Developing a machine learning algorithm to predict the probability of aseptic loosening of the glenoid component after anatomical total shoulder arthroplasty

Developing a machine learning algorithm to predict the probability of aseptic loosening of the glenoid component after anatomical total shoulder arthroplasty: protocol for a retrospective, multicentre study

Macken, Arno Alexander (Erasmus MC; Clinique Generale Annecy)
Macken, Loïc C. (Vrije Universiteit Amsterdam)
Oosterhoff, J.H.F. (TU Delft Information and Communication Technology)
Boileau, Pascal (Centre Hospitalier Universitaire de Nice)
Athwal, George S. (Schulich School of Medicine and Dentistry, London Ontario)
Doornberg, Job N. (University Medical Center Groningen)
Lafosse, Laurent (Clinique Generale Annecy)
Lafosse, Thibault (Clinique Generale Annecy)
van den Bekerom, Michel P.J. (Vrije Universiteit Amsterdam; Onze Lieve Vrouwe Gasthuis)
Buijze, Geert Alexander (Clinique Generale Annecy; Hôpital Lapeyronie)

2023

INTRODUCTION: Despite technological advancements in recent years, glenoid component loosening remains a common complication after anatomical total shoulder arthroplasty (ATSA) and is one of the main causes of revision surgery. Increasing emphasis is placed on the prevention of glenoid component failure. Previous studies have successfully predicted range of motion, patient-reported outcomes and short-term complications after ATSA using machine learning methods, but an accurate predictive model for (glenoid component) revision is currently lacking. This study aims to use a large international database to accurately predict aseptic loosening of the glenoid component after ATSA using machine learning algorithms. METHODS AND ANALYSIS: For this multicentre, retrospective study, individual patient data will be compiled from previously published studies reporting revision of ATSA. A systematic literature search will be performed in Medline (PubMed) identifying all studies reporting outcomes of ATSA. Authors will be contacted and invited to participate in the Machine Learning Consortium by sharing their anonymised databases. All databases reporting revisions after ATSA will be included, and individual patients with a follow-up less than 2 years or a fracture as the indication for ATSA will be excluded. First, features (predictive variables) will be identified using a random forest feature selection. The resulting features from the compiled database will be used to train various machine learning algorithms (stochastic gradient boosting, random forest, support vector machine, neural network and elastic-net penalised logistic regression). The developed and validated algorithms will be evaluated across discrimination (c-statistic), calibration, the Brier score and the decision curve analysis. The best-performing algorithm will be used to create an open-access online prediction tool. ETHICS AND DISSEMINATION: Data will be collected adhering to the WHO regulation on data sharing. An Institutional Review Board review is not applicable. The study results will be published in a peer-reviewed journal.

Clinical Decision-Making
Health informatics
ORTHOPAEDIC & TRAUMA SURGERY
Risk Factors
Shoulder

http://resolver.tudelft.nl/uuid:abad5de3-f63f-4468-8d14-ceb402843e03

https://doi.org/10.1136/bmjopen-2023-074700

2044-6055

BMJ Open, 13 (10), e074700

Institutional Repository

journal article

© 2023 Arno Alexander Macken, Loïc C. Macken, J.H.F. Oosterhoff, Pascal Boileau, George S. Athwal, Job N. Doornberg, Laurent Lafosse, Thibault Lafosse, Michel P.J. van den Bekerom, Geert Alexander Buijze