Castellini Greta, Gianola Silvia, Stucovitz Elena, Tramacere Irene, Banfi Giuseppe, Moja Lorenzo
IRCCS Istituto Ortopedico Galeazzi, Unit of Clinical Epidemiology.
IRCCS Istituto Ortopedico Galeazzi, Motion Analysis Laboratory.
Medicine (Baltimore). 2019 Sep;98(39):e17105. doi: 10.1097/MD.0000000000017105.
We aimed to determine the accuracy and failure of OAK device, an automated screening, for the assessment of fall risk in a prospective cohort of healthy adults aged over 65 years. The algorithm for fall risk assessment of the centers for disease control and prevention (CDC) was used as reference standard. Of the 183 individuals recruited, the CDC algorithm classified 80 as being at moderate/high risk and 103 at low risk of falling. OAK device failure incidence was 4.9% (confidence interval [CI] upper limit 7.7%), below the preset threshold for futility-early termination of the study (i.e., not above 15%). The OAK device showed a sensitivity of 84% and a specificity of 67% (receiver operating characteristic [ROC] area 82%; 95% confidence interval [CI] 76-88%), not reaching the preplanned target sensitivity (not lower than 85%). Diagnostic accuracy was not far from the sensitivity levels similar to those obtained with other fall risk assessment. However, some limitations can be considered.ClinicalTrials.gov identifier: NCT02655796.
我们旨在确定用于评估65岁以上健康成年人跌倒风险的自动化筛查设备OAK的准确性和失败率。以疾病控制与预防中心(CDC)的跌倒风险评估算法作为参考标准。在招募的183名个体中,CDC算法将80人分类为中度/高跌倒风险,103人分类为低跌倒风险。OAK设备的失败发生率为4.9%(置信区间[CI]上限为7.7%),低于预先设定的无效性研究早期终止阈值(即不高于15%)。OAK设备的敏感性为84%,特异性为67%(受试者操作特征曲线[ROC]面积为82%;95%置信区间[CI]为76 - 88%),未达到预先计划的目标敏感性(不低于85%)。诊断准确性与其他跌倒风险评估所获得的敏感性水平相近。然而,仍存在一些局限性。ClinicalTrials.gov标识符:NCT02655796。