Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model

Abstract

There is a great need for a diagnostic tool using simple clinical information collected from patients to diagnose uric acid (UA) stones in nephrolithiasis. We built a predictive model making use of machine learning (ML) methodologies entering simple parameters easily obtained at the initial clinical visit. Socio-demographic, health, and clinical data from two cohorts (A and B), both diagnosed with nephrolithiasis, one between 2012 and 2016 and the other between June and December 2020, were collected before nephrolithiasis treatment. A ML-based model for predicting UA stones in nephrolithiasis was developed using eight simple parameters—sex, age, gout, diabetes mellitus, body mass index, estimated glomerular filtration rate, bacteriuria, and urine pH. Data from Cohort A were used for model training and validation (ratio 3:2), while data from Cohort B were used only for validation. One hundred and forty-six (13.3%) out of 1098 patients in Cohort A and 3 (4.23%) out of 71 patients in Cohort B had pure UA stones. For Cohort A, our model achieved a validation AUC (area under ROC curve) of 0.842, with 0.8475 sensitivity and 0.748 specificity. For Cohort B, our model achieved 0.936 AUC, with 1.0 sensitivity, and 0.912 specificity. This ML-based model provides a convenient and reliable method for diagnosing urolithiasis. Using only eight readily available clinical parameters, including information about metabolic disorder and obesity, it distinguished pure uric acid stones from other stones before treatment.

Theranostic roles of machine learning in clinical management of kidney stone disease

“…In another study, the microwave dielectric properties, which differ in various stone types, have been used to predict three types of kidney stones [63] . Moreover, the eight simple clinical parameters, including gender, age, body mass index, estimated glomerular filtration rate, urine pH, the presence of bacteriuria, the presence of gout, and the presence of diabetes mellitus, can improve uric acid stone prediction with an area under the curve (AUC) of 0.936 [64] .…”

The role of the general practictioner in the management of urinary calculi

“…Uric acid composition can be predicted from stone radiodensity on CT (HU < 500) and low urine pH values (pH < 5.2). Demonstration of the stone on ultrasound in the absence of radiopaque images on the abdominal X-ray may be an alternative to CT (48). Undersaturation of the urine with respect to uric acid causes the dissolution of uric acid stones and can be achieved by alkalizing the urine with citrate or bicarbonate, increasing urine volume and reducing the excretion of uric acid (allopurinol) (49)(50)(51)(52)(53)(54).…”

Data-Driven Technology Roadmaps to Identify Potential Technology Opportunities for Hyperuricemia Drugs

“…It is estimated that there are currently about 17.7 million hyperuricemia patients worldwide. As can be seen, hyperuricemia positively correlates to many other potential diseases, such as obesity, hypertension, diabetes, cardiovascular disease, and chronic kidney disease [11]. The primary approach in treating hyperuricemia is to rebalance uric acid synthesis and excretion.…”