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Multiobjective Design Optimization of Reverse Total Shoulder Arthroplasty

反向全肩关节置换术的多目标设计优化

基本信息

批准号：
9322001
负责人：
Ryan Willing
金额：
$ 7.09万
依托单位：
STATE UNIVERSITY OF NY,BINGHAMTON
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-08-01 至 2019-07-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): Reverse total shoulder arthroplasty (rTSA) restores range of motion and provides pain relief primarily for patients suffering from cuff tear arthropathy. The success of this procedure has prompted increased usage for other shoulder joint conditions, including fracture, rheumatoid arthritis and salvage of previous failed joint replacements. In fact, rTSA is on pace to exceed the number of conventional shoulder replacements performed annually in the U.S. However, the complication rate of rTSA is higher which may limit more widespread use. These complications often relate to stability, range of motion (ROM), muscle efficiency, outcome reliability and durability. Despite several previous experimental and computational studies, the optimal rTSA implant shape has not been determined, and the relationships between these potentially competing performance measures have not been quantified. We hypothesize that competing relationships exist between rTSA performance measures, and that the trade-offs can be quantified using validated computer models and a rigorous and systematic design technique called Multiobjective Optimization (MOO). Our objective is to investigate the complex relationships between ROM, stability, muscle efficiency, outcome reliability, and implant durability through a series of three specific aims, each targeting two or three of these performance metrics. The relationships between competing performance measures will be explained using Pareto curves and surfaces, which will show exactly how much sacrifice is required in one performance measure in order to improve in another. The need for gender-specific rTSA implant designs, and the effects of uncertainty resulting from patient-to-patient and surgical variability will also be considered through the use of statistical shape models. The resulting implant designs will be more robust and, along with the Pareto curves and surfaces describing the relationship between the multiple performance metrics, will permit surgeons to provide more patient-specific care and improved clinical outcomes.