Do Aggregate Socioeconomic Status Factors Predict Outcomes for Total Knee Arthroplasty in a Rural Population?
Benjamin J. Keeney, PhD,1,2,*# Karl M. Koenig, MD, MS,1,2,3,^ Nicholas G. Paddock, B.Sc.,1 Wayne E. Moschetti, MD, MS,1,2 Michael B. Sparks, MD,1,2 and David S. Jevsevar, MD, MBA1,2Knee
Background
We sought to determine whether several pre-operative 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 2,198 TKAs at a high-volume rural tertiary academic hospital from April 2011 through March 2016. SES variables included race/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<$35,000 predicted longer LOS. Ethnic minority patients, the unemployed, and those living alone predicted facility discharge. Unemployed patients were less likely to achieve PCS improvement. Without the 5 SES variables, the AUC values of the LOS, discharge, and PCS models were 0.74 (95% CI 0.72–0.77, “acceptable”; 0.86 (CI 0.84–0.87, “excellent”); and 0.80 (CI 0.78–0.82, “excellent”), respectively. Including the 5 SES variables, the ten-fold cross-validated and bootstrapped AUC values were 0.76 (CI 0.74–0.79); 0.87 (CI 0.85–0.88); and 0.81 (0.79–0.83), respectively.
Conclusions
We developed validated predictive models for outcomes after TKA. Although inclusion of multiple SES variables provided statistical predictive value in our models, the amount of improvement may not be clinically meaningful.
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