各种手术病例开发的,可以帮助主动治疗疼痛.
在广泛临床实施之前,建议进行外部验证.

关键词:

经手术后的急性疼痛.预测预测预测预测模型开发的发展.预测预后预测预测风险分层是指风险的分层.风险工具风险工具

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