Development and training of a machine learning algorithm to identify patients at risk for recurrence following an arthroscopic Bankart repair (CLEARER): protocol for a retrospective, multicentre, cohort study

IntroductionShoulder instability is a common injury, with a reported incidence of 23.9 per 100 000 person-years. There is still an ongoing debate on the most effective treatment strategy. Non-operative treatment has recurrence rates of up to 60%, whereas operative treatments such as the Bankart repa...

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Bibliographic Details
Published in:	BMJ open Vol. 12; no. 9; p. e055346
Main Authors:	van Spanning, Sanne H, Verweij, Lukas P E, Allaart, Laurens J H, Hendrickx, Laurent A M, Doornberg, Job N, Athwal, George S, Lafosse, Thibault, Lafosse, Laurent, van den Bekerom, Michel P J, Buijze, Geert Alexander, Flinkillä, T, Nakagawa, S, Loppini, M, Waterman, B R, Owens, B, Gotoh, M, Nakamura, H, Rossi, L, Pasqualini, I, Scheibel, M, Minkus, M, Shaha, J S, Ruiz Ibán, M A, Li, R T, Lin, A, Kleinlugtenbelt, Y V, Woodmass, J M, MacDonald, P, Phadnis, J, Stone, A, Hatrick, C, van Iersel, T P
Format:	Journal Article
Language:	English
Published:	England British Medical Journal Publishing Group 08-09-2022 BMJ Publishing Group LTD BMJ Publishing Group
Series:	Protocol
Subjects:	Adult orthopaedics Algorithms Arthroscopy - adverse effects Arthroscopy - methods Artificial Intelligence Calibration Cohort analysis Cohort Studies Consortia Datasets Decision making Elbow & shoulder Humans Joint Instability - surgery Machine Learning Multicenter Studies as Topic Patients Recurrence Retrospective Studies Risk factors Shoulder Shoulder Joint - surgery Surgery Adult orthopaedics Shoulder Elbow & shoulder
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Summary:	IntroductionShoulder instability is a common injury, with a reported incidence of 23.9 per 100 000 person-years. There is still an ongoing debate on the most effective treatment strategy. Non-operative treatment has recurrence rates of up to 60%, whereas operative treatments such as the Bankart repair and bone block procedures show lower recurrence rates (16% and 2%, respectively) but higher complication rates (<2% and up to 30%, respectively). Methods to determine risk of recurrence have been developed; however, patient-specific decision-making tools are still lacking. Artificial intelligence and machine learning algorithms use self-learning complex models that can be used to make patient-specific decision-making tools. The aim of the current study is to develop and train a machine learning algorithm to create a prediction model to be used in clinical practice—as an online prediction tool—to estimate recurrence rates following a Bankart repair.Methods and analysisThis is a multicentre retrospective cohort study. Patients with traumatic anterior shoulder dislocations that were treated with an arthroscopic Bankart repair without remplissage will be included. This study includes two parts. Part 1, collecting all potential factors influencing the recurrence rate following an arthroscopic Bankart repair in patients using multicentre data, aiming to include data from >1000 patients worldwide. Part 2, the multicentre data will be re-evaluated (and where applicable complemented) using machine learning algorithms to predict outcomes. Recurrence will be the primary outcome measure.Ethics and disseminationFor safe multicentre data exchange and analysis, our Machine Learning Consortium adhered to the WHO regulation ‘Policy on Use and Sharing of Data Collected by WHO in Member States Outside the Context of Public Health Emergencies’. The study results will be disseminated through publication in a peer-reviewed journal. No Institutional Review Board is required for this study.
Bibliography:	Protocol ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2044-6055 2044-6055
DOI:	10.1136/bmjopen-2021-055346