Armijo-Olivo Susan, Woodhouse Linda J, Steenstra Ivan A, Gross Douglas P
Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Alberta, Canada.
Department of Physical Therapy, University of Alberta, Edmonton, Alberta, Canada.
Occup Environ Med. 2016 Dec;73(12):807-815. doi: 10.1136/oemed-2016-103791. Epub 2016 Aug 24.
To determine whether the Disabilities of the Arm, Shoulder, and Hand (DASH) tool added to the predictive ability of established prognostic factors, including patient demographic and clinical outcomes, to predict return to work (RTW) in injured workers with musculoskeletal (MSK) disorders of the upper extremity.
A retrospective cohort study using a population-based database from the Workers' Compensation Board of Alberta (WCB-Alberta) that focused on claimants with upper extremity injuries was used. Besides the DASH, potential predictors included demographic, occupational, clinical and health usage variables. Outcome was receipt of compensation benefits after 3 months. To identify RTW predictors, a purposeful logistic modelling strategy was used. A series of receiver operating curve analyses were performed to determine which model provided the best discriminative ability.
The sample included 3036 claimants with upper extremity injuries. The final model for predicting RTW included the total DASH score in addition to other established predictors. The area under the curve for this model was 0.77, which is interpreted as fair discrimination. This model was statistically significantly different than the model of established predictors alone (p<0.001). When comparing the DASH total score versus DASH item 23, a non-significant difference was obtained between the models (p=0.34).
The DASH tool together with other established predictors significantly helped predict RTW after 3 months in participants with upper extremity MSK disorders. An appealing result for clinicians and busy researchers is that DASH item 23 has equal predictive ability to the total DASH score.
确定上肢、肩部和手部功能障碍(DASH)工具是否能增强已确定的预后因素(包括患者人口统计学和临床结果)的预测能力,以预测上肢肌肉骨骼(MSK)疾病受伤工人的复工情况。
采用基于人群的艾伯塔工人赔偿委员会(WCB - 艾伯塔)数据库进行回顾性队列研究,重点关注上肢受伤的索赔者。除了DASH工具外,潜在预测因素包括人口统计学、职业、临床和健康使用变量。结果是3个月后获得赔偿福利情况。为了确定复工预测因素,采用了有目的的逻辑建模策略。进行了一系列受试者工作特征曲线分析,以确定哪个模型具有最佳判别能力。
样本包括3036名上肢受伤的索赔者。预测复工的最终模型除了其他已确定的预测因素外,还包括DASH总分。该模型的曲线下面积为0.77,解释为中等判别能力。该模型与仅包含已确定预测因素的模型在统计学上有显著差异(p<0.001)。比较DASH总分与DASH第23项时,模型之间无显著差异(p = 0.34)。
DASH工具与其他已确定的预测因素一起,显著有助于预测上肢MSK疾病参与者3个月后的复工情况。对临床医生和忙碌的研究人员来说,一个吸引人的结果是DASH第23项与DASH总分具有同等的预测能力。