Personalized Rejection Risk Assessment in Cardiac Transplantation

心脏移植中的个性化排斥风险评估

基本信息

批准号：
10687099
负责人：
Eliot Peyster
金额：
$ 16.69万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-10 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10687099
关键词：
Acute Algorithms Allografting Archives Area Assessment tool Biopsy Caring Clinical Clinical Data Clinical Informatics Collection Complex Computer Analysis Computer Vision Systems Computing Methodologies Data Diagnosis Diagnostic Diagnostic Procedure Disease Donor person Electronic Health Record Event Exclusion Exposure to Funding Future Heart Heart Transplantation Hematoxylin and Eosin Staining Method Histologic Histology Image Analysis Immune Immunodiagnostics Immunofluorescence Immunologic Immunologics Immunosuppression In Situ Individual Inflammatory Infiltrate Informatics Injury Institution International Investigation Knowledge Laboratories Legal patent Longevity Lung Transplantation Machine Learning Mentors Modeling Molecular Morphology Myocardial Nature Opportunistic Infections Organ Transplantation Pathology Patients Performance Pharmaceutical Preparations Predictive Value Prevention Prevention Protocols Prevention strategy Procedures Process Protocols documentation Publishing Research Research Personnel Resources Retrospective cohort Risk Risk Assessment Schedule Slide Societies Solid Spatial Distribution Stains Standardization Therapeutic immunosuppression Time Tissue Sample Tissues Translational Research Transplant Recipients Transplantation Transplantation Immunology Weaning Work advanced analytics allograft rejection analysis pipeline analytical method analytical tool archived data career development cohort data integration data modeling data resource data streams design electronic health record system experience feature extraction heart allograft high risk histological specimens improved innovation machine learning algorithm machine learning method meetings molecular modeling multimodality myocardial injury novel personalized diagnostics pharmacologic post-transplant predictive modeling predictive tools premature prevent prognostic prospective quantitative imaging risk prediction risk prediction model risk stratification standard of care surveillance strategy tissue archive translational impact translational potential transplant centers transplantation medicine treatment as usual

项目摘要

Project Summary: Cardiac allograft rejection (CAR) is a serious concern in transplant medicine, representing the leading threat to short- and long-term allograft survival. As a result, CAR surveillance and prevention is a primary focus of post-transplant care, with recipients undergoing frequent, scheduled, surveillance endomyocar- dial biopsy (EMB) for histologic CAR grading along with frequent, scheduled de-escalation of immunosuppres- sion (IS). The uniformity of this standardized approach to CAR mitigation is the result of an inability to employ reliable, proactive, and tailored strategies based on individual CAR risk. Consequently, patients at low CAR risk are exposed to unnecessary EMB procedures and excess IS therapy, while patients at high risk experience inadequate CAR surveillance and early/inappropriate weaning of IS. This exposes patients to potential harm, and highlights the clear, unmet need for precision CAR risk-assessment tools. The overarching premise for this proposal is that contained within the clinical data and EMB tissues already collected as part of usual care at transplant centers exists the means to provide actionable CAR risk assessments. Extensive immunologic, diag- nostic, and pharmacologic data are captured in electronic health records (EHR) at transplant centers, while large collections of EMB histology samples are stored (and often digitized) in pathology archives. This proposal seeks to utilize advanced machine-learning algorithms and in-situ diagnostic methods to deeply mine these archival resources for the purpose of validating novel CAR risk-prediction models. In Aim 1, we will leverage our experi- ence with automated histologic analysis pipelines to develop a ‘morphologic model’ for predicting future CAR using archived H&E slides. Hematoxylin-and-Eosin (H&E) histology slides are generated from all EMB events as part of standard-of-care. In published and patented prior efforts, we have extracted quantitative morphologic features from digitized H&E slides which, when modeled, demonstrate excellent performance for diagnosing myocardial injury and CAR grades. In Aim 2, we will move beyond standard H&E, leveraging our experience with quantitative, in-situ immune-profiling of transplant EMBs to develop a ‘morpho-molecular’ model for predicting future CAR. This aim will expand upon exciting pilot work which showed the CAR risk-stratification potential of combining quantitative image-analysis with multiplex immunofluorescence. Finally, in Aim 3, we will develop a ‘histo-informatics’ model for predicting CAR by integrating data from Aims 1 & 2 with comprehensive clinical informatics data extracted from the EHR. Ultimately, as a result of this work, we expect to validate a novel pre- cision prediction model for use in prospective investigations exploring personalized CAR surveillance and pre- vention strategies. Beyond the potential translational impact, this research plan will build on the Applicant’s knowledge of complex cohort design, integrated data modeling, and transplant immunodiagnostics. Along with planned coursework and a diverse mentoring, advisory, and collaborative team, this proposal provides the opti- mal vehicle for Dr. Peyster’s maturation into an investigator with proven expertise in multi-modality diagnostics.

项目摘要：心脏同种异体移植排斥（CAR）是移植医学中的一个严重问题，代表因此，CAR 监测和预防是对短期和长期同种异体移植物存活的主要威胁。移植后护理的主要焦点是受者接受频繁、定期的监测拨盘活检（EMB）用于组织学 CAR 分级以及频繁、定期的免疫抑制剂降级这种标准化的 CAR 缓解方法的统一性是由于无法采用的结果。根据测试的个体 CAR 风险、低 CAR 风险患者制定可靠、主动且量身定制的策略。接受不必要的 EMB 手术和过量的 IS 治疗，而高危患者则经历 CAR 监测不足和 IS 过早/不适当的撤机会使患者面临潜在的伤害，并强调了对精确 CAR 风险评估工具的明确但尚未满足的需求。建议包含在临床数据和 EMB 组织中，这些数据和 EMB 组织已作为常规护理的一部分收集到移植中心有能力提供可操作的 CAR 风险评估。移植中心的电子健康记录 (EHR) 中捕获了诺斯蒂克和药理学数据，而大量该提案寻求将 EMB 组织学样本的集合存储（并且通常数字化）。利用先进的机器学习算法和现场诊断方法来深度挖掘这些档案为了验证新颖的 CAR 风险预测模型，我们将利用我们的经验。借助自动化组织学分析流程，开发用于预测未来 CAR 的“形态学模型” 使用存档的 H&E 幻灯片从所有 EMB 事件生成。作为护理标准的一部分，在先前已发表并获得专利的工作中，我们提取了定量形态学数据。数字化 H&E 幻灯片的特征，在建模时表现出出色的诊断性能在目标 2 中，我们将利用我们的经验超越标准 H&E。对移植 EMB 进行原位免疫分析，开发用于定量预测的“形态分子”模型这一目标将扩大到令人兴奋的试点工作，该工作显示了 CAR 风险分层的潜力。最后，在目标 3 中，我们将开发一种将定量图像分析与多重免疫荧光相结合的方法。 “组织信息学”模型，通过将目标 1 和 2 的数据与综合临床相结合来预测 CAR 最终，作为这项工作的结果，我们希望验证一种新颖的预预测方法。用于探索个性化 CAR 监测和预预测的前瞻性研究的决策预测模型除了潜在的转化影响之外，该研究计划还将建立在申请人的基础上。复杂队列设计、集成数据建模和移植免疫诊断的知识。计划的课程作业和多元化的指导、咨询和协作团队，该提案提供了最佳选择 Peyster 博士成长为一名在多模态诊断方面拥有成熟专业知识的研究者的最佳途径。