Tasian, Gregory E.

Introduction and Objective: Kidney stone growth and new stone formation are common clinical trial endpoints and are associated with future symptomatic events. To date, a manual review of CT scans has been required to assess stone growth and new stone formation, which is laborious. We validated the performance of a software algorithm that automatically identified, registered, and measured stones over longitudinal CT studies. Methods: We validated the performance of a pretrained machine learning algorithm to classify stone outcomes on longitudinal CT scan images at baseline and at the end of the 2-year follow-up period for 62 participants aged >18 years in the Prevention of Urinary Stones with Hydration (PUSH) randomized controlled trial. Stones were defined as an area of voxels with a minimum linear dimension of 2 mm that was higher in density than the mean plus 4 standard deviations of all nonnegative HU values within the kidney. The four outcomes assessed were: (1) growth of at least one existing stone by ≥2 mm, (2) formation of at least one new ≥2 mm stone, (3) no stone growth or new stone formation, and (4) loss of at least one stone. The accuracy of the algorithm was determined by comparing its outcomes to the gold standard of independent review of the CT images by at least two expert clinicians. Results: The algorithm correctly classified outcomes for 61 paired scans (98.4%). One pair that the algorithm incorrectly classified as stone growth was a new renal artery calcification on end-of-study CT. Conclusions: An automated image analysis method validated for the prospective PUSH trial was highly accurate for determining clinical outcomes of new stone formation, stone growth, stable stone size, and stone loss on longitudinal CT images. This method has the potential to improve the accuracy and efficiency of clinical care and endpoint determination for future clinical trials.

Enteric hyperoxaluria is a medical condition characterized by elevated urinary oxalate excretion due to increased gastrointestinal oxalate absorption. Causative features include fat malabsorption and/or increased intestinal permeability to oxalate. Enteric hyperoxaluria has long been known to cause nephrolithiasis and nephrocalcinosis, and, more recently, an association with CKD and kidney failure has been shown. Currently, there are no US Food and Drug Administration–approved therapies for enteric hyperoxaluria, and it is unclear what end points should be used to evaluate the efficacy of new drugs and biologics for this condition. This study represents work of a multidisciplinary group convened by the Kidney Health Initiative to review the evidence supporting potential end points for clinical trials in enteric hyperoxaluria. A potential clinical outcome is symptomatic kidney stone events. Potential surrogate end points include ( 1 ) an irreversible loss of kidney function as a surrogate for progression to kidney failure, ( 2 ) asymptomatic kidney stone growth/new stone formation observed on imaging as a surrogate for symptomatic kidney stone events, ( 3 ) urinary oxalate and urinary calcium oxalate supersaturation as surrogates for the development of symptomatic kidney stone events, and ( 4) plasma oxalate as a surrogate for the development of the clinical manifestations of systemic oxalosis. Unfortunately, because of gaps in the data, this Kidney Health Initiative workgroup was unable to provide definitive recommendations. Work is underway to obtain robust information that can be used to inform trial design and medical product development in this space.