Predictors of Uric Acid Stones: Mean Stone Density, Stone Heterogeneity Index, and Variation Coefficient of Stone Density by Single-Energy Non-Contrast Computed Tomography and Urinary pH

Occupational hazard in urolithiasis patients in Qatar: A single-center cross-sectional study

BACKGROUND

Urolithiasis is one of the most prevalent urological diseases and is associated with a substantial economic burden. Its prevalence varies according to geographical location. Qatar is a Middle Eastern country located in the Afro-Asian Stone Belt. It has a dry and hot climate, which may predispose individuals working in these environments to form kidney stones (KSs).

METHODS

A population sample of 4204 patients was categorized into five occupational classes. The frequencies and correlations of these occupations with KS formation were calculated.

RESULTS

Among the total cases, 2000 presented with KSs, with the majority being of Asian descent (49%), followed by individuals of Middle Eastern descent (35.1%). Technicians accounted for 35.15% of KS cases followed by clerks (29.2%) and executives (14.6%). Among KS cases, 44% had a single stone, 30% had multiple stones, and 26% had two stones. In comparing both KS and non-KS groups, age, gender, occupation, and race were significantly associated with KS formation (p<0.05), while BMI did not show any significant correlation (p>0.05). Asian males aged 31-40, working as technicians, were significantly more prone to urolithiasis. In comparing age, BMI, and gender with stone characteristics, only age was found significantly associated with stone size (p<0.05). Occupation showed an impact on all studied stone characteristics. Clerks and technicians presented more frequently with stones within the 11-15 mm range, while executives more frequently presented with smaller stones (p<0.001). Stone density was more frequently <500 HU in workers, technicians and housewives and >500 HU in executives and clerks (p<0.001).

CONCLUSIONS

Our findings revealed an elevated risk of urolithiasis among certain occupational groups, particularly technicians, who frequently work outdoors in high-temperature environments. Alternatively, the sedentary nature of clerical and executive positions can also contribute to the risk of urolithiasis.

Pure uric acid stone prediction model using the variant coefficient of stone density measured by thresholding 3D segmentation-based methods: A multicenter study

Urinary stones are common urological diseases with increasing prevalence and incidence worldwide. Among the various types of stones, uric acid stones can be dissolved by oral chemolysis without any surgical procedure. Therefore, our study demonstrates that variant coefficient of stone density measured by thresholding a three-dimensional segmentation-based method from noncontrast computed tomography images can be used to identify pure uric acid stones from non-pure uric acid stones. This study provides a preoperative pure uric acid stone prediction model that could reduce invasive procedural treatments. The pure uric acid stone prediction model may offer optimized clinical decision-making for patients with urinary stones.

BACKGROUND AND OBJECTIVES

While most urinary stones are managed with interventional therapy, uric acid (UA) stones can be dissolved by oral chemolysis without invasive procedures. This study aimed to develop and validate a pure UA (pUA) stone prediction model using a variant coefficient of stone density (VCSD) measured by thresholding a three-dimensional (3D) segmentation-based method.

METHODS

Patients with urolithiasis treated at Keimyung University Dongsan Hospital between January 2017 and December 2020 were divided into training and internal validation sets, and patients from Kyungpook National University Hospital between January 2017 and December 2018 were used as an external validation set. Each stone was segmented by a thresholding 3D segmentation-based method using an attenuation threshold of 130 Hounsfield units. VCSD was calculated as the stone heterogeneity index divided by the mean stone density.

RESULTS

A total of 1175 urinary stone cases in 1023 patients were enrolled in this study. Of these, 224 (19.1%) were pUA stone cases. Among the potential predictors, thresholding 3D segmentation-based VCSD, age, sex, radio-opacity, hypertension, diabetes, and urine pH were identified as independent pUA stone predictors, and VCSD was the most powerful indicator. The pUA stone prediction model showed good discrimination, yielding area under the receiver operating characteristic curve of 0.960 (95% confidence interval (CI): 0.940-0.979, P < 0.001), 0.931 (95% CI: 0.875-0.987, P < 0.001), and 0.938 (95% CI: 0.912-0.965, P < 0.001) in the training, internal validation, and external validation sets, respectively.

CONCLUSIONS

VCSD measured using 3D segmentation was a decisive independent predictive factor for pUA stones. Furthermore, the established prediction model with VCSD can serve as a noninvasive preoperative tool to identify pUA stones.

A New Parameter for Calcium Oxalate Stones: Impact of Linear Calculus Density on Non-Contrast Computed Tomography

Background and Objectives: Non-contrast computed tomography (NCCT) is widely used to evaluate urolithiasis. The NCCT attenuation, measured in Hounsfield units (HU), has been evaluated to predict stone characteristics. We propose a novel parameter, linear calculus density (LCD), and analyze variables from NCCT imaging to predict calcium oxalate (CaOx) stones, which are common and challenging to fragment. Materials and Methods: We retrospectively reviewed the medical records of patients with urolithiasis between 2014 and 2017. Among those, 790 patients were included. Based on the NCCT pre-treatment, the maximal stone length (MSL), mean stone density (MSD), and stone heterogeneity index (SHI) were obtained. In addition, the variation coefficient of stone density (VCSD = SHI/MSD × 100) and linear calculus density (LCD = VCSD/MSL) were calculated. In accordance with the stone analysis, the patients were divided into two groups (CaOx and non-CaOx groups). The logistic regression model and receiver operating characteristic (ROC) curve were used for predictive modeling. Results: In the CaOx group, the SHI, VCSD, and LCD were more significant than in the non-CaOx group (all p < 0.001). SHI (OR 1.002, 95% CI 1.001-1.004, p < 0.001), VCSD (OR 1.028, 95% CI 1.016-1.041, p < 0.001), and LCD (OR 1.352, 95% CI 1.270-1.444, p < 0.001) were significant independent factors for CaOx stones in the logistic regression models. The areas under the ROC curve for predicting CaOx stones were 0.586 for SHI, 0.66 for VCSD, and 0.739 for LCD, with a cut-point of 2.25. Conclusions: LCD can be a useful new parameter to provide additional information to help discriminate CaOx stones before treatment.

The combination of mean and maximum Hounsfield Unit allows more accurate prediction of uric acid stones

Based on mean Hounsfield Unit (HuMean), we aimed to evaluate the additional use of standard deviation of Hounsfield Unit (HuStd), minimum Hounsfield Unit (HuMin), and maximum Hounsfield Unit (HuMax) in noncontrast computed tomography (NCCT) to evaluate uric acid (UA) stones more accurately. The data of patients who underwent the NCCT examination and infrared spectroscopy in our hospital from August 2017 to December 2021 were analyzed retrospectively. Based on CT scans, the HuMean, HuStd, HuMin, and HuMax of all patients were measured. The patients were divided into groups according to the stone composition. The attenuation value of mixed stones was in the middle of their pure stones. Except for Str, statistically significant differences between UA stones and other pure stones were observed for HuMean, HuStd, HuMin, and HuMax. A moderate correlation was found between HuMean, HuStd, HuMin, and HuMax and UA stones (r_s showed -0.585, -0.409, -0.492, and -0.577, respectively). Receiver operator characteristic (ROC) curve showed that the area under the curve (AUC) of HuMean and HuMax were higher than those of HuStd and HuMin (AUC = 0.896, AUC = 0.891 vs. AUC = 0.777, AUC = 0.833). Higher AUC (0.904), specificity (0.899) and positive predictive value (PPV) (0.712) can be obtained by combining HuMean and HuMax in the diagnosis of UA stones. In conclusion, HuMean and HuMax can better predict UA stones than HuStd and HuMin. The combined use of HuMean and HuMax can lead to higher accuracy.

Structure Types of Kidney Stones and Their Susceptibility to Shock Wave Fragmentation

Background:

The modern approach in the treatment of urolithiasis involves the use of non-invasive and minimally invasive techniques based on the stone fragmentation, among which shock wave lithotripsy (SWL) is considered as the first-line treatment for kidney stones < 2 cm and proximal ureter stones.

Objective:

To study the microstructure and mineral composition of kidney stones and to evaluate their influence on the stones’ susceptibility to fragmentation by shock waves.

Methods:

The microstructure and mineral composition of kidney stone samples obtained from shock wave lithotripsy in 87 patients were studied using crystal optical analysis and infrared spectroscopy. The volume fraction of amorphous and crystalline phases of the stone composition, the quantitative and qualitative composition of mineral components were assessed. The fragmentation features of stones with different microstructure were retrospectively analyzed based on the total number of shock waves required for complete stone fragmentation.

Results:

Three kidney stone structure types were identified: amorphous-crystalline structure stones predominantly including the amorphous phase (type A); amorphous-crystalline structure stones predominantly including the crystalline phase (type B); fully crystalline structure stones (type C). Significant positive correlation between the total number of shock waves required for complete stone fragmentation and the volume fraction of crystalline phase was found.

Conclusion:

The structure type of kidney stones is determined by the volume ratio between the amorphous and crystalline phases of their composition. The amorphous-crystalline structure stones with the predominant content of the amorphous phase are more sensitive to shock-wave exposure. The increase in the volume fraction of crystalline phase in the stone structure reduces the stone’s susceptibility to fragmentation by shock waves.