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Purpose: To evaluate the association between dietary selenium intake and the risk of kidney stones in adults. Materials and methods: We performed a cross-sectional analysis using data from 2007 to 2018 National Health and Nutrition Examination Survey (NHANES). Dietary intake information of 30,184 participants was obtained using first 24-h dietary recall interview, and kidney stones were presented by a standard questionnaire. The quartile analysis, stratified analysis and non-linearity analysis were used to estimate the association between dietary selenium intake and kidney stones after an adjustment for potential confounders. Results: The multiple logistic regression indicated that the fourth quantile (Q4) of dietary selenium intake had a lower risk of kidney stones than the first quantile (Q1) in Model 3 (OR 0.82, P < 0.05). The stratified analyses indicated there were statistical differences between dietary selenium intake and kidney stones among younger (age < 50) (OR 0.65, P < 0.01), male (OR 0.73, P < 0.01) and overweight/obese (BMI ≥ 25.0) (OR 0.80, P < 0.05) individuals in Model 3. The non-linear relationship was founded between dietary selenium intake and kidney stones in all participants, younger, male and overweight/obese individuals after adjusting for confounding factors. Conclusion: Our study revealed an inverse relation between the level of dietary selenium intake and the risk of kidney stones for the United States population, especially for younger (age < 50), male and overweight/obese (BMI ≥ 25.0) individuals. The study provides preliminary guidance on dietary selenium intake for the prevention of kidney stones in different populations. Further studies are required to confirm our findings and clarified the biological mechanisms.
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Purpose: To establish the first comprehensive nomogram for prediction of infection stones before treatment for better perioperative treatment and postoperative prevention of infection stones. Methods: A total number of 461 patients with kidney stones who underwent mini-percutaneous nephrolithotomy and flexible ureteroscopy between January 2019 and March 2021 were retrospectively analyzed. Univariable analysis and multivariable logistic regression analysis were conducted to identify the predictors for infection stones. Furthermore, the nomogram was established as a predicted model for infection stones. Results: Among 461 patients with infrared spectroscopy stone analysis, 100 (21.70%) had infection stones and 361 (78.31%) had noninfection stones. Multivariate logistic regression analysis indicated that female (odds ratio [OR] 2.816, 95% confidence interval [CI] 1.148-6.909, p = 0.024), recurrent kidney stones (OR 8.263, 95% CI 2.295-29.745, p = 0.001), stone burden (OR 6.872, 95% CI 2.973-15.885, p < 0.001), HU (OR 15.208, 95% CI 6.635-34.860, p < 0.001), positive preoperative bladder urine culture (PBUC; OR 4.899, 95% CI 1.911-12.560, p = 0.001), positive urine leukocyte esterase (ULE; OR 3.144, 95% CI 1.114-8.870, p = 0.030), urine pH (OR 2.692, 95% CI 1.573-4.608, p < 0.001), and positive urine turbidity (OR 3.295, 95% CI 1.207-8.998, p = 0.020) were predictors for infection stone. Conclusions: For patients with kidney stones, female, recurrent kidney stones, stone burden (>601 mm2), HU (750-1000), positive PBUC, positive ULE, urine pH, and positive urine turbidity were predictors for infection stone. We established the first comprehensive model for identifying infection stones in vivo, which is extremely useful for the management of infection stones.
Subject(s)
Kidney Calculi , Nephrolithotomy, Percutaneous , Female , Humans , Kidney , Kidney Calculi/surgery , Nomograms , Retrospective StudiesABSTRACT
PURPOSE: To assess the value of procalcitonin (PCT) as an early biomarker for predicting urosepsis caused by Gram-negative (GN) bacteria, Gram-positive (GP) bacteria and fungi following mini-percutaneous nephrolithotomy (mPCNL) and flexible ureteroscopy (FURS). METHODS: A total number of 356 patients with positive preoperative UC (urine cultures) who underwent mPCNL and FURS between June 2017 and January 2021 were retrospectively analyzed. Univariable analysis and multivariable logistic regression analysis were conducted to compare the predictors for urosepsis caused by different organisms. Furthermore, the nomogram was established as a predicted model for urosepsis. RESULTS: Among 356 positive UC, 265 (74.4%) were positive for GN bacteria, 77 (21.4%) for GP bacteria and 14 (3.9%) for fungal pathogens. Escherichia coli (48.9%) were the predominant pathogens and Enterococcus (54/77) were the most common GP bacteria. Multivariate logistic regression analysis showed that positive nitrite (OR 3.31, 95% CI 1.20-9.14; P = 0.021), operative time > 90 min (OR 3.10, 95% CI 1.10-8.75, P = 0.033) and postoperative PCT > 0.1 ng/mL (OR 56.18, 95% CI 15.20-207.64, P < 0.001) were associated with postoperative urosepsis originated in GN infections, while urosepsis caused by GP bacteria and fungi was not associated with PCT > 0.1 ng/mL (P = 0.198), only stone burden > 800 mm2 (OR 3.69, 95% CI 1.01-13.53, P = 0.049) was an independent risk factor. CONCLUSIONS: For patients with positive preoperative UC, postoperative PCT > 0.1 ng/mL was an independent risk factor of post-PCNL and post-FURS urosepsis caused by GN bacteria rather than GP bacteria and fungi.