ArticleJournal of orthopaedics2025
Risk assessment model used to predict discharge care after total hip and total knee arthroplasty: A population-based study.
Article in Journal of orthopaedics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Transfer to a post-acute care facility or hospital readmission after total joint arthroplasty represent additional costs and increased surgical and health care resource utilization. Accurate prediction of post-acute care factors could help providers to plan the patient's discharge destination and have a positive impact on postoperative outcomes and readmission rates. Objective: To develop a risk assessment model to predict discharge care after total hip arthroplasty (THA) and total knee arthroplasty (TKA). Design: A retrospective longitudinal observational study. Settings: and participants: This study included 209 patients who underwent primary unilateral THA or TKA at a major academic medical center in Switzerland from January 2018 to December 2019. Methods: A collection of computerized- and paper-recorded data identified the discharge destination, socio-demographic factors, comorbidities, and other factors related to the patient. Univariate and multivariate analyses were performed to describe the predictors of post-surgical discharge destinations. Results: The characteristics associated with post-acute care after primary unilateral THA or TKA were the absence of a caregiver, advanced age, female gender, presence of walking aids, high ASA score, and comorbidity severity. A prediction model demonstrated that these six characteristics were associated 52 % with discharge to a post-acute care destination. Conclusions: This study allowed us to identify predictors of discharge to a post-surgical destination. Predictive models can be efficiently used to better predict which patients are predisposed to post-acute care after hospital discharge. Further studies are needed to determine the optimal criteria for different destinations.
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