We are pleased to share with you a recent publication in the Canadian Journal of Surgery. This publication is entitled "Not all total joint replacement patients are created equal: preoperative factors and length of stay in hospital."
Find the abstract below and click here to access the full-version of the article.
Winemaker M, Petruccelli D, Kabali C, de Beer J. Not All Total Joint Replacement Patients are Created Equal: Preoperative Factors and Length of Stay In-Hospital. Canadian Journal of Surgery 2015; 58(3): 160-6.
BACKGROUND: We conducted a cross-sectional study of primary total joint replacement (TJR) patients to determine predictors for prolonged length of stay (LOS) in hospital to identify patient characteristics that may inform resource allocation, accounting for patient complexity.
METHODS: Preoperative demographics, medical comorbidities and acute hospital LOS from a consecutive series of primary TJR patients from an academic arthroplasty centre were abstracted. We categorized patients as LOS of 3 or fewer days, 4 days, or 5 or more days to align results with varying LOS benchmarks. To identify predictors for LOS, we used a generalized logistic regression model fitted on an LOS ternary outcome, using LOS of 3 or fewer days as a reference category.
The sample included 1459 patients: 61.7% total knee and 38.3% total hip. Male sex was predictive of an LOS of 3 or fewer days (4 d: odds ratio [OR] 0.48, 95% confidence interval [CI] 0.364-0.631; ≥ 5 d: OR 0.57, 95% CI 0.435-0.758), as was current smoking status (4 d: OR 0.425, 95% CI 0.274-0.659; ≥ 5 d: OR 0.489, 95% CI 0.314-0.762). Strong predictors of prolonged LOS included total hip versus total knee arthroplasty, age 75 years or older, American Society of Anesthesiologists classification of 3 and 4 and number of cardiovascular comorbidities.
CONCLUSION: Not all patients undergoing TJR are equal. The goal should be individual patient-focused care rather than a predetermined LOS that is not achievable for all patients. Hospital resource planning must account for patient complexity when planning future bed management.