Bone and Joint Institute
Perioperative Predictors of Length of Stay After Total Hip Arthroplasty
Document Type
Article
Publication Date
7-1-2016
Journal
Journal of Arthroplasty
Volume
31
Issue
7
First Page
1427
Last Page
1430
URL with Digital Object Identifier
10.1016/j.arth.2016.01.005
Abstract
© 2016 Elsevier Inc. Background: Few studies had examined whether specific patient variables or performance on functional testing can predict length of stay (LOS) after total hip arthroplasty (THA). Such tools would enable providers to minimize prolonged LOS by planning appropriate discharge dispositions preoperatively. Methods: We prospectively recruited 120 patients undergoing a THA through an anterior (n = 40), posterior (n = 40), or lateral (n = 40) approach. Patients performed a timed up-and-go (TUG) test preoperatively to determine if it was predictive of hospital LOS after THA. Other variables of interest included patient age, body mass index, age-adjusted Charlson Comorbidity Index, mean procedure time, and time spent in the postanesthetic care unit. A logistic regression analysis was performed to determine which variables predicted LOS greater than 48 hours, which is our institution's target time to discharge. Results: The TUG test was predictive of LOS beyond 48 hours. For every 5-second interval increase in TUG time, patients were twice as likely to stay in hospital beyond 48 hours (odds ratio [OR] = 2.02, 95% confidence interval [CI] = 1.02-4.01, P = .043). Patient age (OR = 0.97, 95% CI = 0.90-1.05, P = .46), body mass index (OR = 1.01, 95% CI = 0.86-1.18, P = .90), Charlson Comorbidity Index (OR = 1.29, 95% CI = 0.68-2.44, P = .44), mean procedure time (OR = 1.05, 95% CI = 0.97-1.14, P = .27), and mean time in the postanesthetic care unit (OR = 1.00, 95% CI = 0.99-1.00, P = .94) were not predictive of increased LOS. Conclusion: The TUG test was predictive of hospital LOS after THA. It is a simple functional test that can be used to assist with discharge planning preoperatively to minimize extended hospital stays.
