Suppr超能文献

《评估图卢兹圣路易大学微型跌倒评估工具在预测居住在养老院的老年人跌倒事件中的应用:一项为期 6 个月的前瞻性研究》。

An Assessment of the Toulouse Saint Louis University Mini Falls Assessment Tool to Predict Incident Falls among Older Adults Residing in Nursing Homes: A 6-Month Prospective Study.

机构信息

Olivier Bruyère, University of Liège, Liège, Belgium,

出版信息

J Nutr Health Aging. 2021;25(7):933-937. doi: 10.1007/s12603-021-1651-1.

Abstract

OBJECTIVES

Toulouse Saint Louis University Mini Falls Assessment (TSLUMFA) tool has been designed to predict falls. It was initially validated in a geriatric clinic in 2018. The primary objective was to evaluate the predictive capacity of the TSLUMFA for incident falls in older adults residing in nursing homes. The secondary objective was to determine the TSLUMFA optimal cut-off value identifying those older adults with a high-risk of falling.

SETTINGS

A longitudinal study was carried out over a period of six months.

PARTICIPANTS

93 older adults residing in nursing homes were evaluated for the present study.

MEASUREMENTS

The TSLUMFA (made up of 7 criteria) was administered at baseline, and incident falls were recorded based on a registry of falls. Comparisons of TSLUMFA scores between fallers and non-fallers were performed using the U Mann-Whitney test or Chi². Correlation between the total TSLUMFA score (/30 points) and incident fall(s) was explored using the Cox proportional hazard model. ROC analysis enabled an optimal cut-off value to be established to identify those adults at the highest-risk of falling.

RESULTS

In the study, 93 older adults (61.3% women) with a median age of 80 (69-87) years were included. The median total TSLUMFA score was 21 (19-24.5) points. During the 6-month study period, 38 subjects (40.9%) experienced at least one fall. The total TSLUMFA score in older adults with incident fall(s) was significantly lower than in those who did not fall (20 (15.75-22.25) points versus 23 (20-25) points and a p-value of <0.001). For each 1-point higher score at the total TSLUMFA a 9% less chance of falling was observed during the study period (p-value = 0.006). The AUC was 0.736 (95%CI: 0.617-0.822) and p-value <0.001, clearly demonstrating its interesting performance as a screening tool. A score of ≤ 21 points was identified as the optimal cut-off to identify those older adults at a higher-risk of falling.

CONCLUSION

The TSLUMFA performed well and successfully identified older adults with a high risk of falling in a nursing home setting. Further comparisons with existing tools are warranted.

摘要

目的

图卢兹圣路易大学微型跌倒评估(TSLUMFA)工具旨在预测跌倒。它最初于 2018 年在一家老年诊所得到验证。主要目的是评估 TSLUMFA 在居住在养老院的老年人中预测偶发性跌倒的能力。次要目的是确定 TSLUMFA 的最佳截断值,以识别那些有高跌倒风险的老年人。

设置

进行了一项为期六个月的纵向研究。

参与者

评估了 93 名居住在养老院的老年人。

测量

在基线时进行 TSLUMFA(由 7 项标准组成),并根据跌倒登记册记录偶发性跌倒。使用 U 曼-惠特尼检验或 Chi²比较跌倒者和非跌倒者的 TSLUMFA 评分。使用 Cox 比例风险模型探讨总 TSLUMFA 评分(/30 分)与偶发跌倒之间的相关性。ROC 分析确定了最佳截断值,以识别那些跌倒风险最高的成年人。

结果

本研究纳入了 93 名老年人(61.3%为女性),中位年龄为 80 岁(69-87 岁)。中位总 TSLUMFA 评分为 21 分(19-24.5 分)。在 6 个月的研究期间,38 名受试者(40.9%)至少经历了一次跌倒。有偶发性跌倒的老年人的总 TSLUMFA 评分明显低于未跌倒的老年人(20(15.75-22.25)分与 23(20-25)分,p 值<0.001)。在研究期间,总 TSLUMFA 评分每增加 1 分,跌倒的可能性就降低 9%(p 值=0.006)。AUC 为 0.736(95%CI:0.617-0.822),p 值<0.001,清楚地表明它作为一种筛选工具具有良好的性能。得分≤21 分被确定为识别有更高跌倒风险的老年人的最佳截断值。

结论

TSLUMFA 在养老院环境中表现良好,成功识别了有高跌倒风险的老年人。需要与现有工具进行进一步比较。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验