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.

项目摘要/摘要 本文作者是纽约大学格罗斯曼管理学院外科与人口健康助理教授 医学。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。在接受研究训练之前，他曾在京熙大学接受医学教育 大学毕业，并以临床医生的身份执业。他之前的研究主要集中在免疫抑制和临床 在美国肾病学会的支持下，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造一个证据，帮助23万名KT；受者活得更长、更健康- 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受者护理的一个重要特征。免疫抑制是主要的 干预以预防急性排斥反应；然而，它会引起各种副作用，特别是2.1至6.2倍的风险 感染、癌症和心血管疾病，这些疾病合计占本地区死亡人数的65% 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不同的 受者，免疫抑制方案的选择应根据受者的情况进行个体化 独特的风险配置文件。 移植团体很早就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应该如何做到这一点，主要是由于3种方法论 挑战。首先，传统的分析方法在提供对风险的个性化预测方面效率低下 和免疫抑制后的益处。第二，客观评估风险-收益平衡的方法 缺乏患者的视角。最后，新的个体化方案必须具有临床相关性。然而， 鉴于在如何实现个别化方面缺乏共识，新议定书的相关性 不能客观地评估。 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集 并为应对这三大挑战奠定基础。我们提出了以下方法。首先，我们将 创建可处理统计交互的基于机器学习的个性化风险预测模型 高效和透明。其次，我们将进行访谈和配对比较调查，以量化 患者对风险-收益平衡的看法，例如，急性排斥反应的减少多少值得冒险 2型糖尿病增加10%。第三，我们将调查KT临床医生，以确定他们的临床实践和 关于个性化的观点。Bae博士将增强他的分析技能，扩大他的领域知识 通过讲授课程讲授以病人为中心的研究方法和实施科学，并进行研究 由致力于Bae博士职业发展的多学科专家团队提供指导。