  Autosomal polycystic kidney disease (ARPKD) is associated with progressive
enlargement of the kidneys fuelled by the formation and expansion of
fluid-filled cysts. The disease is congenital and children that do not succumb
to it during the neonatal period will, by age 10 years, more often than not,
require nephrectomy+renal replacement therapy for management of both pain and
renal insufficiency. Since increasing cystic index (CI; percent of kidney
occupied by cysts) drives both renal expansion and organ dysfunction,
management of these patients, including decisions such as elective nephrectomy
and prioritization on the transplant waitlist, could clearly benefit from
serial determination of CI. So also, clinical trials in ARPKD evaluating the
efficacy of novel drug candidates could benefit from serial determination of
CI. Although ultrasound is currently the imaging modality of choice for
diagnosis of ARPKD, its utilization for assessing disease progression is highly
limited. Magnetic resonance imaging or computed tomography, although more
reliable for determination of CI, are expensive, time-consuming and somewhat
impractical in the pediatric population. Using a well-established mammalian
model of ARPKD, we undertook a big data-like analysis of minimally- or
non-invasive serum and urine biomarkers of renal injury/dysfunction to derive a
family of equations for estimating CI. We then applied a signal averaging
protocol to distill these equations to a single empirical formula for
calculation of CI. Such a formula will eventually find use in identifying and
monitoring patients at high risk for progressing to end-stage renal disease and
aid in the conduct of clinical trials.
