摘要
目的分析老年住院患者平衡能力现状及影响因素,探讨平衡功能差异与罹患老年综合征倾向的关联性。方法通过方便抽样法选取2023年4月至8月于江苏省人民医院住院的老年患者共262例作为研究对象。由专业测评人员于入院一周内,利用江苏省人民医院"边缘智能系统"软件对其进行系统性健康评估。并根据其中Tinetti平衡与步态量表(POMA)评估结果将其分为平衡功能正常组(188例)、POMA评分19~24分组(36例)、<19分组(38例)两个平衡障碍亚组,比较3组患者罹患老年综合征倾向差异,采用logistic回归分析进一步筛选关联因素,构建回归方程,绘制受试者工作特征曲线评价回归方程预测价值。结果262例患者年龄60~100(74.11±8.77)岁,男性156例(59.54%),总体POMA评分为(23.69±6.00)分,其中74例(28.24%)存在平衡功能障碍。单因素分析结果显示,不同平衡能力的患者年龄(t=20.356,P<0.001)、人血白蛋白(t=3.999,P=0.019)、罹患抑郁、衰弱、肌少症、睡眠障碍、存在营养风险、高跌倒风险人群占比差异均有统计学意义(χ^(2)=10.250、76.763、101.728、37.805、22.472、75.095,均P<0.05)。二元logistic回归模型结果显示,年龄、是否罹患肌少症、可疑失眠、失眠、存在营养风险是老年患者平衡能力的独立预测因子(OR=1.071、12.424、6.719、8.321、3.440,均P<0.05)。将上述关联变量纳入回归方程:Logit(P)=-8.792+0.069×年龄+2.520×罹患肌少症+1.905×可疑失眠+2.119×失眠+1.236×存在营养风险。受试者工作特征曲线分析结果显示其曲线下面积为0.902(95%CI:0.857~0.946,P<0.001),特异度为86.17%、灵敏度为85.14%。结论年龄≥75.5岁、罹患肌少症、睡眠障碍、存在营养风险可作为老年住院患者发生平衡障碍的预测指标,据此构建的回归模型具有较好预测价值,边缘智能系统建立推动医疗信息化水平提升。
Objective To analyze the current status and influencing factors of balance ability in elderly inpatients,and to explore the correlation between balance function and the tendency to suffer from geriatric syndromes.Methods A total of 262 elderly patients hospitalized from April to August 2023 were selected as the research objects by convenience sampling method.A systematic health assessment was performed by professional evaluators using the"Edge Intelligent Geriatric Assessment System"software of the Jiangsu Province Hospital within one week after admission.According to the results of Performance Oriented Mobility Assessment(POMA),the subjects were divided into normal balance group(n=188),POMA score 19 to 24 group(n=36)and less than 19 score group(n=38),the differences in the tendency of the three groups of patients to develop geriatric syndromes were compared.Logistic regression analysis was used to screen the related factors and construct the regression equation.Receiver operating characteristic(ROC)curve was drawn to evaluate the predictive value of regression equation.Results A total of 262 patients,of which 156(59.54%)were males,with an age range of 60 to 100 years(mean age 74.11±8.77 years)were included in the study.The total POMA score of 262 patients was 23.69±6.00,of which 74 cases(28.24%)had balance dysfunction.Univariate analysis showed that there were significant differences in age(t=20.356,P<0.001),serum albumin(t=3.999,P=0.019),proportions of people suffering from depression,frailty,sarcopenia,sleep disorders,nutrition risk and high fall risk between patients with different balance ability(χ^(2)=10.250,76.763,101.728,37.805,22.472,75.095,all P<0.05).Binary logistic regression model showed that age,sarcopenia,suspected insomnia,insomnia,and nutritional risk were independent predictors of balance ability in elderly patients(OR=1.071,12.424,6.719,8.321,3.440,all P<0.05).The above related variables were included in the regression equation:Logit(P)=-8.792+0.069×age+2.520×sarcopenia+1.905×suspected insomnia+2.119×insomnia+1.236×nutritional risk.ROC curve analysis showed that the area under the curve(AUC)was 0.902(95%CI:0.857-0.946,P<0.001),the predictive specificity was 86.17%and the sensitivity was 85.14%.Conclusions Age≥75.5 years,sarcopenia,sleep disorders,and nutritional risk could be used as predictors of balance disorders in elderly inpatients.The regression model constructed based on these indicators has a good predictive value.The establishment of the edge intelligent geriatric assessment system promotes the improvement of medical information level.
作者
许敏铮
薛强
范云霞
沈玉
刘滢
Xu Minzheng;Xue Qiang;Fan Yunxia;Shen Yu;Liu Ying(Department of Geriatrics,the First Affiliated Hospital of Nanjing Medical University Jiangsu Province Hospital,Nanjing 210029,China)
出处
《中华老年医学杂志》
北大核心
2025年第2期173-179,共7页
Chinese Journal of Geriatrics
基金
江苏省干部保健科研课题(BJ23005)
江苏省人民医院临床能力提升工程护理项目(JSPH-NC-2022-23)。
关键词
姿势平衡
老年人身心健康评价
人工智能
Postural balance
Geriatric assessment
Artificial intelligence
