Individualized Immunosuppression for Kidney Transplant Recipients

肾移植受者的个体化免疫抑制

• 批准号: 10664609
• 负责人: Sunjae Bae
• 金额: $ 15.7万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-22 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10664609
• 关键词: Accounting; Acute; Address; American; Antibodies; Benefits and Risks; Biometry; Cardiovascular Diseases; Caring; Cessation of life; Clinical; Cluster randomized trial; Communities; Consensus; Data; Data Set; Decision Analysis; Doctor of Philosophy; Epidemiology; Equilibrium; Foundations; Future; Goals; Grant; Health; Immunosuppression; Individual; Infection; Intervention; Interview; Kidney Transplantation; Knowledge; Longevity; Machine Learning; Malignant Neoplasms; Measures; Medical Education; Mentored Research Scientist Development Award; Mentors; Mentorship; Methodology; Methods; Modeling; Nephrology; Non-Insulin-Dependent Diabetes Mellitus; Operative Surgical Procedures; Outcome; Paired Comparison; Paper; Patient Recruitments; Patients; Population; Positioning Attribute; Protocols documentation; Public Health Schools; Published Comment; Quality-Adjusted Life Years; Regimen; Research; Research Methodology; Research Training; Respondent; Risk; Risk Factors; Risk-Benefit Assessment; Scholarship; Science; Societies; Statistical Computing; Steroids; Subgroup; Surveys; Techniques; Training; Transplant Recipients; Transplant Surgeon; Transplantation; Universities; Variant; Withdrawal; Work; analytical method; career development; clinical practice; clinically relevant; computerized tools; design; disability weights; efficacy outcomes; evidence base; experience; feasibility testing; gradient boosting; graft failure; high risk; implementation science; improved; infection risk; insight; machine learning prediction; medical schools; mortality; multidisciplinary; novel; outcome prediction; participant interview; patient oriented; patient oriented research; personalized predictions; personalized risk prediction; population health; predictive modeling; prevent; professor; response; risk prediction; risk prediction model; safety outcomes; side effect; skills; tool

PROJECT SUMMARY/ABSTRACT Dr. Sunjae Bae is an Assistant Professor of Surgery and Population Health at NYU Grossman School of Medicine. He completed his PhD in epidemiology and Master’s in biostatistics at Johns Hopkins Bloomberg School of Public Health. Prior to his research training, he received his medical education at Kyung Hee University and practiced as a clinician. His previous research has focused on immunosuppression and clinical outcomes after kidney transplantation (KT), with the support from the American Society of Nephrology and Mogam Science Scholarship Foundation. Dr. Bae’s long-term goal is to help >230,000 KT recipients live longer and healthier by creating an evidence- based, patient-centered tool for determining the ideal immunosuppression regimen. Lifelong immunosuppression is a defining feature in the care of KT recipients. Immunosuppression is the primary intervention to prevent acute rejection; however, it causes various side effects, notably 2.1- to 6.2-fold risk of infections, cancers, and cardiovascular diseases, which collectively account for >65% of deaths in this population. Since the risks of acute rejection and immunosuppression-related side effects are unique in each recipient, the selection of the immunosuppression regimen should be individualized according to the recipient’s unique risk profile. The transplant community has long recognized the importance of immunosuppression individualization. However, there is little scientific evidence guiding how it should be done, mainly due to 3 methodological challenges. First, traditional analytic methods are ineffective in providing individualized predictions of the risks and benefits after immunosuppression. Second, a method to objectively assess the risk-benefit balance from patients’ perspectives is lacking. Lastly, the new individualization protocol must be clinically relevant. However, given the lack of consensus on how the individualization should be done, the relevance of a new protocol cannot be assessed objectively. This K01 Mentored Research Scientist Development Award will enable Dr. Bae to expand his research skillset and lay the groundwork for addressing these 3 challenges. We propose the following approaches. First, we will create a machine learning-based individualized risk prediction model that can process statistical interactions efficiently and transparently. Second, we will conduct interviews and paired-comparison surveys to quantify the patients’ viewpoints on the risk-benefit balance, e.g., how much reduction in acute rejection is worth risking a 10% increase in type 2 diabetes. Third, we will survey KT clinicians to characterize their clinical practice and perspectives on individualization. Dr. Bae will enhance his analytical skills and expand his domain knowledge through didactic coursework in patient-oriented research methods and implementation science, and research mentorship by a team of multidisciplinary experts committed to Dr. Bae’s career development.

项目概要/摘要 Sunjae Bae 博士是纽约大学格罗斯曼学院外科和人口健康助理教授 药品。他在约翰霍普金斯大学彭博社完成了流行病学博士学位和生物统计学硕士学位 公共卫生学院。在接受研究培训之前，他在庆熙大学接受了医学教育 大学并担任临床医生。他之前的研究重点是免疫抑制和临床 在美国肾脏病学会的支持下，肾移植 (KT) 后的结果 莫加姆科学奖学金基金会。 Bae 博士的长期目标是通过创建证据来帮助超过 230,000 名 KT 接受者活得更长久、更健康—— 基于、以患者为中心的工具，用于确定理想的免疫抑制方案。终身 免疫抑制是 KT 接受者护理的一个决定性特征。免疫抑制是首要 干预以预防急性排斥反应；然而，它会引起各种副作用，尤其是 2.1 至 6.2 倍的风险 感染、癌症和心血管疾病，这些疾病合计占该地区死亡人数的 65% 以上 人口。由于急性排斥反应和免疫抑制相关副作用的风险在每种情况下都是独特的 对于受者来说，免疫抑制方案的选择应根据受者的个体情况进行个体化选择。 独特的风险状况。 移植界早已认识到免疫抑制个体化的重要性。 然而，几乎没有科学证据来指导如何做到这一点，主要是由于 3 种方法论 挑战。首先，传统的分析方法无法有效提供个性化的风险预测 以及免疫抑制后的益处。二是客观评估风险收益平衡的方法 缺乏患者的观点。最后，新的个体化方案必须具有临床相关性。然而， 鉴于对如何进行个体化缺乏共识，新协议的相关性 无法客观评价。 K01 指导研究科学家发展奖将使 Bae 博士能够扩展他的研究技能 并为解决这三个挑战奠定基础。我们提出以下方法。首先，我们将 创建基于机器学习的个性化风险预测模型，可以处理统计交互 高效、透明。其次，我们将进行访谈和配对比较调查来量化 患者对风险收益平衡的看法，例如，急性排斥反应减少多少值得冒风险 2 型糖尿病增加 10%。第三，我们将对 KT 临床医生进行调查，以描述他们的临床实践和 对个性化的看法。 Bae 博士将提高他的分析能力并扩展他的领域知识 通过以患者为中心的研究方法和实施科学以及研究方面的教学课程 由致力于裴博士职业发展的多学科专家团队提供指导。