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

项目摘要/摘要 慢性肾脏疾病(CKD)患者和接受肾移植(KTX)的患者存在 肾/移植物衰竭。准确估计这些不良临床事件的发生时间对于 患者咨询和干预的时机。在临床实践中，这些患者经常在 反复就诊以了解疾病的进展。人们希望有一种工具可以使 在每次临床就诊时对肾/移植失败风险进行个性化、实时预测，以适应时间- 病人的情况各不相同。目前已发表的CKD和KTX风险预测方程通常为 通过将在较早的时间点(如基线)测量的风险因素与 回归模型中的后续不良事件。此方法不能合并来自 所有临床就诊，可能会产生次优或有偏差的风险估计，不适合实时 预测。建立在统计动态预测(DP)方法最新进展的基础上 文献，我们的目标是为CKD和KTX开发个性化的、时间适配的风险预测方程 分别进行了分析。建议的工作包括开发治疗肾/移植物衰竭的新DP方法，并进行调整 对于因死亡、外部验证和重新校准以及创建常规使用的软件而产生的竞争风险 临床实践。对于慢性肾脏病，肾功能衰竭的预测模型将从慢性肾脏疾病发展而来。 不充分队列研究(CRIC)数据，并使用退伍军人健康电子健康记录进行验证 行政部门。对于KTX，移植失败的预测模型将从威斯康星州的同种异体移植发展而来 受者数据库(WisARD)，并使用移植受者科学登记(SRTR)进行验证。 该统计方法和软件可用于肾内科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。

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.