Personalized Rejection Risk Assessment in Cardiac Transplantation

心脏移植中的个性化排斥风险评估

• 批准号: 10284138
• 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
• 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/10284138
• 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; Data Store; Diagnosis; Diagnostic; Diagnostic Procedure; Disease; Donor person; Electronic Health Record; Event; Exposure to; Funding; Future; Heart; Heart Transplantation; Heart-Lung Transplantation; Hematoxylin and Eosin Staining Method; Histologic; Histology; Image Analysis; Immune; Immunodiagnostics; Immunofluorescence Immunologic; Immunologics; Immunology; Immunosuppression; In Situ; Individual; Inflammatory Infiltrate; Informatics; Injury; International; Investigation; Knowledge; Laboratories; Legal patent; Longevity; Machine Learning; Mentors; Modeling; Molecular; Morphology; Myocardial; Nature; Opportunistic Infections; Organ Transplantation; Pathology; Patients; Performance; Pharmaceutical Preparations; Pharmacology; 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; System; Therapeutic immunosuppression; Time; Tissue Sample; Tissues; Translational Research; Transplant Recipients; Transplantation; Weaning; Work; advanced analytics; allograft rejection; analysis pipeline; analytical method; analytical tool; archive data; archived data; base; career development; cohort; data modeling; data resource; data streams; design; 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; post-transplant; predictive modeling; premature; prevent; prognostic; prospective; quantitative imaging; risk prediction; risk prediction model; risk stratification; standard of care; surveillance strategy; tissue archive; tool; translational impact; transplant centers; transplantation medicine; treatment as usual

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.

项目总结：心脏同种异体移植排斥反应（CAR）是移植医学中的一个严重问题