Background and purpose — Up to 20% of patients are dissatisfied after total knee arthroplasty (TKA), mainly because of pain and restricted physical function. We developed a prediction model for 6-month knee range of motion, knee pain, and walking limitations in patients undergoing TKA surgery. Patients and methods — We performed a prospective cohort study of 4,026 patients who underwent elective, primary TKA between July 2013 and July 2017. Candidate predictors included demographic, clinical, psychosocial, and preoperative outcome measures. The outcomes of interest were (i) knee extension and flexion range of motion, (ii) knee pain rated on a 5-point ordinal scale, and (iii) self-reported maximum walk time at 6 months post TKA. For each outcome, we fitted a multivariable proportional odds regression model with bootstrap internal validation. Results — At 6 months post TKA, around 5% to 20% of patients had a flexion contracture ³ 10°, range of motion < 90°, moderate to severe knee pain, or a maximum walk time £ 15 minutes. The model c-indices (the probabilities to correctly discriminate between 2 patients with different levels of follow-up TKA outcomes) when evaluating these patients were 0.71, 0.79, 0.65, and 0.76, respectively. Each postoperative outcome was strongly influenced by the same outcome measure obtained preoperatively (all p-values < 0.001). Additional statistically significant predictors were age, sex, race, education level, diabetes mellitus, preoperative use of gait aids, contralateral knee pain, and psychological distress (all p-values < 0.001). Interpretation — We have developed models to predict, for individual patients, their likely post-TKA levels of knee extension and flexion range of motion, knee pain, and walking limitations. After external validation, they can potentially be used preoperatively to identify at-risk patients and to help patients set more realistic expectations about surgical outcomes.

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Purpose Machine-learning methods are flexible prediction algorithms with potential advantages over conventional regression. This study aimed to use machine learning methods to predict post-total knee arthroplasty (TKA) walking limitation, and to compare their performance with that of logistic regression. Methods From the department's clinical registry, a cohort of 4026 patients who underwent elective, primary TKA between July 2013 and July 2017 was identified. Candidate predictors included demographics and preoperative clinical, psychosocial, and outcome measures. The primary outcome was severe walking limitation at 6 months post-TKA, defined as a maximum walk time ≤ 15 min. Eight common regression (logistic, penalized logistic, and ordinal logistic with natural splines) and ensemble machine learning (random forest, extreme gradient boosting, and SuperLearner) methods were implemented to predict the probability of severe walking limitation. Models were compared on discrimination and calibration metrics. Results At 6 months post-TKA, 13% of patients had severe walking limitation. Machine learning and logistic regression models performed moderately [mean area under the ROC curves (AUC) 0.73-0.75]. Overall, the ordinal logistic regression model performed best while the SuperLearner performed best among machine learning methods, with negligible differences between them (Brier score difference, < 0.001; 95% CI [− 0.0025, 0.002]). Conclusions When predicting post-TKA physical function, several machine learning methods did not outperform logistic regression-in particular, ordinal logistic regression that does not assume linearity in its predictors. Level of evidence Prognostic level II

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Rehabilitation attendance post-TKA is associated with an increase in self-report physical function. Among patients who attended rehabilitation, a modest dose-response relationship was observed between the number of sessions and functional outcomes.