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

> **NIH NIH R01** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2020 · $316,530

## Abstract

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.

## Key facts

- **NIH application ID:** 9912766
- **Project number:** 5R01DK118079-02
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** Brad C Astor
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $316,530
- **Award type:** 5
- **Project period:** 2019-04-10 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9912766

## Citation

> US National Institutes of Health, RePORTER application 9912766, Dynamic Prediction of Renal Failure Using Longitudinal Prognostic Information among Patients with Chronic Kidney Disease and Kidney Transplant (5R01DK118079-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9912766. Licensed CC0.

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