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首页> 外文期刊>Archives of Physical Medicine and Rehabilitation >Longitudinal description of the disability rating scale for individuals in the national institute on disability and rehabilitation research traumatic brain injury model systems national database
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Longitudinal description of the disability rating scale for individuals in the national institute on disability and rehabilitation research traumatic brain injury model systems national database

机译：国家伤残与康复研究所创伤性脑损伤模型系统国家数据库中个人的伤残等级量表的纵向描述

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Objective To develop a detailed understanding of temporal change (ie, estimated trajectories) at the individual level as measured by the Disability Rating Scale (DRS). Design Individual growth curve (IGC) analysis of retrospective data obtained from the National Institute on Disability and Rehabilitation Research Traumatic Brain Injury (TBI) Model Systems National Database. Setting Multicenter longitudinal database study. Participants Individuals with TBI (N=8816) participating in the TBI Model Systems National Database project. Interventions Not applicable. Main Outcome Measure DRS. Results The negative exponential consisting of 3 growth parameters (pseudointercept, asymptote, rate) was successfully used to predict trajectory of recovery on the DRS qualified by the following covariates: race, sex, level of education and age at admission, rehabilitation length of stay, and cognitive and motor FIM scores at rehabilitation admission. Based on these results, an interactive tool was developed to allow prediction of the trajectory of recovery for individuals and subgroups with specified characteristics on the selected covariates. Conclusions With the use of IGC analysis, the longitudinal trajectory of recovery on the DRS for individuals sharing common characteristics and traits can be described. This methodology allows researchers and clinicians to predict numerous individual-level trajectories through use of a web-based computer automated interactive tool.
机译：目的通过残疾评估量表（DRS）来详细了解个体水平的时间变化（即估计的轨迹）。从国家残疾与康复研究所创伤性脑损伤（TBI）模型系统国家数据库获得的回顾性数据的设计个人生长曲线（IGC）分析。设置多中心纵向数据库研究。参与者TBI（N = 8816）的个人参加了TBI模型系统国家数据库项目。干预措施不适用。主要指标DRS。结果成功地使用了由3个生长参数（伪截距，渐近线，比率）组成的负指数来预测DRS的康复轨迹，该变量具有以下协变量：种族，性别，入学时的教育程度和年龄，住院的康复时间，康复入院时的认知和运动FIM得分。基于这些结果，开发了一种交互式工具，可以预测在选定协变量上具有指定特征的个人和亚组的恢复轨迹。结论利用IGC分析，可以描述具有共同特征和特征的个体在DRS上的恢复的纵向轨迹。这种方法使研究人员和临床医生可以通过使用基于Web的计算机自动交互工具来预测许多个人级别的轨迹。

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