摘要
目的:分析老年住院患者跌倒警觉度低下的影响因素,构建风险列线图并验证,为临床医护人员早期识别跌倒警觉度低下的老年住院患者提供参考。方法:选取2023年10月至2024年3月就诊于皖南医学院弋矶山医院的605例老年住院患者为研究对象,按照7∶3的比例随机分为训练集(n=423)和验证集(n=182),采用一般资料调查表、社会衰弱筛查工具、蒂尔堡衰弱指数、跌倒警觉度量表对患者进行测评,采用多因素Logistic回归分析确定老年住院患者跌倒警觉度低下的影响因素,使用RStudio构建老年住院患者跌倒警觉度低下的列线图,采用受试者工作特征(receiveroperating characteristic,ROC)曲线、校准图形、决策曲线(DecisionCurve Analysis,DCA)验证模型的区分度、一致性及临床净获益。结果:多因素Logistic回归分析结果显示,近1年有无跌倒史、个人月收入情况、既往体育运动时间、社会衰弱评分以及蒂尔堡衰弱量表评分是老年患者住院期间发生跌倒警觉度低下的独立危险因素,Hosmer-Lemeshowχ2检验显示,χ2=8.863,P=0.354,预测模型校准度良好;训练集与验证集的ROC曲线下面积分别为0.860 (95%CI:0.815~0.904)和0.937(95%CI:0.888~0.986),模型最大约登指数分别为0.576、0.788,模型区分度较好;DCA决策曲线显示,该模型临床有效性较好。结论:构建的列线图效果良好,可帮助临床医护人员快速有效地筛查出有跌倒警觉度低下风险的老年住院患者。
Objective:To analyze the influencing factors of low fall alertness in elderly inpatients,construct a risk prediction model and validate it,providing a reference for clinical medical staff to identify elderly inpatients with low fall alertness in the early stage.Methods:A total of 605 elderly inpatients treated in Yijishan Hospital affiliated to Wannan Medical College from Oct 2023 to Mar 2024 were enrolled and randomly divided into the training group(n=423)and validation group(n=182)at a ratio of 7∶3.The patients were evaluated using a general information questionnaire,the Social Frailty Screening Tool(HALFT),the Tilburg Frailty Indicator(TFI),and the Self-Awareness of Falls in Elderly scale(SAFE).Multivariate logistic analysis was used to determine the influencing factors of low fall alertness in elderly inpatients.RStudio was used to construct a risk prediction model of low fall alertness.The discrimination,calibration,and clinical net benefit of the model were verified using the receiver operating characteristic(ROC)curves,calibration plots,and decision curve analysis(DCA).Results:Multivariate logistic analysis showed that the history of falls,monthly income,previous physical activity time,social frailty score and TFI score were independent risk factors for low fall alertness in elderly inpatients.The Hosmer-Lemeshowχ2 test showed thatχ2=8.863,P=0.354,indicating good calibration of the prediction model.The area under the ROC curve of the training group and the validation group were 0.860(95%CI:0.815-0.904)and 0.937(95%CI:0.888-0.986),respectively,and the maximum Youden indices of the model was 0.576 and 0.788,respectively,indicating good discrimination of the model.The DCA decision curve showed that the model had good clinical effectiveness.Conclusion:The constructed model has a good prediction effect and can help clinical medical staff quickly and effectively screen out elderly inpatients at risk of low fall alertness.
作者
李新新
滕晓菊
周新凯
马红梅
韩雅婷
李迎霞
朱加梅
罗琨
LI Xinxin;TENG Xiaoju;ZHOU Xinkai;MA Hongmei;HAN Yating;LI Yingxia;ZHU Jiamei;LUO Kun(School of Nursing,Wannan Medical College,Wuhu 241002,China;Department of Nursing,Yijishan Hospital,Wannan Medical College)
出处
《沈阳医学院学报》
2025年第1期12-19,共8页
Journal of Shenyang Medical College
基金
教育部产学合作协同育人项目(No.231006291311732)
芜湖市科技局创新环境(软科学)研究项目(No.2023rkx22)。
