From the Department of Physical Medicine and Rehabilitation (A.W.B., B.A.S.) and Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN (T.M.T.); Uniform Data System for Medical Rehabilitation, Buffalo, NY (P.M.N., C.V.G.); Department of Health Care Studies, Daemen College, Amherst, NY (P.M.N.); and the Department of Neurology, University at Buffalo, Buffalo, NY (C.V.G.).
Stroke. 2015 Apr;46(4):1038-44. doi: 10.1161/STROKEAHA.114.007392. Epub 2015 Feb 24.
Identifying clinical data acquired at inpatient rehabilitation admission for stroke that accurately predict key outcomes at discharge could inform the development of customized plans of care to achieve favorable outcomes. The purpose of this analysis was to use a large comprehensive national data set to consider a wide range of clinical elements known at admission to identify those that predict key outcomes at rehabilitation discharge.
Sample data were obtained from the Uniform Data System for Medical Rehabilitation data set with the diagnosis of stroke for the years 2005 through 2007. This data set includes demographic, administrative, and medical variables collected at admission and discharge and uses the FIM (functional independence measure) instrument to assess functional independence. Primary outcomes of interest were functional independence measure gain, length of stay, and discharge to home.
The sample included 148,367 people (75% white; mean age, 70.6±13.1 years; 97% with ischemic stroke) admitted to inpatient rehabilitation a mean of 8.2±12 days after symptom onset. The total functional independence measure score, the functional independence measure motor subscore, and the case-mix group were equally the strongest predictors for any of the primary outcomes. The most clinically relevant 3-variable model used the functional independence measure motor subscore, age, and walking distance at admission (r(2)=0.107). No important additional effect for any other variable was detected when added to this model.
This analysis shows that a measure of functional independence in motor performance and age at rehabilitation hospital admission for stroke are predominant predictors of outcome at discharge in a uniquely large US national data set.
识别住院康复治疗中风患者入院时获得的临床数据,这些数据可准确预测出院时的关键结果,为制定个性化护理计划以实现良好的结果提供依据。本分析的目的是利用大型综合国家数据集考虑入院时已知的广泛临床要素,以确定可预测康复出院关键结果的要素。
样本数据来自 2005 年至 2007 年的统一医疗康复数据系统中风诊断。该数据集包括入院和出院时收集的人口统计学、行政和医疗变量,并使用功能独立性测量(functional independence measure,FIM)工具评估功能独立性。主要关注的结果是 FIM 增益、住院时间和出院回家。
样本包括 148367 名患者(75%为白人;平均年龄 70.6±13.1 岁;97%为缺血性中风),平均在发病后 8.2±12 天入住住院康复治疗。总 FIM 评分、FIM 运动子评分和病例组合组是所有主要结果的最强预测因素。最具临床相关性的 3 变量模型使用 FIM 运动子评分、年龄和入院时的步行距离(r²=0.107)。当将此模型添加到其他变量时,没有发现任何其他变量的重要附加效果。
本分析表明,中风患者康复医院入院时的运动功能和年龄是美国大型国家数据集中出院时结果的主要预测因素。