Alkaline Water: Help or Hype for Uric Acid and Cystine Urolithiasis?

PURPOSE: The consumption of alkaline water, water with an average pH of 8 to 10, has been steadily increasing globally as proponents claim it to be a healthier alternative to regular water. Urinary alkalinization therapy is frequently prescribed in patients with uric acid and cystine urolithiasis, and as such we analyzed commercially available alkaline waters to assess their potential to increase urinary pH. MATERIALS AND METHODS: Five commercially available alkaline water brands (Essentia, Smart Water Alkaline, Great Value Hydrate Alkaline Water, Body Armor SportWater, and Perfect Hydration) underwent anion chromatography and direct chemical measurements to determine the mineral contents of each product. The alkaline content of each bottle of water was then compared to that of potassium citrate (the gold standard for urinary alkalinization) as well as to other beverages and supplements used to augment urinary citrate and/or the urine pH. RESULTS: The pH levels of the bottled alkaline water ranged from 9.69 to 10.15. Electrolyte content was minimal, and the physiologic alkali content was below 1 mEq/L for all brands of alkaline water. The alkali content of alkaline water is minimal when compared to common stone treatment alternatives such as potassium citrate. In addition, several organic beverages, synthetic beverages, and other supplements contain more alkali content than alkaline water, and can achieve the AUA and European Association of Urology alkali recommendation of 30 to 60 mEq per day with ≤ 3 servings/d. CONCLUSIONS: Commercially available alkaline water has negligible alkali content and thus provides no added benefit over tap water for patients with uric acid and cystine urolithiasis.

Surgical Force: Initial Study and Clinical Implications in the Assessment of Ureteral Access Sheath Induced Injury.

Purpose: Ureteral access sheaths (UAS) pose the risk of severe ureteral injury. Our prior studies revealed forces ≤6 Newtons (N) prevent ureteral injury. Accordingly, we sought to define the force urologists and residents in training typically use when placing a UAS. Materials and Methods: Among urologists and urology residents attending two annual urological conferences in 2022, 121 individuals were recruited for the study. Participants inserted 12F, 14F, and 16F UAS into a male genitourinary model containing a concealed force sensor; they also provided demographic information. Analysis was completed using t-tests and Chi-square tests to identify group differences when passing a 16F sheath UAS. Participant traits associated with surpassing or remaining below a minimal force threshold were also explored through polychotomous logistic regression. Results: Participant force distributions were as follows: ≤4N (29%), >6N (45%), and >8N (32%). More years of practice were significantly associated with exerting >6N relative to forces between 4N and 6N; results for >8N relative to 4N and 8N were similar. Compared to high-volume ureteroscopists (those performing >20 ureteroscopies/month), physicians performing ≤20 ureteroscopies/month were significantly less likely to exert forces ≤4N (p = 0.017 and p = 0.041). Of those surpassing 6N and 8N, 15% and 18%, respectively, were high-volume ureteroscopists. Conclusions: Despite years of practice or volume of monthly ureteroscopic cases performed, most urologists failed to pass 16F access sheaths within the ideal range of 4N to 6N (74% of participants) or within a predefined safe range of 4N to 8N (61% of participants).

Efficient and Accurate Computed Tomography-Based Stone Volume Determination: Development of an Automated Artificial Intelligence Algorithm.

PURPOSE: Given the shortcomings of current stone burden characterization (maximum diameter or ellipsoid formulas), we sought to investigate the diagnostic accuracy and precision of a University of California, Irvine-developed artificial intelligence (AI) algorithm for determining stone volume determination. MATERIALS AND METHODS: A total of 322 noncontrast CT scans were retrospectively obtained from patients with a diagnosis of urolithiasis. The largest stone in each noncontrast CT scan was designated the "index stone." The 3D volume of the index stone using 3D Slicer technology was determined by a validated reviewer; this was considered the "ground truth" volume. The AI-calculated index stone volume was subsequently compared with ground truth volume as well with the scalene, prolate, and oblate ellipsoid formulas estimated volumes. RESULTS: There was a nearly perfect correlation between the AI-determined volume and the ground truth (R=0.98). While the AI algorithm was efficient for determining the stone volume for all sizes, its accuracy improved with larger stone size. Moreover, the AI stone volume produced an excellent 3D pixel overlap with the ground truth (Dice score=0.90). In comparison, the ellipsoid formula-based volumes performed less well (R range: 0.79-0.82) than the AI algorithm; for the ellipsoid formulas, the accuracy decreased as the stone size increased (mean overestimation: 27%-89%). Lastly, for all stone sizes, the maximum linear stone measurement had the poorest correlation with the ground truth (R range: 0.41-0.82). CONCLUSIONS: The University of California, Irvine AI algorithm is an accurate, precise, and time-efficient tool for determining stone volume. Expanding the clinical availability of this program could enable urologists to establish better guidelines for both the metabolic and surgical management of their urolithiasis patients.