摘要
目的构建并验证社区老年衰弱前期风险预测模型,为早期识别社区老年衰弱前期高危人群提供参考。方法筛选542名社区无衰弱和衰弱前期老年人作为建模组,运用反向传播神经网络机器学习算法构建衰弱前期预测模型;再筛选205名社区无衰弱和衰弱前期老年人作为验证组,利用受试者工作特征曲线对构建模型的预测效能进行时间跨度验证。结果按照重要性排序,社区老年衰弱前期危险因素分别为年龄、住院史、跌倒史、运动量少、多病共存、抑郁倾向、认知功能下降、文化程度低、日常生活能力下降及多重用药。以logistic回归模型作为参考,反向传播神经网络预测效能佳,AUC为0.891,95%CI(0.846~0.918),灵敏度为0.858,特异度为0.782。结论反向传播神经网络模型预测效能优于logistic回归模型,社区工作人员可通过预防跌倒、运动干预、慢病健康教育、抑郁及认知干预等预防老年衰弱前期的发生发展。
Objective To construct and validate a prefrailty risk prediction model among community older people,so as to provide reference for early detection of prefrailty.Methods A total of 542 robust and prefrail community older people were screened to develop the prefrialty prediction model by using back propagation(BP)neural network machine learning.A second group of 205 robust and prefrail community older people were screened to validate the model performance using Received Operator Characteristics curve.Results The risk factors of prefrailty ranking in order of importance were age,hospitalization last year,fall last year,less exercise,multimorbidity,depression,cognitive function impairment,lower education,lower daily activity and polypharmacy.Compared with logistic regression model,BP neural network model had a better prediction performance,its AUC was 0.891,95%CI(0.846-0.918),accuracy was 0.858,and specificity was 0.782.Conclusion BP neural network model has better prediction performance,and community workers could prevent the development of prefrailty in community older people through fall prevention,exercise intervention,chronic disease health education,depression and cognition intervention.
作者
李彩福
赵伟
叶秀春
赵东丽
邹继华
董海娜
周英
许丽娟
Li Caifu;Zhao Wei;Ye Xiuchun;Zhao Dongli;Zou Jihua;Dong Haina;Zhou Ying;Xu Lijuan(Medical College,Lishui University,Lishui 323000,China)
出处
《护理学杂志》
CSCD
北大核心
2022年第15期84-88,共5页
Journal of Nursing Science
基金
国家自然科学基金项目(71904073)
浙江省自然科学基金项目(LY19G030001)
浙江省医药卫生科技计划项目(2021KY418)。
