Dynamic Prediction of Renal Failure Using Longitudinal Prognostic Information among Patients with Chronic Kidney Disease and Kidney Transplant

利用慢性肾病和肾移植患者的纵向预后信息动态预测肾衰竭

• 批准号: 9912766
• 负责人: Brad C Astor
• 金额: $ 31.65万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-10 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9912766
• 关键词: Accounting; Adverse event; Allografting; Area; Biological Markers; Calibration; Caring; Characteristics; Chronic; Chronic Disease; Chronic Kidney Failure; Chronic Kidney Insufficiency; Clinic Visits; Clinical; Cohort Studies; Computer software; Counseling; Data; Data Set; Databases; Derivation procedure; Disease Progression; Disease model; Electronic Health Record; Equation; Event; Failure; Future; Guidelines; Intervention; Investigation; Kidney; Kidney Diseases; Kidney Failure; Kidney Transplantation; Lead; Literature; Longitudinal cohort study; Measures; Methodology; Methods; Modeling; Monitor; Nephrology; Online Systems; Patient Care; Patients; Physicians; Population; Populations at Risk; Psyche structure; Publications; Publishing; Recording of previous events; Recurrence; Renal Replacement Therapy; Research; Research Personnel; Resources; Risk; Risk Factors; Statistical Algorithm; Testing; Time; Transplant Recipients; Update; Validation; Veterans; Visit; Wisconsin; Work; base; clinical biomarkers; clinical care; clinical development; clinical practice; cost effective; disorder risk; follow-up; graft failure; health administration; kidney allograft; medical specialties; model design; mortality risk; novel; patient population; personalized decision; personalized predictions; predictive modeling; prognostic; risk prediction model; time use; tool; transplant registry; web-based tool

Project Summary/Abstract Patients with chronic kidney disease (CKD) and patients receiving kidney transplantation (KTx) are at risk of kidney/graft failure. Accurate estimation of the time of these adverse clinical events is of great importance for patient counseling and for the timing of interventions. In clinical practice, these patients are often monitored at recurrent clinical visits for the progression of the disease. It is desirable to have tools that can make personalized, real-time prediction of the risk of kidney/graft failure at each clinical visit, adapting to the time- varying patient conditions. Currently, the published risk prediction equation for CKD and KTx are usually developed by relating risk factors measured at an earlier time point, such as baseline, to the time of subsequent adverse event in a regression model. This approach cannot incorporate the longitudinal data from all the clinical visits, and may generate suboptimal or biased risk estimation and are not suitable for real-time prediction. Building upon on recent advancement in dynamic prediction (DP) methodology from the statistical literature, we aim to develop personalized, time-adapted risk prediction equations for CKD and KTx respectively. The proposed works include developing novel DP methods for kidney/graft failure with adjustment for the competing risk by death, external validation and re-calibration, and creating software for routine use in clinical practice. For CKD, the prediction model of kidney failure will be developed from the Chronic Renal Insufficiency Cohort Study (CRIC) data, and validated using the electronic health records of Veterans Health Administration. For KTx, the prediction model of graft failure will be developed from the Wisconsin Allograft Recipient Database (WisARD), and validated using the Scientific Registry of Transplant Recipients (SRTR). The statistical methodology and software can be used in other medical specialties beyond nephrology to develop risk prediction models for adverse clinical events from longitudinal data.

项目概要/摘要 慢性肾脏病 (CKD) 患者和接受肾移植 (KTx) 的患者面临以下风险： 肾/移植物衰竭。准确估计这些不良临床事件发生的时间对于 患者咨询和干预时机。在临床实践中，这些患者经常接受监测 定期进行临床检查以了解疾病的进展情况。最好有能够使 每次临床就诊时个性化、实时预测肾/移植失败的风险，适应时间 不同的患者情况。目前，已发表的 CKD 和 KTx 风险预测方程通常为 通过将较早时间点（例如基线）测量的风险因素与发生时间相关联来开发 回归模型中的后续不良事件。该方法无法合并来自的纵向数据 所有临床就诊，可能会产生次优或有偏差的风险估计，并且不适合实时 预言。基于统计动态预测 (DP) 方法的最新进展 文献中，我们的目标是为 CKD 和 KTx 开发个性化、适应时间的风险预测方程 分别。拟议的工作包括开发用于肾/移植物衰竭的新型 DP 方法并进行调整 针对死亡、外部验证和重新校准的竞争风险，并创建日常使用的软件 临床实践。对于CKD，肾衰竭的预测模型将从慢性肾病模型发展而来。 不足队列研究 (CRIC) 数据，并使用 Veterans Health 的电子健康记录进行验证 行政。对于 KTx，移植失败的预测模型将从威斯康星州同种异体移植中开发出来 受者数据库 (WisARD)，并使用移植受者科学登记处 (SRTR) 进行验证。 统计方法和软件可用于肾脏病学以外的其他医学专业 根据纵向数据开发不良临床事件的风险预测模型。