Orthopaedic Surgery, Clinique Générale Annecy, Annecy, Auvergne-Rhône-Alpes, France
Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
BMJ Open. 2023 Feb 10;13(2):e063673. doi: 10.1136/bmjopen-2022-063673.
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.
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.
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.
肩袖撕裂修复手术的效果受多种与患者相关、以病理为中心和技术因素的影响，这被认为是导致报告的再撕裂率在 17%至 94%之间的原因。充分的患者选择被认为是达到满意结果的关键。然而，对于哪些因素最能预测手术的成功，目前仍未达成明确共识。一种包含所有方面的临床决策工具仍有待制定。人工智能（AI）和机器学习算法使用复杂的自我学习模型，可用于制定针对特定患者的决策工具。本研究旨在开发和训练一种算法，可作为在线临床预测工具，预测接受肩袖修复的患者再撕裂的风险。
这是一项回顾性、多中心队列研究，使用来自多个肩袖修复患者的多中心研究的 pooled 个体患者数据，这些患者在手术后至少 6 个月通过先进的影像学检查评估愈合情况。本研究包括两部分。第一部分：从回顾性多中心数据中收集所有可能影响再撕裂风险的潜在因素，目标是纳入全球超过 1000 名患者。第二部分：将所有影响因素结合到一个模型中，该模型可以通过机器学习在临床上用作预测工具。
为了安全地进行多中心数据交换和分析，我们的机器学习联盟遵守世界卫生组织关于“在非突发公共卫生事件情况下，世卫组织会员国使用和共享数据的政策”的规定。研究结果将通过在同行评议的期刊上发表来传播。本研究方案不需要机构审查委员会的批准。
