Abstract
BACKGROUND: In January 2020, The Centers for Medicare and Medicaid Services approved total knee arthroplasty (TKA) to be performed in ambulatory surgery centers (ASCs). This study aims to develop a predictive model for targeting appropriate patients for ASC-based TKA. METHODS: A retrospective review of 2266 patients (205 same-day discharge [SDD; 9.0%] and 2061 one-day length of stay [91.0%]) undergoing TKA at a regional medical center between July 2016 and September 2020 was conducted. Multiple logistic regression was used to evaluate predictors of SDD, as these patients represent those most likely to safely undergo TKA in an ASC. RESULTS: Controlling for other demographics and comorbidities, patients with the following characteristics were at reduced odds of SDD: increased age (odds ratio [OR] = 0.935, P < .001), body mass index ≥35 (OR = 0.491, P = .002), female (OR = 0.535, P < .001), nonwhite race (OR = 0.456, P = .003), primary hypertension (OR = 0.710, P = .032), ≥3 comorbidities (OR = 0.507, P = .002), American Society of Anesthesiologists score ≥3 (OR = 0.378, P < .001). The model was deemed to be of adequate fit using the Hosmer and Lemeshow test (χ(2) = 12.437, P = .112), and the area under the curve was found to be 0.773 indicating acceptable discrimination. CONCLUSION: For patients undergoing primary TKA, increased age, body mass index ≥35, female gender, nonwhite race, primary hypertension, ≥3 comorbidities, and American Society of Anesthesiologists score ≥3 decrease the likelihood of SDD. A predictive model based on readily available patient presentation and comorbidity characteristics may aid surgeons in identifying patients that are candidates for SDD or ASC-based TKA.