Orthopaedic Surgery, OLVG, Amsterdam, Noord-Holland, The Netherlands
Hand, Upper Limb, Peripheral Nerve, Brachial Plexus and Microsurgery Unit, Alps Surgery Institute, Annecy, France.
BMJ Open. 2022 Sep 8;12(9):e055346. doi: 10.1136/bmjopen-2021-055346.
Shoulder 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.
This 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.
For 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.
肩关节不稳定是一种常见的损伤，据报道其发病率为每 10 万人中有 23.9 例。目前仍在争论最有效的治疗策略。非手术治疗的复发率高达 60%，而 Bankart 修复和骨块手术等手术治疗的复发率分别为 16%和 2%，但并发症发生率较高（分别为<2%和高达 30%）。已经开发出了确定复发风险的方法；然而，仍然缺乏针对患者的决策工具。人工智能和机器学习算法使用自我学习的复杂模型，可以用来制作针对患者的决策工具。本研究的目的是开发和训练机器学习算法，创建一个预测模型，用于临床实践-作为在线预测工具-来估计 Bankart 修复后复发率。
这是一项多中心回顾性队列研究。将纳入接受关节镜 Bankart 修复术（无填充）治疗的创伤性前肩脱位患者。本研究包括两个部分。第 1 部分，使用多中心数据收集所有可能影响关节镜 Bankart 修复术后复发率的因素，旨在纳入来自全球>1000 名患者的数据。第 2 部分，将使用机器学习算法重新评估（如有必要补充）多中心数据，以预测结果。复发将作为主要结局指标。
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了世界卫生组织关于“在非突发公共卫生事件情况下，世卫组织在会员国收集的数据的使用和共享政策”的规定。研究结果将通过在同行评议期刊上发表论文进行传播。本研究不需要机构审查委员会。
