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.

项目摘要/摘要 虽然肺移植是末期肺部疾病的唯一治疗选择，但术后早期术后 并发症很常见，并且限制了长期的患者生存，同时又增加了经济 一种已经昂贵的疗法的负担。成功的手术结果可以通过理想或“教科书”来定义 结果”（到）患者没有明显的早期术后并发症，肺部并发症 移植可以包括一级移植功能障碍，急性肺同种异体功能障碍，肾衰竭或感染。我们的小组 最近发布了基于单中心数据的定义的第一次肺移植，发现未能实现 与较差的患者生存率密切相关，卫生系统的成本明显更高。随后的序列 对62个美国肺移植中心的注册分析发现，达到27％至72％的登记率。 强调结果的广泛变异以及干预和改进的潜力。肺移植 该提议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。这些中心是最古老，最受人尊敬的肺移植中心 该国具有地理和大小多样性的国家，包括小型，中和大量。我们的建议目标 了解关键的移植前临床特征以及新的潜在生物衰老差异， 这有助于更糟的早期结果或未能取得成就。我们假设该特定的临床变量和 生物老化措施可以预测早期并发症。可以驱动前移植患者的生物年龄 通过器官特异性晚期肺部疾病，因此用肺移植改善。或者，生物年龄 可能是跨器官系统的系统过程，并且无法通过移植解决。确定意义 特定器官与全身性衰老的衰老，我们将评估接受者预科植物的移植前生物学老化 免疫系统和外植的肺。使用迭代机器学习，我们将开发并验证A对预测 基于确定的临床变量和生物学测量的模型，以确定个性化 单个候选人的肺部肺部围手术期风险。该建议不仅是一种预测工具，还将 允许识别可以利用的可能修改的临床和生物学变量以改进 结果。作为较大的肺移植财团的一部分，我们将招募参与者并贡献数据，并 在肺移植财团数据的主持下，通过共同研究方案生物测量 协调中心和指导委员会。