Sivalingam, Sri

Objective To assess the effect of Moses™ 2.0 (Lumenis Be Ltd., Yokneam Illit, Israel; Boston Scientific, Marlborough, MA, USA) for lithotripsy of renal stones of 8–20 mm in diameter on operative time, surgeon workload, and retropulsion. Subjects/Patients and Methods In our multicentre, randomised, double‐blind clinical trial, patients were randomised 1:1 to standard or Moses 2.0 laser lithotripsy and stratified by stone size. Patients and surgeons were blinded to laser modality. Primary outcomes were total operative time as well as surgeon workload assessed by the National Aeronautics and Space Administration‐Task Load Index (NASA‐TLX). Secondary outcomes were stone retropulsion evaluated by blinded video review and postoperative clinical outcomes. Linear regression modelling was used for analysis. Results A total of 153 patients (79 Standard, 74 Moses 2.0) with similar clinical, stone, and operative characteristics were evaluated. There was no significant difference in operative times between the Standard and Moses 2.0 groups ( P = 0.73). Moses 2.0 was associated with lower effort, frustration, mental, physical, and temporal workload, as well as higher rated overall performance compared to the standard laser. Video review demonstrated no difference in rates of retropulsion ( P = 0.13). Complication rates (Standard: 13%; Moses 2.0: 10%) and stone‐free rates (Standard: 51%; Moses: 2.0 55%) were comparable. Conclusion Our study evaluated the impact of Moses 2.0 technology on high‐powered renal stone dusting, constituting the largest double‐blind trial of its kind to date. There was insufficient evidence to suggest any difference in operative times or clinical outcomes when using Moses 2.0 compared to standard laser lithotripsy. Moses 2.0 was associated with reduced surgeon workload, which has implicit impact on surgeon wellness.

Introduction: Percutaneous nephrolithotomy (PCNL) has been a mainstay treatment for large stone burdens since the 1980s, historically offering improved stone-free rates over retrograde intrarenal surgery (RIRS). Gaining optimal access into the renal collecting system can be challenging, requiring advanced skills or interventional radiology assistance. The learning curve for fluoroscopic and ultrasonography access can be steep, with only a minority of PCNL access performed by urologists in the United States. This study compares robotic-assisted electromagnetic (EM) guidance to traditional fluoroscopy for obtaining percutaneous renal access between cohorts of novice and expert urologists. Methods: Ten novices and five expert urologists used robotic-assisted EM guidance to obtain access in a modified supine position compared with using fluoroscopy in a traditional prone position in human cadavers. Primary success was defined as papillary access. Performance metrics, including number of puncture attempts, time to access, radiation exposure, and participant confidence, were compared between novices and experts. Results: Robotic-assisted EM guidance improved success rates for both novices (100% vs 70%) and experts (93% vs 87%) compared with fluoroscopy. Novices showed greater accuracy using robotic assistance (97% vs 37%). The number of insertion attempts decreased with robotic guidance for both groups (novices: 3.42 ± 0.44 vs 1.47 ± 0.19; experts: 2.13 ± 0.36 vs 1.40 ± 0.24; p < 0.002). EM guidance ( p < 0.05) and experience ( p < 0.05) significantly reduced the time from needle insertion to access (novices: 12.86 ± 2.41 minutes vs 4.49 ± 0.96 minutes; experts: 4.90 ± 1.40 minutes vs 4.09 ± 1.12 minutes). Radiation exposure was notably lower with EM guidance (novices, 1.12 ± 0.17 mGy vs 4.86 ± 0.70 mGy; experts, 0.69 ± 0.12 mGy vs 4.11 ± 1.21 mGy; p < 0.001). Novices felt more confident (5[3–5] vs 2[1–4], p < 0.001) and at ease (5[3–5] vs 2.75[1–3], p < 0.001) with EM guidance. Conclusion: Robotic-assisted EM guidance improves percutaneous access success, reduces attempts and radiation exposure, and enhances novice confidence and accuracy. This technology could enable urologists to more effectively and safely perform PCNL, especially for less experienced practitioners.

Introduction and Objective: Kidney stone growth and new stone formation are common clinical trial endpoints and are associated with future symptomatic events. To date, a manual review of CT scans has been required to assess stone growth and new stone formation, which is laborious. We validated the performance of a software algorithm that automatically identified, registered, and measured stones over longitudinal CT studies. Methods: We validated the performance of a pretrained machine learning algorithm to classify stone outcomes on longitudinal CT scan images at baseline and at the end of the 2-year follow-up period for 62 participants aged >18 years in the Prevention of Urinary Stones with Hydration (PUSH) randomized controlled trial. Stones were defined as an area of voxels with a minimum linear dimension of 2 mm that was higher in density than the mean plus 4 standard deviations of all nonnegative HU values within the kidney. The four outcomes assessed were: (1) growth of at least one existing stone by ≥2 mm, (2) formation of at least one new ≥2 mm stone, (3) no stone growth or new stone formation, and (4) loss of at least one stone. The accuracy of the algorithm was determined by comparing its outcomes to the gold standard of independent review of the CT images by at least two expert clinicians. Results: The algorithm correctly classified outcomes for 61 paired scans (98.4%). One pair that the algorithm incorrectly classified as stone growth was a new renal artery calcification on end-of-study CT. Conclusions: An automated image analysis method validated for the prospective PUSH trial was highly accurate for determining clinical outcomes of new stone formation, stone growth, stable stone size, and stone loss on longitudinal CT images. This method has the potential to improve the accuracy and efficiency of clinical care and endpoint determination for future clinical trials.

Introduction: A number of clinical and radiological predictors of either stone impaction or ureteral stone passage (SP) have been proposed. We aimed at identifying the key predictors of successful SP by using readily available CT-based tools/measurements. Methods: Patients presenting to the emergency department from February 2017 to February 2018 with an acute unilateral ureteral stone confirmed on non-contrast CT and managed conservatively were followed for SP. Patients with renal impairment, sepsis or requiring emergent intervention were excluded. Patients were followed at 1 month to confirm SP (stone collection/repeat imaging) or failure of passage. The CT variables analyzed included: Stone factors [location, size, volume, HU density (HUD)], impaction factors [ureteral HUD above and below the stone, maximal ureteral wall thickness (UWT) at the stone site, contralateral UWT, and ureteral diameter above and below the stone]. Binary logistic regression analysis was performed to identify predictors of SP. Results: Forty-nine patients met study inclusion criteria, of whom 32 (65.3%) passed the stone without further intervention. Patients with successful passage were more likely to have smaller, lower volume and less dense stones located in the distal ureter ( p < 0.01). Lower ureteral HUD below the stone, lower maximal UWT, and lower ureteral diameter above the stone were associated with successful passage ( p < 0.01). On multivariable logistic regression analysis, only maximal UWT at the stone site was a significant independent predictor of SP outcome ( p = 0.01). Youden's criterion identified 2.3 mm as the optimal UWT cut-off point, which will accurately predict SP with 82.4% sensitivity and 87.5% specificity. Conclusions: Maximal UWT at the stone site was the most significant predictor of successful passage in acute unilateral ureteral stones, with an optimal cut-off point of 2.3 mm. Further prospective studies are needed to accurately predict spontaneous SP.