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Autosomal polycystic kidney disease (ARPKD) is associated with progressiveenlargement of the kidneys fuelled by the formation and expansion offluid-filled cysts. The disease is congenital and children that do not succumbto it during the neonatal period will, by age 10 years, more often than not,require nephrectomy+renal replacement therapy for management of both pain andrenal insufficiency. Since increasing cystic index (CI; percent of kidneyoccupied by cysts) drives both renal expansion and organ dysfunction,management of these patients, including decisions such as elective nephrectomyand prioritization on the transplant waitlist, could clearly benefit fromserial determination of CI. So also, clinical trials in ARPKD evaluating theefficacy of novel drug candidates could benefit from serial determination ofCI. Although ultrasound is currently the imaging modality of choice fordiagnosis of ARPKD, its utilization for assessing disease progression is highlylimited. Magnetic resonance imaging or computed tomography, although morereliable for determination of CI, are expensive, time-consuming and somewhatimpractical in the pediatric population. Using a well-established mammalianmodel of ARPKD, we undertook a big data-like analysis of minimally- ornon-invasive serum and urine biomarkers of renal injury/dysfunction to derive afamily of equations for estimating CI. We then applied a signal averagingprotocol to distill these equations to a single empirical formula forcalculation of CI. Such a formula will eventually find use in identifying andmonitoring patients at high risk for progressing to end-stage renal disease andaid in the conduct of clinical trials.

An Empirical Biomarker-based Calculator for Autosomal Recessive Polycystic Kidney Disease - The Nieto-Narayan Formula