ArticleKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2020
Machine learning methods are comparable to logistic regression techniques in predicting severe walking limitation following total knee arthroplasty.
Article in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in total and unicompartmental knee arthroplasty.BMC musculoskeletal disorders · 2024Pooled it
- Machine learning prediction models for diabetic kidney disease: systematic review and meta-analysis.Endocrine · 2024Pooled it
- Machine learning in knee arthroplasty: specific data are key-a systematic review.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2022Pooled it
- Joint-specific measures improve risk adjustment in total knee arthroplasty: A machine learning approach.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Article
- Article
- Current Concepts in Artificial Intelligence-Assisted Arthroplasty: A Review of the Perioperative Pathway.Cureus · 2025Review
- A development of machine learning models to preoperatively predict insufficient clinical improvement after total knee arthroplasty.Journal of orthopaedic surgery and research · 2025Article
- Using artificial intelligence to predict patient outcomes from patient-reported outcome measures: a scoping review.Health and quality of life outcomes · 2025Article
- Comparing prediction accuracy for 30-day readmission following primary total knee arthroplasty: the ACS-NSQIP risk calculator versus a novel artificial neural network model.Knee surgery & related research · 2025Article
- Global mapping of institutional and hospital-based (Level II-IV) arthroplasty registries: a scoping review.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2024Article
- Helicobacter Pylori Infection as the Predominant High-Risk Factor for Gastric Cancer Recurrence Post-Gastrectomy: An 8-Year Multicenter Retrospective Study.International journal of general medicine · 2024Article
- A decision support system for upper limb rehabilitation robot based on hybrid reasoning with RBR and CBR.Frontiers in bioengineering and biotechnology · 2024Article
- Optimizing thyroid AUS nodules malignancy prediction: a comprehensive study of logistic regression and machine learning models.Frontiers in endocrinology · 2024Article
- A predictive score of high tibial osteotomy survivorship to help in surgical decision-making: the SKOOP score.Archives of orthopaedic and trauma surgery · 2023Article
- A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty.Arthroplasty (London, England) · 2023Review
- Challenges of Integrating New Technologies for Orthopedic Doctors to Face up to Difficulties during the Pandemic Era.Healthcare (Basel, Switzerland) · 2023Article
- Prediction of complications and surgery duration in primary TKA with high accuracy using machine learning with arthroplasty-specific data.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2023Article
- Identification of high-risk factors associated with mortality at 1-, 3-, and 5-year intervals in gastric cancer patients undergoing radical surgery and immunotherapy: an 8-year multicenter retrospective analysis.Frontiers in cellular and infection microbiology · 2023Article
- Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data.Internal and emergency medicine · 2022Article
- Artificial intelligence in knee arthroplasty: current concept of the available clinical applications.Arthroplasty (London, England) · 2022Review
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeMachine-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.
methodsFrom 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.
resultsAt 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]).
conclusionsWhen 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.