TY - JOUR
T1 - Developing a machine learning algorithm to predict probability of retear and functional outcomes in patients undergoing rotator cuff repair surgery
T2 - protocol for a retrospective, multicentre study
AU - Allaart, Laurens J. H.
AU - Spanning, Sanne van
AU - Lafosse, Laurent
AU - Lafosse, Thibault
AU - Ladermann, Alexandre
AU - Athwal, George S.
AU - Hendrickx, Laurent A. M.
AU - Machine Learning Consortium
AU - Doornberg, Job N.
AU - van den Bekerom, Michel P. J.
AU - Buijze, Geert Alexander
N1 - Funding Information: This research has received funding by the SECEC/ESSSE 2020 Research Grant as part of the project 'The Effect of Risk Factors, Surgical Technique and Biomodulation on Tendon Healing after Rotator Cuff Repair'. Publisher Copyright: © 2023 BMJ Publishing Group. All rights reserved.
PY - 2023/2/10
Y1 - 2023/2/10
N2 - INTRODUCTION: The effectiveness of rotator cuff tear repair surgery is influenced by multiple patient-related, pathology-centred and technical factors, which is thought to contribute to the reported retear rates between 17% and 94%. Adequate patient selection is thought to be essential in reaching satisfactory results. However, no clear consensus has been reached on which factors are most predictive of successful surgery. A clinical decision tool that encompassed all aspects is still to be made. Artificial intelligence (AI) and machine learning algorithms use complex self-learning models that can be used to make patient-specific decision-making tools. The aim of this study is to develop and train an algorithm that can be used as an online available clinical prediction tool, to predict the risk of retear in patients undergoing rotator cuff repair. METHODS AND ANALYSIS: This is a retrospective, multicentre, cohort study using pooled individual patient data from multiple studies of patients who have undergone rotator cuff repair and were evaluated by advanced imaging for healing at a minimum of 6 months after surgery. This study consists of two parts. Part one: collecting all potential factors that might influence retear risks from retrospective multicentre data, aiming to include more than 1000 patients worldwide. Part two: combining all influencing factors into a model that can clinically be used as a prediction tool using machine learning. ETHICS AND DISSEMINATION: For safe multicentre data exchange and analysis, our Machine Learning Consortium adheres 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. Institutional Review Board approval does not apply to the current study protocol.
AB - INTRODUCTION: The effectiveness of rotator cuff tear repair surgery is influenced by multiple patient-related, pathology-centred and technical factors, which is thought to contribute to the reported retear rates between 17% and 94%. Adequate patient selection is thought to be essential in reaching satisfactory results. However, no clear consensus has been reached on which factors are most predictive of successful surgery. A clinical decision tool that encompassed all aspects is still to be made. Artificial intelligence (AI) and machine learning algorithms use complex self-learning models that can be used to make patient-specific decision-making tools. The aim of this study is to develop and train an algorithm that can be used as an online available clinical prediction tool, to predict the risk of retear in patients undergoing rotator cuff repair. METHODS AND ANALYSIS: This is a retrospective, multicentre, cohort study using pooled individual patient data from multiple studies of patients who have undergone rotator cuff repair and were evaluated by advanced imaging for healing at a minimum of 6 months after surgery. This study consists of two parts. Part one: collecting all potential factors that might influence retear risks from retrospective multicentre data, aiming to include more than 1000 patients worldwide. Part two: combining all influencing factors into a model that can clinically be used as a prediction tool using machine learning. ETHICS AND DISSEMINATION: For safe multicentre data exchange and analysis, our Machine Learning Consortium adheres 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. Institutional Review Board approval does not apply to the current study protocol.
KW - ORTHOPAEDIC & TRAUMA SURGERY
KW - Orthopaedic & trauma surgery
KW - Shoulder
UR - http://www.scopus.com/inward/record.url?scp=85147892772&partnerID=8YFLogxK
U2 - https://doi.org/10.1136/bmjopen-2022-063673
DO - https://doi.org/10.1136/bmjopen-2022-063673
M3 - Article
C2 - 36764713
SN - 2044-6055
VL - 13
SP - e063673
JO - BMJ Open
JF - BMJ Open
IS - 2
M1 - e063673
ER -