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.

项目总结/摘要 博士Sunjae Bae是纽约大学格罗斯曼医学院外科和人口健康助理教授。 药他在约翰霍普金斯布隆伯格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Kyung Hee接受了医学教育 大学毕业后成为临床医生。他以前的研究主要集中在免疫抑制和临床 在美国肾脏病学会的支持下， 莫加姆科学奖学金基金会。 博士BAE的长期目标是通过创建证据，帮助> 230，000 KT接受者活得更长，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接受者护理中的一个明确特征。免疫抑制是主要的 预防急性排斥反应的干预;然而，它会引起各种副作用，特别是2.1 - 6.2倍的风险， 感染，癌症和心血管疾病，这些疾病共同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一种情况下都是独特的， 对于接受者，免疫抑制方案的选择应根据接受者的 独特的风险特征。 移植界早就认识到免疫抑制个体化的重要性。 然而，很少有科学证据指导应该如何做，主要是由于3种方法 挑战首先，传统的分析方法在提供个性化的风险预测方面是无效的 和免疫抑制后的益处。第二，客观评估风险-收益平衡的方法， 缺乏患者的观点。最后，新的个体化方案必须具有临床相关性。然而，在这方面， 鉴于在如何进行个体化方面缺乏共识， 不能客观评价。 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 并为应对这三大挑战奠定基础。我们建议采取以下办法。一是 创建一个基于机器学习的个性化风险预测模型，可以处理统计交互 高效和透明。第二，我们会进行访问和配对比较调查， 患者对风险-获益平衡的观点，例如，减少多少急性排斥反应值得冒险 2型糖尿病发病率增加10%。第三，我们将调查KT临床医生，以描述他们的临床实践， 个性化的观点。裴博士将提高他的分析能力，并扩大他的领域知识 通过以患者为导向的研究方法和实施科学的教学课程， 由致力于Bae博士职业发展的多学科专家团队提供指导。