Stata statistical software release 17 2021

Univariate chi-square analysis was used to analyze differences in indication for revision among BMI classes. Multivariable logistic regression models controlling for age, sex, race/ethnicity, payer status, hospital setting, and ECI were then constructed to examine the effect of BMI on likelihood of rTHA being due to each different indication for revision. The reference group for the models was normal-weight patients, which lacked ICD-9 codes for any BMI class.
Data were analyzed using Stata Statistical Software: Release 17; 2021 (StataCorp; College Station, TX, USA). A P value of < 0.05 was considered statistically significant.

Differences in discharge destination, return to OR, readmission, and postoperative complications were analyzed using univariate chi-square analysis, and differences in LOS and operative time using two-tailed t-tests. Multivariable logistic and linear regression models (depending on whether the outcome was categorical or continuous, respectively) controlling for the above-listed covariates were then constructed to examine the effect of significant preoperative weight loss on each outcome. Data were analyzed using Stata Statistical Software: Release 17; 2021 (StataCorp; College Station, TX, USA). A P value of <0.05 was considered statistically significant.