Clinic of Rehabilitation and Physical Medicine, University Hospital of Ostrava, 708 52 Ostrava, Czech Republic.
Department of Epidemiology and Public Health, Faculty of Medicine, University of Ostrava, 703 00 Ostrava, Czech Republic.
Int J Environ Res Public Health. 2022 Jul 27;19(15):9181. doi: 10.3390/ijerph19159181.
Although fall prevention in patients after stroke is crucial, the clinical validity of fall risk assessment tools is underresearched in this population. The study aim was to determine the cut-off scores and clinical validity of the Sensory Organization Test (SOT), the Berg Balance Scale (BBS), and the Fall Efficacy Scale-International (FES-I) in patients after stroke.
In this prospective cross-sectional study, we analyzed data for patients admitted to a rehabilitation unit after stroke from 2018 through 2021. Participants underwent SOT, BBS, and FES-I pre-discharge, and the fall incidence was recorded for 6 months. We used an area under the receiver operating characteristic curve (AUC) to calculate predictive values.
Of 84 included patients (median age 68.5 (interquartile range 67-71) years), 32 (38.1%) suffered a fall. All three tests were significantly predictive of fall risk. Optimal cut-off scores were 60 points for SOT (AUC 0.686), 35 and 42 points for BBS (AUC 0.661 and 0.618, respectively), and 27 and 29 points for FES-I (AUC 0.685 and 0.677, respectively).
Optimal cut-off scores for SOT, BBS, and FES-I were determined for patients at risk for falls after a stroke, which all three tools classified with a good discriminatory ability.
尽管预防脑卒中后患者跌倒至关重要,但这些人群中跌倒风险评估工具的临床有效性仍研究不足。本研究旨在确定脑卒中后患者的感觉组织测试(SOT)、Berg 平衡量表(BBS)和跌倒效能量表-国际版(FES-I)的截断值和临床有效性。
在这项前瞻性的横断面研究中,我们分析了 2018 年至 2021 年期间入住康复病房的脑卒中患者的数据。参与者在出院前接受 SOT、BBS 和 FES-I 检查,记录了 6 个月内的跌倒发生率。我们使用受试者工作特征曲线下的面积(AUC)来计算预测值。
在 84 名纳入的患者中(中位数年龄 68.5(四分位间距 67-71)岁),有 32 名(38.1%)发生了跌倒。这三种测试均能显著预测跌倒风险。SOT 的最佳截断值为 60 分(AUC 0.686),BBS 的最佳截断值为 35 分和 42 分(AUC 0.661 和 0.618),FES-I 的最佳截断值为 27 分和 29 分(AUC 0.685 和 0.677)。
为脑卒中后跌倒风险患者确定了 SOT、BBS 和 FES-I 的最佳截断值,这三种工具均具有良好的区分能力。