Clinical and Metabolic Correlates of Pure Stone Subtypes

Predictive Modeling of Urinary Stone Composition Using Machine Learning and Clinical Data: Implications for Treatment Strategies and Pathophysiological Insights

Purpose: Preventative strategies and surgical treatments for urolithiasis depend on stone composition. However, stone composition is often unknown until the stone is passed or surgically managed. Given that stone composition likely reflects the physiological parameters during its formation, we used clinical data from stone formers to predict stone composition. Materials and Methods: Data on stone composition, 24-hour urine, serum biochemistry, patient demographics, and medical history were prospectively collected from 777 kidney stone patients. Data were used to train gradient boosted machine and logistic regression models to distinguish calcium vs noncalcium, calcium oxalate monohydrate vs dihydrate, and calcium oxalate vs calcium phosphate vs uric acid stone types. Model performance was evaluated using the kappa score, and the influence of each predictor variable was assessed. Results: The calcium vs noncalcium model differentiated stone types with a kappa of 0.5231. The most influential predictors were 24-hour urine calcium, blood urate, and phosphate. The calcium oxalate monohydrate vs dihydrate model is the first of its kind and could discriminate stone types with a kappa of 0.2042. The key predictors were 24-hour urine urea, calcium, and oxalate. The multiclass model had a kappa of 0.3023 and the top predictors were age and 24-hour urine calcium and creatinine. Conclusions: Clinical data can be leveraged with machine learning algorithms to predict stone composition, which may help urologists determine stone type and guide their management plan before stone treatment. Investigating the most influential predictors of each classifier may improve the understanding of key clinical features of urolithiasis and shed light on pathophysiology.

Renal Stones and Gallstones Correlated with the Ten-Year Risk Estimation of Atherosclerotic Cardiovascular Disease Based on the Pooled Cohort Risk Assessment of Males Aged 40–79

Background: The risk of developing atherosclerotic cardiovascular disease (ASCVD) is unknown for subjects with both gallstones and renal stones, nor is it known whether there is a difference in the risk between gallstones and renal stones. This study aimed to determine the risk relationship between gallstones and renal stones and the risk of ASCVD in a male population. Methods: We recruited 6371 eligible males aged 40 to 79 years old who did not have a documented ASCVD history. The ten-year ASCVD risk was calculated using the pooled cohort equations developed by the American College of Cardiology (ACC) and the American Heart Association (AHA). The ASCVD risk score was classified as a low risk (<7.5%), an intermediate risk (7.5% to 19.9%), or a high risk (≥20%). The diagnosis of gallstones and renal stones was established based on the results of abdominal sonography. Results: Both gallstones and renal stones were associated with a high level of intermediate risk (OR = 3.21, 95% CI = 1.89–5.49, p < 0.001) and high risk (OR = 3.01, 95% CI = 1.48–6.12, p < 0.001), compared to individuals with no stones at all, after adjusting for the effects of other clinical variables. The possession of gallstones was associated with a higher level of high ASCVD risk (OR = 1.84, 95% CI = 1.31–2.59, p < 0.05) than that of renal stones. Conclusions: The ASCVD risk was higher for males with gallstones than for those with renal stones. Men with both types of stones faced a risk of ASCVD that was three times higher than that of men without stones.

Type 4 renal tubular acidosis and uric acid nephrolithiasis: two faces of the same coin?

PURPOSE OF REVIEW

The present review summarizes findings of recent studies examining the epidemiology, pathophysiology, and treatment of type 4 renal tubular acidosis (RTA) and uric acid nephrolithiasis, two conditions characterized by an abnormally acidic urine.

RECENT FINDINGS

Both type 4 RTA and uric acid nephrolithiasis disproportionately occur in patients with type 2 diabetes and/or chronic kidney disease. Biochemically, both conditions are associated with reduced renal ammonium excretion resulting in impaired urinary buffering and low urine pH. Reduced ammoniagenesis is postulated to result from hyperkalemia in type 4 RTA and from insulin resistance and fat accumulation in the renal proximal tubule in uric acid nephrolithiasis. The typical biochemical findings of hyperkalemia and systemic acidosis of type 4 RTA are rarely reported in uric acid stone formers. Additional clinical differences between the two conditions include findings of higher urinary uric acid excretion and consequent urinary uric acid supersaturation in uric acid stone formers but not in type 4 RTA.

SUMMARY

Type 4 RTA and uric acid nephrolithiasis share several epidemiological, clinical, and biochemical features. Although both conditions may be manifestations of diabetes mellitus and thus have a large at-risk population, the means to the shared biochemical finding of overly acidic urine are different. This difference in pathophysiology may explain the dissimilarity in the prevalence of kidney stone formation.

Metabolic evaluation: is there really a future?

PURPOSE OF REVIEW

To confirm the need for a metabolic evaluation in stone formers based on the latest information published in the last 24 months and in the current 2022 American and European urological guidelines.

RECENT FINDINGS

Recent studies suggest that urolithiasis prevalence has been increasing since 1990. Along with it, metabolic abnormalities that contribute to the development of kidney stones are also increasing, such as hyperuricosuria, hyperoxaluria, hypocitraturia, hypomagnesuria, hypocalciuria, hypophosphaturia, and hyperuricemia. Those abnormalities can only be detected through a metabolic evaluation in patients with stone disease.

SUMMARY

It is important for us, urologists, to have in mind the actual stone prevalence worldwide. As part of our initial evaluation of the patient with urolithiasis, we must not forget to perform the basic metabolic analysis and, according to the patient's risk classification, continue with a deep metabolic analysis. This extensive analysis includes blood and urine tests. The urinalysis covers 24-h urinalysis, and it would be accurate to also have an early fresh urine analysis. Moreover, identifying the stone type is intimately correlated with the search for other metabolic parameters.

Urinary stone composition in Germany: results from 45,783 stone analyses

Purpose

Stone composition can provide valuable information for the diagnosis, treatment and recurrence prevention of urolithiasis. The aim of this study was to evaluate the distribution of urinary stone components and the impact of different crystal forms according to gender and age of patients in Germany.

Methods

A total of 45,783 urinary stones submitted from 32,512 men and 13,271 women between January 2007 and December 2020 were analyzed by infrared spectroscopy. Only the first calculus obtained per patient was included in the analysis.

Results

The most common main stone component was calcium oxalate (CaOx) (71.4%), followed by carbonate apatite (CA) (10.2%) and uric acid (UA) (8.3%). Struvite (2.1%), brushite (1.3%), protein (0.5%) and cystine (0.4%) stones were only rarely diagnosed. CaOx (75%) and UA stones (81%) were more frequently obtained from men than women (p < 0.001). Weddellite (COD) and uric acid dihydrate (UAD) were more common in younger ages than whewellite (COM) and anhydrous uric acid (UAA), respectively, in both men and women. The ratios of COM-to-COD and UAA-to-UAD calculi were approximately 4:1 and 8:1, respectively. The peak of stone occurrence was between the ages of 40 and 59 years.

Conclusion

Stone composition is strongly associated with gender and age. The peak incidence of calculi in both women and men was in the most active phase of their working life. The distinction between different crystal forms could provide clues to the activity and mechanisms of lithogenesis. Further research is needed in understanding the causative factors and the process of stone formation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00345-022-04060-w.