Surgical factors play a critical role in predicting functional outcomes using machine learning in robotic-assisted total knee arthroplasty.

Journal: Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA

PMID: 38819941

Abstract

PURPOSE: Predictive models help determine predictive factors necessary to improve functional outcomes after total knee arthroplasty (TKA). However, no study has assessed predictive models for functional outcomes after TKA based on the new concepts of personalised surgery and new technologies. This study aimed to develop and evaluate predictive modelling approaches to predict the achievement of minimal clinically important difference (MCID) in patient-reported outcome measures (PROMs) 1 year after TKA.

Authors

Lisa Spahn Lundgren

Stryker Department, Amsterdam, The Netherlands.
Nathalie Willems

Stryker Department, Amsterdam, The Netherlands.
Robert C Marchand

Orthopedic Surgery Department, South County Orthopaedics, Ortho Rhode Island, Wakefield, Rhode Island, USA.
Cécile Batailler

Department of Orthopedic Surgery and Sport Medicine, FIFA Medical Centre of Excellence, Hôpital de La Croix Rousse - Lyon University, 103 Grande Rue de La Croix Rousse, 69004, Lyon, France.
Sébastien Lustig

Department of Orthopedic Surgery and Sport Medicine, FIFA Medical Centre of Excellence, Hôpital de La Croix Rousse - Lyon University, 103 Grande Rue de La Croix Rousse, 69004, Lyon, France.

Keywords

Aged Arthroplasty, Replacement, Knee Female Humans Machine Learning Male Middle Aged Minimal Clinically Important Difference Osteoarthritis, Knee Patient Reported Outcome Measures Recovery of Function Retrospective Studies Robotic Surgical Procedures Treatment Outcome

External Resources

View on PubMed Access via DOI PubMed (38819941)

Surgical factors play a critical role in predicting functional outcomes using machine learning in robotic-assisted total knee arthroplasty.

Abstract

Authors

Keywords

External Resources

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