Can 24-Hour Urine Stone Risk Profiles Predict Urinary Stone Composition?

Protein Intake and High Uric Acid Stone Risk

Rationale & Objective

We evaluated the metabolic differences between pure and impure uric acid stone formers in this retrospective study of uric acid kidney stone formers diagnosed between 1996 and 2021.

Study Design

Demographics and medical history were compared by χ2 tests. Twenty-four-hour urine chemistries were compared using logistic regressions while controlling for demographics and comorbid conditions.

Setting & Participants

Patients from Yale Urology and Nephrology Clinics with a documented kidney stone analysis containing uric acid were included. In total, 4,294 kidney stone formers had a stone analysis, and 722 (16.8%) contained uric acid. Patients with all stone analyses  ≥ 50% uric acid were allocated to the pure group, while patients with ≥1 stone analysis <50% uric acid were allocated to the impure group.

Results

Among kidney stone formers, the prevalence of uric acid nephrolithiasis was 16.8%. Pure uric acid stone formers were more likely to be older, heavier, and were 1.5 times more likely to have chronic kidney disease. When controlling for age, sex, race, ethnicity, and body mass index, pure uric acid stone formers had lower urinary pH and lower urine citrate normalized for creatinine. Additionally, they had a higher protein catabolic rate, urine urea nitrogen, and urine sulfur normalized for creatinine, all markers of dietary protein intake. These findings persisted after controlling for chronic kidney disease.

Limitations

This is a retrospective study from a single center.

Conclusions

Pure uric acid stone formation is more common with diminished kidney function; however, after controlling for kidney function, pure uric acid stone formation is associated with protein intake, suggesting that modifying protein intake may reduce risk.

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.

Clinical and Metabolic Correlates of Pure Stone Subtypes

Background: There are multiple stone types, with each forming under different urinary conditions. We compared clinical and metabolic findings in pure stone formers (SFs) to understand whether there are consistent factors that differentiate these groups in terms of underlying etiology and potential for empiric treatment. Materials and Methods: Pure SFs based on infrared spectroscopic analysis of stones obtained at our institution between January 2002 and July 2018 with a corresponding 24-hour urinalysis were retrospectively evaluated. Results: One hundred twenty-one apatite (AP), 54 brushite (BRU), 50 calcium oxalate (CaOx) dihydrate, 104 CaOx monohydrate, and 82 uric acid (UA) patients were analyzed. AP, BRU, and CaOx dihydrate patients were younger than CaOx monohydrate and UA patients. The UA patients had the highest male predominance (76.8%), whereas AP patients were predominantly female (80.2%). UA was most associated with diabetes mellitus (45.3%), and CaOx monohydrate with cardiovascular disease (27.2%) and malabsorptive gastrointestinal conditions (19.2%). BRU patients had the highest prevalence of primary hyperparathyroidism (17%). AP, BRU, and CaOx dihydrate patients demonstrated high rates of hypercalciuria (66.1%, 79.6%, 82%). AP and BRU patients had the highest urinary pH. AP patients exhibited the highest rate of hypocitraturia, whereas CaOx dihydrate patients exhibited the lowest (55.4%, 30%). CaOx monohydrate patients had the highest rate of hyperoxaluria (51.9%). UA patients had the lowest urinary pH. There were no observable differences in the rates of hyperuricosuria or hypernatriuria. Conclusions: These results demonstrate that pure stone composition correlates with certain urinary and clinical characteristics. These data can help guide empiric clinical decision making.

Preliminary analysis of serum electrolytes and body mass index in patients with and without urolithiasis

OBJECTIVES

To compare body mass index (BMI); serum parameters; and urine parameters between patients with and without urolithiasis.

METHODS

Data from 1164 patients admitted to our Department of Urology from January 2011 to July 2013 were retrospectively reviewed; 714 patients (age, 5-87 years; male:female ratio, 1.8:1) exhibited urolithiasis, and 450 patients (age, 12-94 years; male:female ratio, 3.8:1) did not. Blood and urine were collected from patients the morning after hospital admission. Serum and urine parameters were checked by an automatic biochemistry analyzer. Statistical analysis included the Mann-Whitney U test and binary logistic regression.

RESULTS

Serum sodium, potassium, chloride, calcium, phosphorus, and carbon dioxide combining power significantly differed between groups. In male patients, serum sodium, calcium, and phosphorus levels were higher in the urolithiasis group, whereas serum potassium and urine pH levels were lower. In female patients, serum sodium was higher in the urolithiasis group. BMI was higher in the urolithiasis group in all patients, male and female. Respective β-values of serum sodium and BMI in male patients were 0.077 and 0.084; in female patients, these values were 0.119 and 0.102.

CONCLUSIONS

Changes in serum sodium and BMI may be involved in the pathogenesis and treatment of urolithiasis.

Evaluation of low-dose dual energy computed tomography for in vivo assessment of renal/ureteric calculus composition

PURPOSE

This study aimed to assess the accuracy of low-dose dual-energy computed tomography (DECT) in predicting the composition of urinary calculi.

MATERIALS AND METHODS

A total of 52 patients with urinary calculi were scanned with a 128-slice dual-source DECT scanner by use of a low-dose protocol. Dual-energy (DE) ratio, weighted average Hounsfield unit (HU) of calculi, radiation dose, and image noise levels were recorded. Two radiologists independently rated study quality. Stone composition was assessed after extraction by Fourier transform infrared spectroscopy (FTIRS). Analysis of variance was used to determine if the differences in HU values and DE ratios between the various calculus groups were significant. Threshold cutoff values to classify the calculi into separate groups were identified by receiver operating characteristic curve analysis.

RESULTS

A total of 137 calculi were detected. FTIRS analysis differentiated the calculi into five groups: uric acid (n=17), struvite (n=3), calcium oxalate monohydrate and dihydrate (COM-COD, n=84), calcium oxalate monohydrate (COM, n=28), and carbonate apatite (n=5). The HU value could differentiate only uric acid calculi from calcified calculi (p<0.001). The DE ratio could confidently differentiate uric acid, struvite, calcium oxalate, and carbonate apatite calculi (p<0.001) with cutoff values of 1.12, 1.34, and 1.66, respectively, giving >80% sensitivity and specificity to differentiate them. The DE ratio could not differentiate COM from COM-COD calculi. No study was rated poor in quality by either of the observers. The mean radiation dose was 1.8 mSv.

CONCLUSIONS

Low-dose DECT accurately predicts urinary calculus composition in vivo while simultaneously reducing radiation exposure without compromising study quality.

Nomogram to predict uric acid kidney stones based on patient's age, BMI and 24-hour urine profiles: A multicentre validation

INTRODUCTION

We performed a multicentre validation of a nomogram to predict uric acid kidney stones in two populations.

METHODS

We reviewed the kidney stone database of two institutions, searching for patients with kidney stones who had stone composition analysis and 24-hour urine collection from January 2010 to December 2013. A nomogram to predict uric acid kidneys stones based on patient age, body mass index (BMI), and 24-hour urine collection was tested. Receiver-operating curves (ROC) were performed.

RESULTS

We identified 445 patients, 355 from Cleveland, United States, and 90 from Sao Paulo, Brazil. Uric acid stone formers were 7.9% and 8.9%, respectively. Uric acid patients had a significantly higher age and BMI, as well as significant lower urinary calcium than calcium stone formers in both populations. Uric acid had significantly higher total points when scored according to the nomogram. ROC curves showed an area under the curve of 0.8 for Cleveland and 0.92 for Sao Paulo. The cutoff value that provided the highest sensitivity and specificity was 179 points and 192 for Cleveland and Sao Paulo, respectively. Using 180 points as a cutoff provided a sensitivity and specificity of 87.5% and 68% for Cleveland, and 100% and 42% for Sao Paulo. Higher cutoffs were associated with higher specificity. The main limitation of this study is that only patients from high volume hospitals with uric acid or calcium stones were included.

CONCLUSION

Predicting uric acid kidneys stone based on a nomogram, which includes only demographic data and 24-hour urine parameters, is feasible with a high degree of accuracy.

Metabolic evaluation of urinary lithiasis: what urologists should know and do

INTRODUCTION

Urolithiasis is a complex medical entity and regroups several different types of stones, each caused by a multitude of dietary imbalances or metabolic anomalies. In order to better assess the stone-forming patient, urologists should be competent in performing a thorough metabolic work-up.

MATERIALS AND METHODS

We reviewed the litterature in order to provide an appropriate overview of the various components of the metabolic evaluation, including stone analysis, biochemistry tests, and urine collection.

CONCLUSION

Performing a metabolic evaluation allows precise intervention in order to treat and mainly prevent stone disease.