Clinical and Biological Factors Predicting Lung Transplant Textbook Outcomes (U01)

预测肺移植教科书结果的临床和生物学因素（U01）

• 批准号: 10677558
• 负责人: Matthew Galen Hartwig
• 金额: $ 41.11万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-05 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10677558
• 关键词: Abdomen; Acute; Address; Adopted; Adult; Age; Aging; Biological; Biological Aging; Biological Assay; Biological Factors; Cells; Characteristics; Clinical; Collaborations; Complication; Country; Data; Data Coordinating Center; Discipline; Economic Burden; Enrollment; Failure; Freedom; Functional disorder; Funding Opportunities; Future; Geography; Goals; Graft Survival; Health system; Hospital Costs; Immune; Immunity; Individual; Infection; Intervention; Kidney Failure; Lung; Lung Transplantation; Lung diseases; Machine Learning; Measurement; Measures; Minnesota; Modeling; Operative Surgical Procedures; Organ; Orthopedic Surgery; Outcome; Output; Patient-Focused Outcomes; Patients; Pennsylvania; Peptides; Performance; Perioperative; Perioperative complication; Physiological; Positioning Attribute; Postoperative Complications; Postoperative Period; Predictive Factor; Procedures; Process; Prospective cohort; Protocols documentation; Provider; Publications; Publishing; Registries; Research; Research Project Grants; Resources; Risk; Risk Assessment; Risk Factors; Site; Surrogate Endpoint; Textbooks; Time; Transplant Recipients; Transplantation; Transplantation and Immune System; Universities; Viral; body system; clinical center; cost; cytokine; graft dysfunction; graft failure; improved; improved outcome; lung allograft; machine learning prediction; modifiable risk; novel; outcome prediction; participant enrollment; predictive modeling; predictive tools; recruit; response; risk prediction model; surgery outcome; transplant centers

PROJECT SUMMARY/ABSTRACT While lung transplantation is the only treatment option for end-stage lung disease, early post-operative complications are common and limit long-term patient survival while concomitantly increasing the economic burden of an already expensive therapy. Successful surgical outcomes can be defined by an ideal or “textbook outcome” (TO) where the patient does not have significant early post-operative complications, which for lung transplant can include primary graft dysfunction, acute lung allograft dysfunction, renal failure or infection. Our group recently published the first lung transplant TO definition based on single-center data and found failure to achieve TO was strongly associated with worse patient survival and significantly higher cost to the health system. A subsequent registry analysis of 62 US lung transplant centers found the rate of achieving TO ranged from 27% to 72%, emphasizing wide variability in outcomes and potential for intervention and improvement. The Lung Transplant Clinical Center in this proposal include Duke University, University of Louisville, University of Minnesota and University of Pennsylvania. These centers are four of the oldest and most respected lung transplant centers in the country with geographic and size diversity, including small, medium, and large volumes. Our proposal aims to understand the critical pretransplant clinical characteristics, as well as novel underlying biologic aging differences, that contribute to worse early outcomes, or failure to achieve a TO. We hypothesize that specific clinical variables and biological aging measures can predict early complications. Biological age in the pretransplant patient may be driven by organ specific advanced lung disease and therefore ameliorated with lung transplant. Alternatively, biological age may be a systemic process across organs systems and does not resolve with transplant. To determine the significance of organ specific versus systemic aging, we will evaluate pretransplant biological aging in the recipient's pretransplant immune system and explanted lung. Using iterative machine learning we will develop and validate a TO prediction model based on the identified clinical variables and biological measurements to determine a personalized perioperative risk of lung transplantation for individual candidates. More than just a predictive tool, this proposal will allow for identification of potentially modifiable clinical and biologic variables that can be leveraged to improve outcomes. As part of the larger Lung Transplant Consortium, we will enroll participants and contribute data and biospecimens through a common research protocol under the auspices of the Lung Transplant Consortium Data Coordinating Center and Steering Committee.

项目概要/摘要 虽然肺移植是终末期肺病的唯一治疗选择，但术后早期 并发症很常见，限制了患者的长期生存，同时也增加了经济负担 已经很昂贵的治疗的负担。成功的手术结果可以通过理想或“教科书”来定义 结果”（TO），其中患者没有明显的早期术后并发症，这对于肺 移植可包括原发性移植物功能障碍、急性肺同种异体移植物功能障碍、肾衰竭或感染。我们组 近期发表首个基于单中心数据的肺移植TO定义，发现未能实现TO 与患者生存率较差和卫生系统成本显着升高密切相关。随后的 对美国 62 个肺移植中心的注册分析发现，达到 TO 的比率在 27% 到 72% 之间， 强调结果的巨大差异以及干预和改进的潜力。肺移植 该提案中的临床中心包括杜克大学、路易斯维尔大学、明尼苏达大学和 宾夕法尼亚大学。这些中心是美国历史最悠久、最受尊敬的四个肺移植中心 该国家的地理和规模具有多样性，包括小、中和大容量。我们的提案目标 了解关键的移植前临床特征以及新的潜在生物衰老差异， 这会导致更糟糕的早期结果，或无法实现目标。我们假设特定的临床变量和 生物衰老测量可以预测早期并发症。移植前患者的生物学年龄可能受到驱动 由器官特异性晚期肺部疾病引起，因此可通过肺移​​植得到改善。或者，生物年龄 可能是跨器官系统的系统性过程，并且不能通过移植来解决。确定重要性 器官特异性老化与全身老化的比较，我们将评估受者移植前的移植前生物老化 免疫系统和移植肺。使用迭代机器学习，我们将开发并验证 TO 预测 基于确定的临床变量和生物测量的模型来确定个性化的 个体候选人肺移植的围手术期风险。该提案不仅仅是一个预测工具， 允许识别潜在可修改的临床和生物学变量，这些变量可用于改善 结果。作为更大的肺移植联盟的一部分，我们将招募参与者并贡献数据和 在肺移植联盟数据的支持下通过共同研究方案获取生物样本 协调中心和指导委员会。