Predicting post-kidney transplant dementia/Alzheimer's Disease risk in older patients

项目总结/摘要 对于患有ESRD的老年人（≥ 50），肾移植（KT）正在增加。2021年，老年人获得 大约60%的KTs，患痴呆/阿尔茨海默病（AD）的风险增加。KT接受者， 移植后发生痴呆/AD的患者死亡风险增加2.4倍， 移植物丢失。在被诊断患有痴呆/AD的老年KT接受者中，88.6%在10年内死亡。这些 死亡可能是由于无法进行自我护理、营养不足或不坚持服药。 尽管有这些风险，预测谁会发展KT后痴呆/AD并不是KT前评估的一部分。 此外，KT前评价时常规测量的因素（年龄、性别、合并症等）只有 对KT后痴呆/AD的预测能力中等。预测KT后痴呆/AD风险可以帮助识别 年龄较大的候选人将受益于干预措施，如认知障碍或KT后监测。 在移植评估时预测KT后痴呆/AD风险提供了足够的时间在KT之前进行干预。 为了设计一个可以利用机器学习预测KT后痴呆/AD风险的老年人特定模型，我们 将利用一项正在进行的NIA资助的R01老年KT中虚弱的前瞻性纵向队列研究 候选人完成以下目标：（1）识别痴呆/AD病例和可能的亚型， KT接受者，并量化该正在进行的队列中KT接受者中AD/痴呆的累积发病率 研究;（2）确定与KT后相关的临床、老年和ESRD特异性风险因素 痴呆/AD;（3）在机器学习的帮助下设计一个模型，成功预测痴呆/AD的风险。 接受KT评价的老年患者中的KT后痴呆/AD。我们的团队在脆弱和 痴呆/AD和进入正在进行的肾脏疾病虚弱评估（FAIR）队列，沿着博士。 Long在机器学习和回归方面的培训兴趣，为建立预测提供了独特的机会 这些模型可以识别出KT后痴呆/AD风险最高的老年候选人。 我们假设一个风险预测工具，结合传统的临床，老年，和ESRD的具体风险， KT评估时通常测量的因素将改善KT后痴呆/AD风险预测。如果 我们将提高识别老年患者的能力， KT后痴呆/AD，他们需要额外的干预措施来改善KT后的结果。

PROJECT SUMMARY/ABSTRACT Kidney transplantation (KT) is increasing for older adults (≥50) with ESRD. In 2021, older adults received roughly 60% of all KTs and are at increased risk of dementia/Alzheimer’s disease (AD). KT recipients who develop dementia/AD post-transplant have a 2.4-fold increased risk of mortality and a 1.5-fold increased risk of graft loss. Of older KT recipients who are diagnosed with dementia/AD, 88.6% die within 10 years. These deaths may be due to inability to perform self-care, inadequate nutrition, or medication non-adherence. Despite these risks, predicting who will develop post KT dementia/AD is not part of pre-KT evaluation. Furthermore, factors routinely measured at pre-KT evaluation (age, sex, comorbidities, etc.) have only moderate predictive power for post-KT dementia/AD. Predicting post-KT dementia/AD risk can help identify older candidates who would benefit from interventions such as cognitive prehabilitation or post-KT surveillance. Predicting post-KT dementia/AD risk at transplant evaluation provides enough time to intervene prior to KT. To design a geriatric-specific model that can predict post-KT dementia/AD risk utilizing machine learning, we will leverage an ongoing NIA-funded R01 prospective longitudinal cohort study of frailty among older KT candidates to accomplish the following aims: (1) To identify dementia/AD cases and possible subtypes among KT recipients and quantify the cumulative incidence of AD/dementia in KT recipients in this ongoing cohort study; (2) To identify clinical, geriatric, and ESRD-specific risk factors that are associated with post-KT dementia/AD; and (3) To design a model with the aid of machine learning that successfully predicts the risk of post-KT dementia/AD in older patients undergoing KT evaluation. Our group’s expertise in frailty and dementia/AD and access to the ongoing Frailty Assessment in Renal Disease (FAIR) cohort, along with Dr. Long’s training interests in machine learning and regression, provide a unique opportunity to build prediction models that could identify older candidates at highest risk of post-KT dementia/AD. We hypothesize that a risk prediction tool that incorporates traditional clinical, geriatric, and ESRD-specific risk factors that are commonly measured at KT evaluation, will improve post-KT dementia/AD risk prediction. If the proposed aims are achieved, we will improve our ability to identify older patients at increased risk of developing post-KT dementia/AD, who will need additional interventions to improve post-KT outcomes.