Does potassium citrate administration change the type and composition of encrusted material on Double-J stent compared to primary stone?

Urological Challenges during Pregnancy: Current Status and Future Perspective on Ureteric Stent Encrustation

The management of ureter hydronephrosis and urolithiasis during pregnancy has been changed by the adoption of ureteric stents. Despite their broad use for several other conditions, from emergency to elective settings, their complications cannot be ignored. Being most prevalent during pregnancy, urinary tract infections and stent encrustations are particularly common and can affect either fetal growth or maternal-fetal homeostasis, leading to obstetric complications. The main concern associated with ureteric stents is the indwelling time, which could represent the potential trigger of those complications. However, to ensure the optimal management of a ureteric stent during pregnancy, factors such as the grading of encrustations and the presence, size, and location of stones should be evaluated in pre-operative planning. As a consequence, a multimodal approach, including obstetrics, gynecologists, urologists, and nurses, is essential to ensure a complication-free procedure and successful ureteric stent removal. Finally, future research should focus on utilizing biodegradable and biocompatible materials to reduce and even eliminate the complications related to forgotten stents in order to reduce the financial burden associated with stent replacement and the management of stent-encrustation-related complications.

Identifying ureteral stent encrustation using machine learning based on CT radiomics features: a bicentric study

Obstructive

To develop and validate radiomics and machine learning models for identifying encrusted stents and compare their recognition performance with multiple metrics.

Methods

A total of 354 patients with ureteral stent placement were enrolled from two medical institutions and divided into the training cohort (n = 189), internal validation cohort (n = 81) and external validation cohort (n = 84). Based on features selected by Wilcoxon test, Spearman Correlation Analysis and least absolute shrinkage and selection operator (LASSO) regression algorithm, six machine learning models based on radiomics features were established with six classifiers (LR, DT, SVM, RF, XGBoost, KNN). After comparison with those models, the most robust model was selected. Considering its feature importance as radscore, the combined model and a nomogram were constructed by incorporating indwelling time. Accuracy, sensitivity, specificity, area under the curve (AUC), decision curve analysis (DCA) and calibration curve were used to evaluate the recognition performance of models.

Results

1,409 radiomics features were extracted from 641 volumes of interest (VOIs) and 20 significant radiomics features were selected. Considering the superior performance (AUC 0.810, 95%CI, 0.722-0.888) in the external validation cohort, feature importance of XGBoost was used as a radscore, constructing a combined model and a nomogram with indwelling time. The accuracy, sensitivity, specificity and AUC of the combined model were 98, 100, 97.3% and 0.999 for the training cohort, 83.3, 80, 84.5% and 0.867 for the internal cohort and 78.2, 76.3, 78.8% and 0.820 for the external cohort, respectively. DCA indicates the favorable clinical utility of models.

Conclusion

Machine learning model based on radiomics features enables to identify ureteral stent encrustation with high accuracy.

The correlation between promoter hypermethylation of VDR, CLDN, and CasR genes and recurrent stone formation

Objectives

Recurrent Kidney stone formation is a main medical problem imposing a significant burden on both healthcare and the economy worldwide. Environmental and genetic factors have been linked to a bigger risk of kidney stone formation. We aim to assess the role of methylation on recurrent stone formation in three target genes.

Methods

We aimed to check the association between promoter hypermethylation vitamin D receptor (VDR), calcium-sensing receptor (CaSR), and claudin 14 (CLDN14) genes in recurrent kidney stones. We enrolled 30 consecutive recurrent kidney stone formers (age 18–60 years) (cases) and 30 age and gender-matched controls.3. To identify promoter methylation, two target regions from each candidate gene were bisulfited after blood collection and DNA extraction. Methylation quantification was done through methylation-specific high resolution melting (MS-HRM).

Results

The mean age of the patients and controls (mean ± SD) was 49.58 ± 14.23 years and BMI 36.12 ± 2.72. The methylation status in all six target regions was meaningfully different between the stone-former group and controls when methylation was considered in three clusters of unmethylated, methylated, and hypermethylated. A higher effect in VDR and CLDN was observed compare to CasR (p-value < 0.001, and < 0.005 versus p-value < 0.256).

Conclusions

Methylation as an important epigenetic mechanism should be considered more in recurrent stone formations. Promoter hypermethylation of VRD and CLDN genes may have an essential role in recurrent kidney stones formations.