World falls guidelines algorithm 在 AGELESS-MELoR 队列中的预测价值。

Predictive value of the World falls guidelines algorithm within the AGELESS-MELoR cohort.

机构信息

Department of Medicine, Faculty of Medicine, University of Malaya 50603 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia.

Department of Medicine, Faculty of Medicine, University of Malaya 50603 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia; Department of Medical Sciences, School of Medical and Life Sciences, Sunway University, Bandar Sunway 47500 Petaling Jaya, Selangor, Malaysia.

出版信息

Arch Gerontol Geriatr. 2024 Oct;125:105523. doi: 10.1016/j.archger.2024.105523. Epub 2024 Jun 6.

DOI:10.1016/j.archger.2024.105523

PMID:38878671

Abstract

AIM

The World Falls Guidelines (WFG) Task Force published a falls risk stratification algorithm in 2022. However, its adaptability is uncertain in low- and middle-income settings such as Malaysia due to different risk factors and limited resources. We evaluated the effectiveness of the WFG risk stratification algorithm in predicting falls among community-dwelling older adults in Malaysia.

METHODS

Data from the Malaysian Elders Longitudinal Research subset of the Transforming Cognitive Frailty into Later-Life Self-Sufficiency cohort study was utilized. From 2013-2015, participants aged ≥55 years were selected from the electoral rolls of three parliamentary constituencies in Klang Valley. Risk categorisation was performed using baseline data. Falls prediction values were determined using follow-up data from wave 2 (2015-2016), wave 3 (2019) and wave 4 (2020-2022).

RESULTS

Of 1,548 individuals recruited, 737 were interviewed at wave 2, 858 at wave 3, and 742 at wave 4. Falls were reported by 13.4 %, 29.8 % and 42.9 % of the low-, intermediate- and high-risk groups at wave 2, 19.4 %, 25.5 % and 32.8 % at wave 3, and 25.8 %, 27.7 % and 27.0 % at wave 4, respectively. At wave 2, the algorithm generated a sensitivity of 51.3 % (95 %CI, 43.1-59.2) and specificity of 80.1 % (95 %CI, 76.6-83.2). At wave 3, sensitivity was 29.4 % (95 %CI, 23.1-36.6) and specificity was 81.6 % (95 %CI, 78.5-84.5). At wave 4, sensitivity was 26.0 % (95 %CI, 20.2-32.8) and specificity was 78.4 % (95 %CI, 74.7-81.8).

CONCLUSION

The algorithm has high specificity and low sensitivity in predicting falls, with decreasing sensitivity over time. Therefore, regular reassessments should be made to identify individuals at risk of falling.

摘要

目的

世界跌倒指南（WFG）工作组于 2022 年发布了跌倒风险分层算法。然而，由于风险因素不同和资源有限，该算法在马来西亚等中低收入国家的适用性尚不确定。我们评估了 WFG 风险分层算法在预测马来西亚社区居住的老年人跌倒中的有效性。

方法

本研究使用转化认知衰弱为晚年自理能力研究的马来西亚老年人纵向研究亚组的数据。2013-2015 年，从巴生谷三个选区的选民名单中选择了年龄≥55 岁的参与者。使用基线数据进行风险分类。使用第 2 波（2015-2016 年）、第 3 波（2019 年）和第 4 波（2020-2022 年）的随访数据确定跌倒预测值。

结果

在招募的 1548 人中，有 737 人在第 2 波接受了访谈，858 人在第 3 波接受了访谈，742 人在第 4 波接受了访谈。在第 2 波，低、中、高风险组的跌倒发生率分别为 13.4%、29.8%和 42.9%；在第 3 波，分别为 19.4%、25.5%和 32.8%；在第 4 波，分别为 25.8%、27.7%和 27.0%。在第 2 波，该算法的敏感性为 51.3%（95%CI，43.1-59.2），特异性为 80.1%（95%CI，76.6-83.2）。在第 3 波，敏感性为 29.4%（95%CI，23.1-36.6），特异性为 81.6%（95%CI，78.5-84.5）。在第 4 波，敏感性为 26.0%（95%CI，20.2-32.8），特异性为 78.4%（95%CI，74.7-81.8）。