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Prediction of Recurrent Anterior Shoulder Instability Using the On/Off Track Method and 3D MRI: A Clinical Outcomes and Cost-Effectiveness Study

Prediction of Recurrent Anterior Shoulder Instability Using the On/Off Track Method and 3D MRI: A Clinical Outcomes and Cost-Effectiveness Study
使用 On/Off Track 方法和 3D MRI 预测复发性肩部前不稳定:临床结果和成本效益研究
批准号:
10396027
负责人:
Soterios Gyftopoulos
金额:
$39.83万
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-07-01 至 2024-04-30

项目摘要

项目成果

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中文摘要
翻译
项目摘要 肩关节前不稳定(ASI)是美国最常见的肩关节疾病之一。1 一部分ASI患者接受手术稳定,通常是关节镜下软组织修复(即, 关节唇和/或关节囊),每年约有260,000例此类手术, 美国。2,3虽然这些病例中的大多数是成功的,但高达35%被认为是临床失败 由于复发性ASI和/或持续性肩关节功能受限。4,5 ASI患者的关节镜修复是对发生在这种情况下的骨损伤的评估和治疗不足, 临床环境、肱骨Hill Sachs病变和前关节盂骨丢失(即双极骨丢失),报告至 是高达67%的患者术后复发性ASI的原因。6 MRI在 术前评估ASI患者,提供相关软组织的重要信息, 骨损伤。先前的研究已经定义了基于影像学的肱骨“显著”骨丢失的阈值 用于指导治疗选择的最大骨损伤位置通常 决定管理。然而,这些阈值的实用性和仅考虑一个骨 几项研究表明,治疗结果和患者 使用这些技术的结果。最近,人们更加重视潜在的相互作用, 这些损伤之间的关系,而不是他们的大小作为复发性ASI的主要危险因素。提高 术前评估双极骨丢失,我们将开发和优化一种新的成像策略, 成像技术,3D MRI和成像测量工具,On/Off Track方法, 应用于临床实践。使用标准成像数据创建3D MRI重建, 最小的后处理时间,而没有与其他3D模型相关的成本和辐射。开/关 追踪法是一种使用双极骨丢失的简单测量来评估 这些骨损伤之间的相互作用,以预测复发性ASI。我们将检查 使用3D MRI模型的MRI上的On/Off Track方法预测哪些有ASI病史的患者将接受 在手术后24个月内被视为失败。我们还将确定最具成本效益的术前 有ASI病史的患者的影像学策略。这项研究将建立一个新的具有成本效益的MRI策略 用于评估ASI患者的双极骨丢失,可用于选择最 适当的初始治疗,改善患者的预后。
英文摘要
PROJECT SUMMARY Anterior shoulder instability (ASI) is one of the most common shoulder ailments in the United States.1 A large proportion of ASI patients undergo surgical stabilization, typically arthroscopic soft tissue repair (i.e. repair of the labrum and/or joint capsule), with approximately 260,000 surgeries of this type performed each year in the United States.2,3 While the majority of these cases are successful, up to 35% are considered clinical failures due to recurrent ASI and/or persistent limited shoulder function.4,5 One of the major reasons for failure of arthroscopic repair in ASI patients is the inadequate evaluation and treatment of bone injuries that occur in this clinical setting, humeral Hill Sachs lesions and anterior glenoid bone loss, (i.e. bipolar bone loss), reported to be the cause of recurrent ASI after surgery in up to 67% of patients.6 MRI plays a crucial role in the preoperative evaluation of patients with ASI, providing important information on the associated soft tissue and bone injuries. Prior studies have defined imaging based thresholds of “significant” bone loss at the humerus and glenoid that are used to guide the selection of treatment with the location of greatest bone injury typically determining management. However, the utility of these thresholds and the consideration of only one bone injury have come into question with several studies demonstrating conflicting treatment results and patient outcomes using these techniques. More recently, greater emphasis has been put on the potential interaction between these injuries rather than their sizes as the main risk factor for recurrent ASI. To improve the preoperative evaluation of bipolar bone loss, we will develop and optimize a new imaging strategy that consists of an imaging technique, 3D MRI, and imaging measurement tool, the On/Off Track method, that can be easily implemented into clinical practice. 3D MRI reconstructions are created using standard imaging data and minimal post processing time without the cost and radiation associated with other 3D models. The On/Off Track method is a technique that uses simple measurements of bipolar bone loss to assess the potential for interaction between these osseous injuries in order to predict recurrent ASI. We will examine the value of the On/Off Track method on MRI with 3D MRI models in predicting which patients with a history of ASI will be considered a failure within 24 months of surgery. We will also determine the most cost-effective pre-operative imaging strategy for patients with a history of ASI. This study will establish a new cost-effective MRI strategy for the assessment of bipolar bone loss in the patient with ASI, which can be used to select the most appropriate initial treatment and improve patient outcomes.
期刊论文(1)
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科研奖励(0)
会议论文
Three-dimensional MRI Bone Models of the Glenohumeral Joint Using Deep Learning: Evaluation of Normal Anatomy and Glenoid Bone Loss.
使用深度学习的盂肱关节三维 MRI 骨模型:正常解剖结构和关节盂骨丢失的评估。
DOI: 10.1148/ryai.2020190116
发表时间: 2020
期刊: Radiology. Artificial intelligence
影响因子: --
作者: [CantarelliRodrigues,Tatiane, Deniz,CemM, Alaia,ErinF, Gorelik,Natalia, Babb,JamesS, Dublin,Jared, Gyftopoulos,Soterios]
通讯作者: Gyftopoulos,Soterios
Prediction of Recurrent Anterior Shoulder Instability Using the On/Off Track Method and 3D MRI: A Clinical Outcomes and Cost-Effectiveness Study
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