2022
DOI: 10.1016/j.artd.2022.07.011
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Machine Learning Algorithm to Predict Worsening of Flexion Range of Motion After Total Knee Arthroplasty

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Cited by 7 publications
(3 citation statements)
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“…Thus, differences could have been associated with the patient cohort's age. Previous research has identified associations between poorer knee flexion range and increased age and other factors such as higher BMI, suggesting that these variables should be considered as factors at play [23]. However, the relationship between age and function remains contested in the literature, with other authors reporting no difference in knee flexion ranges between younger (<75 years old) and older (>80 years old) patients [24].…”
Section: Discussionmentioning
confidence: 99%
“…Thus, differences could have been associated with the patient cohort's age. Previous research has identified associations between poorer knee flexion range and increased age and other factors such as higher BMI, suggesting that these variables should be considered as factors at play [23]. However, the relationship between age and function remains contested in the literature, with other authors reporting no difference in knee flexion ranges between younger (<75 years old) and older (>80 years old) patients [24].…”
Section: Discussionmentioning
confidence: 99%
“…The relative importance of preoperative ROM in determining ROM following TKA is unclear with some studies reporting that preoperative ROM is an important predictor of postoperative ROM 15 , 22 , 23 and other studies finding more important predictors of postoperative ROM than preoperative ROM. 24 , 25 In fact, the relationship between preoperative and postoperative ROM following TKA may be nonlinear with patients with the highest preoperative ROM tending to experience the least improvement ROM and those with the worst preoperative ROM tending to gain the most ROM. 25 , 26 …”
Section: Discussionmentioning
confidence: 99%
“…A machine learning algorithm was able to predict the worsening of the flexion range of motion after primary TKA. The random-forest model had the best accuracy, and variables of importance were joint-line change, postoperative femorotibial angle, and hemoglobin A1c 64 . Yeramosu et al 65 compared a multivariable logistic regression model and diverse machine learning techniques and found that 1 of the models (random forest) had an accuracy (AUC) of 0.810 for identifying candidates for same-day discharge after revision TKA.…”
Section: Machine Learning and Artificial Intelligencementioning
confidence: 99%