Department of Special Education and Communication Disorders, University of Nebraska-Lincoln, Lincoln, Nebraska 68583-0738, USA.
Otol Neurotol. 2013 Jun;34(4):729-35. doi: 10.1097/MAO.0b013e31827d8a5f.
This study examined the clinical use of the computerized gaze stabilization test (GST) as a screener for falls.
Cross-sectional, descriptive.
Tertiary medical center.
Fifteen older community-dwelling adults with a history of falls and 15 controls without a history of falls were recruited for participation in the study.
Participants performed GST with yaw plane head movements. The GST velocity was measured and compared with the dynamic gait index (DGI). Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) identified GST velocity cut points for identification of fallers based on history of falls and as compared with DGI score.
Our results suggested that GST can discriminate between individuals at risk for falls versus those not at risk. ROC analysis identified an AUC of 0.92 (≤ 100.5 degrees per second criterion value) for GST based on history of falls and an AUC of 0.85 (≤ 100.5 degrees per second criterion value) based on DGI for classifying falling risk. When GST and DGI scores were combined, the protocol identified an AUC of 1.0 (100% sensitivity, 100% specificity) for identifying falling risk.
There were significant head movement velocity differences from participants classified by history of falls and the DGI. Therefore, GST may serve as a potential falling risk assessment measure for older individuals with a history of falls. It is recommended that GST be used in a combined protocol with DGI to accurately identify individuals with falling risk rather than used in isolation.
本研究旨在探讨计算机化眼球稳定测试(GST)作为跌倒筛查工具的临床应用。
横断面、描述性研究。
三级医疗中心。
15 名有跌倒史的老年社区居住成年人和 15 名无跌倒史的对照者被招募参与本研究。
参与者进行了 GST 试验,头作矢状面运动。测量 GST 速度,并与动态步态指数(DGI)进行比较。受试者工作特征(ROC)曲线和 ROC 曲线下面积(AUC)确定 GST 速度切点,用于基于跌倒史识别跌倒者,并与 DGI 评分进行比较。
我们的研究结果表明,GST 可区分有跌倒风险的个体和无跌倒风险的个体。ROC 分析表明,基于跌倒史的 GST 的 AUC 为 0.92(≤100.5 度/秒标准值),基于 DGI 的 AUC 为 0.85(≤100.5 度/秒标准值),用于区分跌倒风险。当 GST 和 DGI 评分相结合时,该方案确定了 1.0 的 AUC(100%的敏感性,100%的特异性)用于识别跌倒风险。
根据跌倒史和 DGI 分类的参与者之间存在显著的头部运动速度差异。因此,GST 可能成为有跌倒史的老年人跌倒风险评估的潜在工具。建议 GST 与 DGI 联合使用,以准确识别有跌倒风险的个体,而不是单独使用。