Do Aggregate Socioeconomic Status Factors Predict Outcomes for Total Knee Arthroplasty in a Rural Population?
Do Aggregate Socioeconomic Status Factors Predict Outcomes for Total Knee Arthroplasty in a Rural Population?
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DOI：
10.1016/j.arth.2017.07.002
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发表时间：
2017-12-01
影响因子：
3.5
通讯作者：
Jevsevar, David S.
中科院分区：
文献类型：
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作者：
Keeney, Benjamin J.;Koenig, Karl M.;Jevsevar, David S.
背景资料：我们试图确定几个术前社会经济状况（SES）变量是否有意义地改善初次全膝关节置换术（TKA）住院时间（LOS）、出院时间和临床显著的退伍军人兰德-12身体成分评分（PCS）改善的预测模型。我们前瞻性收集了2011年4月至2016年3月在一家高容量农村三级学术医院进行的2198例TKA的临床数据。SES变量包括种族和/或民族、独居、教育、就业和家庭收入，以及沿着许多调整变量。我们确定了个体SES预测因子以及所有SES变量的纳入是否有助于模型受试者工作特征（AUC）下的每个10倍交叉验证面积。我们还使用了1000倍的自举方法，以确定是否SES和非SES models是统计学上不同的other.Results：至少有1 SES预测每个结果。少数民族患者和有收入者
Background: We sought to determine whether several preoperative socioeconomic status (SES) variables meaningfully improve predictive models for primary total knee arthroplasty (TKA) length of stay (LOS), facility discharge, and clinically significant Veterans RAND-12 physical component score (PCS) improvement.Methods: We prospectively collected clinical data on 2198 TKAs at a high-volume rural tertiary academic hospital from April 2011 through March 2016. SES variables included race and/or ethnicity, living alone, education, employment, and household income, along with numerous adjusting variables. We determined individual SES predictors and whether the inclusion of all SES variables contributed to each 10-fold cross-validated area under the model's area under the receiver operating characteristic (AUC). We also used 1000-fold bootstrapping methods to determine whether the SES and non-SES models were statistically different from each other.Results: At least 1 SES predicted each outcome. Ethnic minority patients and those with incomes