ArticleBMC musculoskeletal disorders2024
Factors to improve odds of success following medial opening-wedge high tibial osteotomy: a machine learning analysis.
Article in BMC musculoskeletal disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Article
- Artificial intelligence in knee osteotomy: evolution of alignment assessment and surgical decision-making.Journal of robotic surgery · 2026Review
- Factors affecting outcomes of intra-articular injection of autologous, culture-expanded, adipose-derived mesenchymal stem cells in knee osteoarthritis.Stem cells translational medicine · 2026Observational
- High survival rates and patient satisfaction 12 years after medial open wedge high tibial osteotomy surgery: A prospective cohort study.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Article
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5 authors.
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Abstract
backgroundAlthough high tibial osteotomy (HTO) is an established treatment option for medial compartment osteoarthritis, predictive factors for HTO treatment success remain unclear. This study aimed to identify informative variables associated with HTO treatment success and to develop and internally validate machine learning algorithms to predict which patients will achieve HTO treatment success for medial compartmental osteoarthritis.
methodsThis study retrospectively reviewed patients who underwent medial opening-wedge HTO (MOWHTO) at our center between March 2010 and December 2015. The primary outcomes were a lack of conversion to total knee arthroplasty (TKA) and achievement of a minimal clinically important difference of improvement in the Knee Injury and Osteoarthritis Outcome Score (KOOS) at a minimum of five years postoperatively. Recursive feature selection was used to identify the combination of variables from an initial pool of 25 features that optimized model performance. Five machine learning algorithms (XGBoost, multilayer perception, support vector machine, elastic-net penalized logistic regression, and random forest) were trained using five-fold cross-validation three times and applied to an independent test set of patients. The performance of the model was evaluated by the area under the receiver operating characteristic curve (AUC).
resultsA total of 231 patients were included, and 200 patients (86.6%) achieved treatment success at the mean of 9 years of follow-up. A combination of seven variables optimized algorithm performance, and the following specific cutoffs increased the likelihood of MOWHTO treatment success: body mass index (BMI) ≤ 26.8 kg/m
conclusionsThe random forest machine learning algorithm best predicted MOWHTO treatment success. Patients with a lower BMI, poor clinical status, slight valgus overcorrection, and postoperative MPTA < 94.7 more frequently achieved a greater likelihood of treatment success. LEVEL OF EVIDENCE: Level III, retrospective cohort study.
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