医学
列线图
风险评估
逻辑回归
接收机工作特性
逐步回归
物理疗法
老年人跌倒
伤害预防
毒物控制
职业安全与健康
平衡(能力)
老年学
内科学
急诊医学
计算机安全
计算机科学
病理
作者
Whang-Zong Wu,Qi Zhou,Qiang Gao,Hong Li,Jie Zhang,Juan Wu,Ji Shen,Jing Li,Hong Shi
摘要
Abstract Objectives To develop an instrument to facilitate the risk assessment of falls in older outpatients. Design A quantitative methodological study using the cross‐sectional data. Methods This study enrolled 1988 older participants who underwent comprehensive geriatric assessment (CGA) in an outpatient clinic from May 2020 to November 2022. The history of any falls (≥1 falls in a year) and recurrent falls (≥2 falls in a year) were investigated. Potential risk factors of falls were selected by stepwise logistic regression, and a screening tool was constructed based on nomogram. The tool performance was compared with two reference tools (Fried Frailty Phenotype; CGA with 10 items, CGA‐10) by using receiver operating curves, sensitivity (Sen), specificity (Spe), and area under the curve (AUC). Results Age, unintentional weight loss, depression measured by the Patient Health Questionnaire‐2, muscle strength measured by the five times sit‐to‐stand test, and stand balance measured by semi‐ and full‐tandem standing were the most important risk factors for falls. A fall risk screening tool was constructed with the six measurements (FRST‐6). FRST‐6 showed the best AUC (Sen, Spe) of 0.75 (Sen = 0.72, Spe = 0.69) for recurrent falls and 0.65 (Sen = 0.74, Spe = 0.48) for any falls. FRST‐6 was comparable to CGA‐10 and outperformed FFP in performance. Conclusions Age, depression, weight loss, gait, and balance were important risk factors of falls. The FRST‐6 tool based on these factors showed acceptable performance in risk stratification. Impact Performing a multifactorial assessment in primary care clinics is urgent for falls prevention. The FRST‐6 provides a simple and practical way for falls risk screening. With this tool, healthcare professionals can efficiently identify patients at risk of falling and make appropriate recommendations in resource‐limited settings. Patient or Public Contribution No patient or public contribution was received, due to our study design.
科研通智能强力驱动
Strongly Powered by AbleSci AI