Department of Physical Therapy, Faculty of Health and Medical Sciences, Tokoha University, Hamamatsu, Shizuoka, Japan.
Department of Physical Therapy, Faculty of Health and Medical Science, Teikyo Heisei University, Toshima, Tokyo, Japan.
Arch Phys Med Rehabil. 2021 Apr;102(4):598-603. doi: 10.1016/j.apmr.2020.12.001. Epub 2020 Dec 11.
This study aimed to cross-validate a previously developed knee osteoarthritis falls (KOAF) screening tool to distinguish between fallers and nonfallers among community-dwelling older adults with knee osteoarthritis (OA).
Cross-sectional survey study.
Three independent orthopedic clinics.
Older outpatients with knee OA (N=86; 71 women, 15 men; mean age, 75.2±6.2y).
Not applicable.
The primary outcome was to identify fallers and nonfallers among outpatients with OA based on their history of falls within the past year. We investigated factors including sex, age, body mass index, Kellgren-Lawrence grade, affected side (bilateral or unilateral knee OA), number of comorbidities, pharmacotherapy, physical therapy, pain, and activity as individual predictors of falls. Participants performed the one-leg standing test and the 5 times sit-to-stand test to determine motor function. Sensitivity, specificity, likelihood ratio, and post-test probability of the KOAF screening tool were calculated using receiver operating characteristic (ROC) curve analysis.
The results of the one-leg standing test and 5 times sit-to-stand test differed significantly between the 2 groups (P<.05). ROC curve analysis showed that the area under the curve was 0.88 (95% confidence interval, 0.80-0.96; P<.001). The post-test probability of falls was 83.3% (positive likelihood ratio, 11.54) when the total score of KOAF screening tool was 2 points and 2.6% (negative likelihood ratio, 0.06) when the total score of KOAF screening tool was less than 1 point.
Cross-validation results for the KOAF screening tool were better, confirming that the screening tool could distinguish between fallers and nonfallers with high accuracy. Our findings suggest that this simple screening tool could be readily used in clinical practice and could aid in clinical decision-making through providing choices for physical therapy evaluation and recommendations for physical therapy programs.
本研究旨在对先前开发的膝关节骨关节炎（OA）跌倒（KOAF）筛查工具进行交叉验证，以区分社区居住的膝关节 OA 老年患者中的跌倒者和非跌倒者。
横断面调查研究。
三家独立的骨科诊所。
膝关节 OA 门诊患者（86 例；71 名女性，15 名男性；平均年龄 75.2±6.2 岁）。
不适用。
主要结局是根据过去一年的跌倒史，确定 OA 门诊患者中的跌倒者和非跌倒者。我们研究了包括性别、年龄、体重指数、Kellgren-Lawrence 分级、患侧（双侧或单侧膝骨关节炎）、合并症数量、药物治疗、物理治疗、疼痛和活动等因素，这些因素均为跌倒的个体预测因素。参与者进行单腿站立试验和 5 次坐立试验以确定运动功能。使用受试者工作特征（ROC）曲线分析计算 KOAF 筛查工具的灵敏度、特异性、似然比和后验概率。
两组间单腿站立试验和 5 次坐立试验的结果差异有统计学意义（P<.05）。ROC 曲线分析显示，曲线下面积为 0.88（95%置信区间，0.80-0.96；P<.001）。当 KOAF 筛查工具的总分为 2 分时，跌倒的后验概率为 83.3%（阳性似然比，11.54），当 KOAF 筛查工具的总分为小于 1 分时，跌倒的后验概率为 2.6%（阴性似然比，0.06）。
KOAF 筛查工具的交叉验证结果更好，证实该筛查工具可以准确地区分跌倒者和非跌倒者。我们的研究结果表明，这种简单的筛查工具可以在临床实践中方便地使用，并通过提供物理治疗评估的选择和物理治疗方案的建议，为临床决策提供帮助。
