摘要
目的 对成人患者术后急性疼痛风险预测模型进行范围综述,旨在为构建科学、实用可行的成人患者术后急性疼痛风险预测模型提供参考。方法 采用范围综述方法学框架,系统检索PubMed、Web of Science、Embase、Cochrane Library、CINAHL、中国生物医学文献数据库、中国知网、万方等数据库中有关成人术后急性疼痛风险预测模型相关研究,检索时限为建库至2024年12月。结果 共纳入14个成人术后急性疼痛风险预测模型,研究对象主要集中于骨科手术患者,模型构建方法以Logistic回归模型为主,模型展示以列线图为主,重复报告的独立预测因子包括年龄、女性、焦虑、术前疼痛等。结论 现有模型预测效能较好,但研究整体偏倚风险较高,仍需进一步优化、开发和验证出适合我国的成人术后急性疼痛风险预测模型。
Objective To conduct a scoping review of risk prediction models for acute postoperative pain in adult patients,aiming to provide reference for developing a scientific,practical,and feasible risk prediction model for such patients.Methods Following the scoping review methodological framework,systematic search was performed in PubMed,Web of Science,Embase,Cochrane Library,CINAHL,China Biology Medicine,CNKI,and Wanfang databases for studies related to risk prediction models for such patients.The search period spanned from the inception to December 2024.Results A total of 14 risk prediction models for acute postoperative pain in adults were included.The population of study primarily focused on orthopedic surgery patients.Logistic regression was the predominant modeling method,and nomograms were the most common presentation format.Frequently reported independent predictors included age,female sex,anxiety,and preoperative pain.Conclusions Existing models demonstrate good predictive performance,but the overall risk of bias in the studies is high.Further optimization,development,and validation of risk prediction models tailored to the Chinese population are still needed.
作者
梁元元
熊丹莉
高兴莲
方敏
柯稳
谭璇
张桃
杨荆艳
LIANG Yuanyuan;XIONG Danli;GAO Xinglian;FANG Min;KE Wen;TAN Xuan;ZHANG Tao;YANG Jingyan(Department of Nursing,Union Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan 430022,Hubei Province,China;School of Nursing,Tongji Medical College,Huazhong University of Science and Technology,Wuhan 430030,Hubei Province,China)
出处
《军事护理》
北大核心
2025年第8期41-44,共4页
MILITARY NURSING
基金
湖北省卫生健康委科研资助(WJ2023M028)。
关键词
成人
术后急性疼痛
预测模型
风险因素
adult
acute postoperative pain
prediction model
risk factor
