Risk Factors of Incident Kidney Stones in Indian Adults: A Hospital-Based Cross-Sectional Study

Singh, Smriti; Gupta, Sanjay; Mishra, Tushar Subhadarshan; Banerjee, Basu Dev; Sharma, T.

doi:10.7759/cureus.35558

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…Other studies have identified some other risk factors, like male gender, increased age, sedentary lifestyles, and diets rich in fats, etc. [ 10 - 12 ]. However, studies on groundwater quality parameters of the stone-belt states are scarce.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Hardness and Alkalinity As Risk Factors for Kidney Stone Disease in Alwar, India: An Ecological Study

Manna,

Rathnam,

Udayaraj

et al. 2024

Cureus

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Introduction: Rajasthan is a semi-arid state in India where people still use groundwater for drinking purposes. However, the quality of groundwater as compared to standards have not been studied in any details. This ecological study was done to study the groundwater quality parameters in the stone-belt states, compare the quality of groundwater in Alwar with the rest of Rajasthan, and study the morbidity profile of surgical in-patients in the same district, with special emphasis on kidney stone disease (KSDs). Methods: The morbidity profile of patients coming to the surgery department of a tertiary teaching hospital between January 2002 and June 2023 was obtained from the medical records department, and water quality data was obtained from the publicly available Water Resources Information System (WRIS) groundwater dataset for the year 2023. The dataset provided detailed information on the chemical parameters of water samples throughout the country that were evaluated to estimate the quality of groundwater. Results: It was found that the groundwater in Alwar is non-potable due to the presence of iron, alkalinity, magnesium, and total dissolved solids (TDS). Iron was estimated to be much higher than the acceptable limit of the Bureau of Indian Standards (BIS) drinking-water quality guidelines (0.3 mg/L). Similarly, most of the chemical parameters in the groundwaters of Rajasthan significantly exceeded the national average. The median electrical conductivity, fluoride, magnesium, sodium, hardness, alkalinity, and turbidity were found to be 1680 μS/cm, 1.05 parts per million (PPM), 41 PPM, 233 PPM, 330 PPM, 310 PPM, 988 PPM, respectively, which are above the WHO recommendations for drinking water guidelines. Conclusions: The levels of iron and total alkalinity were significantly higher in the study district as compared to the rest of the state. Also, magnesium hardness and TDS levels were very high in the groundwater of the entire state of Rajasthan, making the population vulnerable to KSDs in the long run.

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Section: Introductionmentioning

confidence: 99%

Groundwater Hardness and Alkalinity As Risk Factors for Kidney Stone Disease in Alwar, India: An Ecological Study

Manna,

Rathnam,

Udayaraj

et al. 2024

Cureus

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Comparing the risk factors of nephrolithiasis in Asian countries population: A systematic review and meta-analysis

Ali,

Rustandi,

Sulchan

et al. 2023

Arab Journal of Urology

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This systematic review and meta-analysis aims to identify the main risk factors for nephrolithiasis in Asian populations, with comparisons to European and American populations. Using a comprehensive literature search across PubMed, Science Direct, and ResearchGate, in accordance with the Preferred Reporting Items of Systematic reviews and Meta-Analysis (PRISMA) guidelines, we synthesized data from 11 geographically diverse studies. Our findings reveal substantial population-specific differences in nephrolithiasis risk factors, particularly familial history, water consumption, and smoking patterns. In Asian populations, a 60% increase in risk was associated with a family history of nephrolithiasis. In the meantime, drinking sources also affected nephrolithiasis risk, with the consumption of boiled water being associated with a 25% increase in risk compared to consumption of bottled or mineral water. These findings highlight the importance of tailoring preventive strategies and treatments to specific risk factors, taking into account regional variations, and call for additional research to understand the complex interaction between genetic, environmental, and lifestyle factors in the development of nephrolithiasis.

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Automatic Kidney Stone Detection System using Guided Bilateral Feature Detector for CT Images

Ravi

et al. 2024

TOPHJ

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Background Kidney stones, common urological diseases worldwide, are formed from hard urine minerals in the kidneys. Early detection is essential to prevent kidney damage and manage recurring stones. CT imaging has made significant progress in providing detailed information for disease diagnosis. Aim This study aimed to enhance kidney stone detection through advanced imaging and machine learning techniques. Objective The Guided Bilateral Feature Detector was proposed to identify and extract features for kidney stone detection in CT images. Unlike traditional filters like Gaussian and Bilateral filters, the Guided Bilateral Filter Detector prevented halo artifacts and preserved image edges by employing a guide weight. The extracted features were combined with the SVM algorithm to accurately detect kidney stones in CT images. Methods The proposed detector used the Guided Bilateral Filter to reduce the halo artifacts in the images and enhance the feature detection process. The detector operated in four stages to extract important features from CT images, and a 128-feature point generator provided a more detailed representation in aiding kidney stone detection and classification. The proposed detector combined with the Support Vector Machine algorithm to improve reliability and reduce computational requirements. Results Experimental results showed that the proposed Guided Bilateral Feature Detector with SVM outperformed existing models, including SIFT+SVM, SURF+SVM, PCA+KNN, EANet, Inception v3, VGG16, and Resnet50. The key performance metrics achieved included an accuracy of 98.56%, precision of 98.9%, recall of 99.2%, and an F1 score of 99%. Conclusion The findings indicate that the Guided Bilateral Feature Detector with SVM significantly enhances the accuracy and reliability of kidney stone detection, providing valuable implications for clinical practice and future research in medical imaging.

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Risk Factors of Incident Kidney Stones in Indian Adults: A Hospital-Based Cross-Sectional Study

Cited by 4 publications

References 26 publications

Groundwater Hardness and Alkalinity As Risk Factors for Kidney Stone Disease in Alwar, India: An Ecological Study

Groundwater Hardness and Alkalinity As Risk Factors for Kidney Stone Disease in Alwar, India: An Ecological Study

Comparing the risk factors of nephrolithiasis in Asian countries population: A systematic review and meta-analysis

Automatic Kidney Stone Detection System using Guided Bilateral Feature Detector for CT Images

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