Novel approach to clinical data analysis: application to kidney transplantation

本研究旨在发展一种新的数学方法来建立抗体不相容性肾移植（AIT）中抗体致病性的模型。目前，肾脏替代是昂贵的，并且具有成本效益的肾脏移植的提供构成了主要的全球卫生优先事项。全世界接受肾脏替代治疗的患者人数超过140万，并且每年以8%的速度增长，超过了一般人群的增长率。迄今为止，预防或逆转肾脏疾病的解决方案未能显著改变全球患者数量的发展。在中期的将来，无法预见同种异体移植的经济上可行的替代方案;因此，肾移植是唯一的解决方案。移植的成功结果取决于供体和受体在组织蛋白HLA上的匹配程度。由于只有大约25%的移植可以完全匹配，许多需要肾脏的人已经产生了针对HLA的抗体，这些抗体可能导致移植排斥。具有预先形成的HLA抗体的患者等待更长时间或不能接受肾脏。AIT已经率先使手术前降低抗体水平和移植“不匹配”的患者成为可能。然而，超过40%的肾脏仍然被排斥。这是因为完全消除抗体是不可能的。有害抗体的类型和必须降低的水平也不清楚。传统的临床研究利用标准的统计分析，需要大量的参与者，并且到目前为止未能预测肾排斥反应。因此，该项目将采用一种替代方法，将统计分析与开发动态模式分析的新方法相结合。将在非线性随机系统方法的框架中对人体对肾移植的免疫反应进行建模，然后通过以下目标将其转化为临床背景：（1）发展适当的方法，并随后分析AIT中抗体进化的动态和静态特性（2）为抗体风险评估提供最具信息量的数据处理工具（3）为基于现有研究数据的综合数据源奠定坚实的基础。该研究旨在解决以下临床问题：（a）什么类型的预先形成的HLA抗体是最有害的，并且与肾排斥的显著风险相关;（B）在手术时预先形成的抗体的临界水平是什么，即可以耐受多少抗体以确保供体肾的安全接受？这种工程和主要是非医疗策略以前没有尝试过。该项目的优势在于其从数学/工程领域到应对医学挑战的转化方面，从而加强了跨学科的生物医学工程接口。这与项目可用的独特临床数据集同时存在。数据的关键方面涉及在移植后关键的前3 - 4周内每天采样的患者，此时抗体水平迅速变化，从而捕获抗体行为中的关键事件。这是世界上唯一一个进行如此高水平抗体监测的项目。从长远来看，通过深入了解AIT的体液免疫反应，该项目的成果将带来显著的临床效益。此外，这将改善移植的风险管理，并为许多无法移植的患者提供移植机会。建立的数据库将巩固这一新兴领域的研究活动，并推动具有国家和国际意义的研究成果。

The research aims to develop a novel mathematical approach to build a model of antibody pathogenicity in antibody incompatible kidney transplantation (AIT). Currently, renal replacement is expensive and cost-effective provision of kidney transplantation constitutes a major global health priority. The number of patients receiving renal replacement therapy exceeds 1.4 million worldwide and is growing by 8 percent annually, in excess of the growth rate of the general population. Solutions for the prevention or reversal of renal disease have so far failed to significantly change the development of global patient numbers. An economically viable alternative to allograft transplantation is not foreseen in the mid-term future; hence renal transplant is the only solution. The successful outcome of a transplantation depends on how well the donor and recipient are matched for tissue proteins called HLA. Since only about 25 percent of transplants can be fully matched many needing a kidney have developed antibodies against HLA and these can cause transplant rejection. Patients with preformed HLA antibodies wait longer or cannot receive a kidney. AIT has been pioneered making it possible to reduce levels of antibody before surgery and transplant 'mismatched' patients. More than 40 percent of kidneys are however still rejected. This is because complete elimination of antibodies is not possible. Types of harmful antibodies and levels to which they must be reduced are also not known. Traditional clinical studies utilise standard statistical analysis that requires very large number of participants and have until now failed to predict kidney rejection. The project will therefore employ an alternative approach that combines statistical analysis with the development of novel methods of dynamic patterns analysis. The human immunological reaction to kidney transplantation will be modelled in the framework of non-linear stochastic systems approaches followed by their translation into clinical context via following objectives: (1) to develop an appropriate methodology and subsequently analyse dynamic and static properties of antibody evolution in AIT (2) to provide the most informative data processing tool for antibody risk assessment (3) to create a rigorous foundation for a comprehensive data source based on the available research data. The research aims to address the following clinical questions: (a) what types of preformed HLA antibodies are most harmful and associated with significant risk of kidney rejection; (b) what are critical levels of preformed antibodies at the time of surgery i.e. how much of the antibodies can be tolerated to ensure safe acceptance of the donor kidney? This engineering and primarily non-medical strategy has not been attempted before. The strength of this project lies in its translational aspect from areas of maths/engineering to tackling medical challenges thus strengthening the cross-disciplinary Biomedical Engineering interface. This is concomitant with the unique clinical data set available for the project. The key aspect of the data concerns the patients being sampled daily in the critical first 3-4 weeks following transplantation when antibody levels change rapidly thus capturing key events in the behaviour of antibodies. This is the only programme anywhere in the world to have carried out with such level of antibody monitoring.For the long term perspective, the project outputs will bring significant clinical benefits through an in-depth understanding of the humoral immune response in AIT. Also, this will improve risk management of transplants and the provision of access to transplantation for many untransplantable patients. The database created will consolidate research activities in this emerging field and advance outputs of the research of national and international significance.