Burnett D M, Cifu D X, Kolakowsky-Hayner S, Kreutzer J S
Department of Physical Medicine and Rehabilitation, Medical College of Virginia/Virginia Commonwealth University, Richmond, VA, USA.
Arch Phys Med Rehabil. 2001 Jan;82(1):114-9. doi: 10.1053/apmr.2001.18042.
To describe the distribution of charges, to distinguish between "charge outliers" and nonoutliers, and to identify a model that uses demographics and injury characteristics to predict charge outlier status in individuals with spinal cord injury (SCI).
Retrospective data analysis of patients admitted to 24 acute inpatient rehabilitation national Spinal Cord Injury Model Systems centers. Statistical analysis, including proportions, means, and standard deviations (SDs), were compiled for the following variables: demographic and injury information, rehabilitation charges, medical complications, associated injuries, and surgical procedures.
Tertiary, university medical centers participating in the National Institute on Disability and Rehabilitation Research's SCI Model Systems project.
A total of 13,392 patients who were admitted to 24 acute, intensive, interdisciplinary rehabilitation settings after traumatic SCI between November 1972 and August 1996.
Statistical data analysis was used to determine significance between charge outliers and nonoutliers on the basis of demographic, injury characteristics, and clinical factors. Outliers, under the current diagnostic-related group system, are defined as cases in which lengths of stay exceed the mean by the lesser of 20 days or 1.94 SDs.
Statistically significant differences were found between SCI charge outliers and nonoutliers based on ethnicity, education, employment, level of injury, American Spinal Injury Association impairment classification, and sponsor of hospitalization. On average, outliers were 4 years older than nonoutliers, and tended to have more associated injuries, pressure ulcers, surgical procedures, and medical complications. A forward-conditional stepwise multiple logistic regression analysis was used to confirm univariate analysis and to predict the presence or absence of outliers based on the predictor variables. A model for the prediction of SCI charge outlier status was defined.
SCI charge outliers are most likely to be retired, insured, have high cervical level injuries, and be educated beyond high school. Improved treatment efficiency serves as a means of cost reduction and is a reason to identify outlier characteristics.
描述费用分布情况，区分“费用异常值”与非异常值，并确定一种利用人口统计学和损伤特征来预测脊髓损伤（SCI）患者费用异常值状态的模型。
对24个急性住院康复全国脊髓损伤模型系统中心收治的患者进行回顾性数据分析。针对以下变量编制了统计分析，包括比例、均值和标准差（SD）：人口统计学和损伤信息、康复费用、医疗并发症、相关损伤和外科手术。
参与美国国立残疾与康复研究所SCI模型系统项目的三级大学医学中心。
1972年11月至1996年8月期间因创伤性SCI后入住24个急性、强化、跨学科康复机构的13392例患者。
采用统计数据分析来确定费用异常值与非异常值在人口统计学、损伤特征和临床因素方面的显著性差异。在当前的诊断相关分组系统下，异常值定义为住院时间超过均值且超出天数为20天或1.94个标准差中较小值的病例。
基于种族、教育程度、就业情况、损伤水平、美国脊髓损伤协会损伤分级和住院资助方，发现SCI费用异常值与非异常值之间存在统计学显著差异。平均而言，异常值患者比非异常值患者大4岁，且往往有更多的相关损伤、压疮、外科手术和医疗并发症。采用向前条件逐步多元逻辑回归分析来确认单变量分析，并根据预测变量预测异常值的存在与否。定义了一种预测SCI费用异常值状态的模型。
SCI费用异常值患者最有可能已退休、有保险、颈椎高位损伤且受过高中以上教育。提高治疗效率是降低成本的一种方式，也是识别异常值特征的一个原因。
