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

预测老年患者肾移植后痴呆/阿尔茨海默氏病的风险

• 批准号: 10751734
• 负责人: Jane J Long
• 金额: $ 8.78万
• 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: 2023-08-02 至 2026-08-01
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10751734
• 关键词: Age; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease risk; Calibration; Cessation of life; Classification; Clinical; Cognitive; Cohort Studies; Communities; Consensus; Cox Proportional Hazards Models; Data; Dementia; Development; Diagnosis; Dialysis procedure; Discrimination; Doctor of Philosophy; Elderly; End stage renal failure; Epidemiologic Methods; Ethnic Origin; Evaluation; Event; Faculty; Funding; Goals; Habilitation; Hybrids; Impaired cognition; Incidence; Instruction; Intervention; Kidney Diseases; Kidney Transplantation; Learning; Longitudinal cohort study; Machine Learning; Measures; Medical History; Mentorship; Methods; Modeling; Outcome; Outcomes Research; Patients; Pharmaceutical Preparations; Race; Recording of previous events; Research; Risk; Risk Factors; Scientist; Self Care; Subgroup; Surgeon; Techniques; Testing; Time; Training; Transplant Recipients; Transplantation; United States National Institutes of Health; Validation; Vascular Dementia; cognitive function; cohort; comorbidity; dementia risk; depressive symptoms; design; experience; follow-up; frailty; functional disability; high risk; improved; interest; medication nonadherence; mixed dementia; mortality risk; neurocognitive test; novel; nutrition; older patient; post-transplant; post-transplant disease; predictive modeling; predictive tools; prospective; recruit; risk prediction; screening; sex; skills; tool; transplant centers

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.

项目概要/摘要 对于患有 ESRD 的老年人（≥50 岁）来说，肾移植（KT）正在增加。 2021 年，老年人获得 大约 60% 的 KT 患有痴呆/阿尔茨海默病 (AD) 的风险增加。 KT 接收者 移植后发生痴呆/AD 的死亡风险增加 2.4 倍，罹患痴呆症的风险增加 1.5 倍 移植物损失。在被诊断患有痴呆症/AD 的老年 KT 接受者中，88.6% 会在 10 年内死亡。这些 死亡可能是由于无法自我护理、营养不足或不遵守药物治疗造成的。 尽管存在这些风险，预测谁会患上 KT 后痴呆/AD 并不是 KT 前评估的一部分。 此外，KT 前评估中常规测量的因素（年龄、性别、合并症等）仅影响 对 KT 后痴呆/AD 的中等预测能力。预测 KT 后痴呆/AD 风险有助于识别 老年候选人将受益于认知预康复或 KT 后监测等干预措施。 在移植评估中预测 KT 后痴呆/AD 风险为 KT 之前的干预提供了足够的时间。 为了设计一个可以利用机器学习预测 KT 后痴呆/AD 风险的老年人特异性模型，我们 将利用正在进行的 NIA 资助的 R01 前瞻性纵向队列研究，研究老年 KT 的虚弱状况 候选人实现以下目标：（1）识别痴呆/AD病例和可能的亚型 KT 接受者并量化该持续队列中 KT 接受者 AD/痴呆的累积发病率 学习; (2) 确定与 KT 后相关的临床、老年和 ESRD 特异性危险因素 痴呆症/AD； (3) 借助机器学习设计一个模型，成功预测风险 接受 KT 评估的老年患者出现 KT 后痴呆/AD。我们团队在衰弱和治疗方面的专业知识 痴呆症/AD 以及参与正在进行的肾病虚弱评估 (FAIR) 队列，以及 Dr. Long 对机器学习和回归的培训兴趣为构建预测提供了独特的机会 可以识别 KT 后痴呆/AD 风险最高的老年候选人的模型。 我们假设风险预测工具结合了传统的临床、老年和 ESRD 特定风险 KT 评估中通常测量的因素将改善 KT 后痴呆/AD 风险预测。如果 如果实现了拟议的目标，我们将提高识别老年患者患病风险增加的能力 KT 后痴呆/AD，需要额外的干预措施来改善 KT 后的结果。