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Titre : | Predicting Mobility Limitations in Patients With Total Knee Arthroplasty in the Inpatient Setting (2019) |
Auteurs : | Eleanor Shu-Xian Chew ; Seng-Jin Yeo ; Terry Haines |
Type de document : | Article |
Dans : | Archives of Physical Medicine and Rehabilitation (Vol. 100, n° 11, 2019) |
Article en page(s) : | p. 2106-2112 |
Langues: | Anglais |
Descripteurs : |
HE Vinci Arthroplastie prothétique de genou ; Genou ; Modèles statistiques ; Pronostic ; Réadaptation ; Risque |
Résumé : |
Objective
To develop a prediction model for postoperative day 3 mobility limitations in patients undergoing total knee arthroplasty (TKA). Design Prospective cohort study. Setting Inpatients in a tertiary care hospital. Participants A sample of patients (N=2300) who underwent primary TKA in 2016-2017. Interventions Not applicable. Main Outcome Measure Candidate predictors included demographic variables and preoperative clinical and psychosocial measures. The outcome of interest was mobility limitations on post-TKA day 3, and this was determined a priori by an ordinal mobility outcome hierarchy based on the type of the gait aids prescribed and the level of physiotherapist assistance provided. To develop the model, we fitted a multivariable proportional odds regression model with bootstrap internal validation. We used a model approximation approach to create a simplified model that approximated predictions from the full model with 95% accuracy. Results On post-TKA day 3, 11% of patients required both walkers and therapist assistance to ambulate safely. Our prediction model had a concordance index of 0.72 (95% confidence interval, 0.68-0.75) when evaluating these patients. In the simplified model, predictors of greater mobility limitations included older age, greater walking aid support required preoperatively, less preoperative knee flexion range of movement, low-volume surgeon, contralateral knee pain, higher body mass index, non-Chinese race, and greater self-reported walking limitations preoperatively. Conclusion We have developed a prediction model to identify patients who are at risk for mobility limitations in the inpatient setting. When used preoperatively as part of a shared-decision making process, it can potentially influence rehabilitation strategies and facilitate discharge planning. |
Disponible en ligne : | Oui |
En ligne : | https://login.ezproxy.vinci.be/login?url=https://www.sciencedirect.com/science/article/pii/S0003999319303855 |