Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) IV: Prognosis for End-Stage Renal Disease and Biomarker Validation

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Project Summary The Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) is a unique, longitudinal, observational cohort study of 241 autosomal dominant polycystic kidney disease (ADPKD) patients that began in 2000. The consortium consists of researchers at University of Alabama, University of Chicago, Emory University, University of Kansas Medical Center, Mayo Clinic, and University of Pittsburgh. The broad, long- term objectives of CRISP are to define the natural history of the disease and to discover prognostic biomarkers in early disease that can accurately predict long-term renal outcomes. The goals of CRISP IV are to continue follow-up of the CRISP cohort for 5 more years so as to refine models of CKD progression and strengthen the association between htTKV and renal outcomes, to validate disease models using existing and additional follow-up data from the HALT PKD study, and to incorporate powerful emerging imaging, genetic and biochemical biomarkers to improve the accuracy of prognostication for individual patients. To achieve this, the specific aims are: Aim 1. Continue follow-up of CRISP and HALT participants. All CRISP participants that have not reached ESRD, together with HALT Study A participants followed at CRISP sites, will be followed up with annual serum creatinine measurements. Aim 2. Develop improved models to predict GFR trajectories and progression to ESRD. Existing logistic regression and trajectory models will be refined and new approaches such as artificial neural network and classification tree models developed, using CRISP data for model fitting and HALT-A data for external validation. Aim 3. Develop novel imaging, biochemical and genetic biomarkers of disease progression. Highly promising new biomarkers that are emerging from ancillary studies or being developed in parallel ongoing studies at the CRISP centers, will be evaluated for their ability to improve the accuracy of prognostic models.