Abstracts from the Abstracts from the Mid-Atlantic Section of the AUA 2021

© The Canadian Journal of Urology TM : International Supplement, October 2021 Moderated Poster Session 2: Stones/Infection/Pediatrics Ambulatory Percutaneous Nephrolithotomy Performed in a Free-Standing Surgery Center: Outcomes of 1000 Cases M. Dunne 1 , N. Ariasvillela 2 , J. Abbott 3 , J. Davalos 1 1 Chesapeake Urology and University of Maryland, Hanover, MD, USA; 2 University of Maryland, Baltimore, MD, USA; 3 Pacific West Urology, Los Angeles, CA, USA Introduction and Objective: Percutaneous Nephrolithotomy (PNL) is a procedure that is traditionally performed in an inpatient setting. Many procedures have evolved from an inpatient setting to ambulatory surgery center (ASC). Feasibility of ambulatory PNL (aPNL) was shown in our initial pilot series of 25 cases. This 1000 case series is reviewed to further evaluate outcomes with a more robust data set Methods: 1000 patients underwent aPNL from 2015 to 2021. Each was performed with the same operative team. All procedures were performed with urologist obtaining renal access and all were tubeless. Patients had hemostatic plugs placed into the access tract with a local intercostal block. Cases were reviewed and demographic data and case details were analyzed. Results: 1000 consecutive aPNL cases were reviewed, identifying 488 men and 512 women, 460 right side and 535 left, mean age 57 years, mean BMI 30, mean ASA of 2.3 and mean stone burden 31 mm, mean fluoroscopy time 84 sec, mean OR time 95 min, mean treatment time 14.9 min and mean PACU time 91 min. A mini-PNL (mPNL) procedure was conducted in 255 (25.5%) patients. The remaining 745 were standard size of which 449 were 30Fr and 296 were 24Fr. Stone free rate was 83%. Thirty-nine patients had complications ranging from Clavien II-IVa, 15 were hospital transfers. Conclusions: These 1000 cases may serve as a landmark series demonstrating the feasibility of aPNL. Transitioning PNL to an ambulatory setting is a paradigm shift in the treatment of complex kidney stones. Each complication that occurred was managed in an appropriate fashion and the site of service did not lead to an alteration in the outcomes of the adverse events. With an experienced surgeon, well trained operative team and with modifications to the procedure focusing on post-operative pain control, PNL can be safely and effectively performed in a free standing ASC. MP2-01 Existing Urine Exosome Gene Expression Signature to Assess Upgrade Risk on Radical Prostatectomy (RP). R. Tutrone 1 , A. Kretschmer 2 , M. Donovan 3 , J. Alter 4 , S. Kumar 4 , G. Sant 4 , M. Noerholm 4 , J. Skog 4 1 United Urology Group, Towson, MD, USA; 2 Ludwig-Maximilliams University, Munich, Germany; 3 Mt. Sinai Medical Center, New York, NY, USA; 4 Exosome Diagnostics (A Bio-Techne Brand), Waltham, MA, USA Introduction and Objective: Active surveillance (AS) is used to treat men with low-risk prostate cancer but sampling error, tumor heterogeneity/multi- focality complicate AS decision-making. The ExoDx Prostate (EPI) Test is a pre-biopsy urine biomarker for high-grade prostate cancer (HGPCa). Herein, we correlate EPI Test scores with RP pathologic upgrading and potential identification of men less suitable for AS. Methods: Patients with no history of PCa, >50 yrs, PSA 2-10 ng/mL and undergoing prostate biopsy (Bx) were included. The EPI Test was performed on first-catch, pre-Bx urine. This study focuses onmenwithGleasonGrade Group 1 (GG1) pathology on Bxwho underwent RP instead ofAS. The EPI scoreswere compared to amulti-parametric linear regressionmodel using covariates PSA, age, ethnicity and family history for correlation with Gleason RP upgrading. Results: 1563 patients in US and Europe (2014 to 2020) were studied. 295 men underwent RP including 106 (36%) with GG1 on biopsy. Between the Bx-GG1 (N=106) and the Bx >GG1 (N=189) cohorts, therewere no significant differences in age (60 [57-65] vs. 64 [59 - 68] years; p=0.61), PSAs (median PSA 5.32 [4.3 - 6.47] vs. 5.48 [4.3-7.0] ng/mL; p=0.66), AA ethnicity (2.8% vs. 7.4%; p=0.89) or family history of PCa (32% vs. 25%; p=0.33). In the Bx-GG1 group, 45% (48) were confirmed GG1 on RP, whereas 55% (58) were upgraded – 41% (43) GG2, 11% (12) GG3, 1% (1) GG4, and 2% (2) GG5. The model showed no significant differences between the groups (p >0.1). EPI scores for patients who remained GG1 vs those upgraded to GG2 showed no significant difference (p=0.45), whereas they were significantly higher (p <0.01) in those upgraded to ≥GG3. Conclusions: The EPI Test offers prognostic value for RP upgrading in men GG1 prostate cancer. Aliquid biomarker test maymore appropriately address tumor heterogeneity compared to post-biopsy tissue-based molecular tests. AutomatedMachine Learning Segmentation andMeasurement of Urinary Stones on CT Scan K. Lembrikova 1 , R. Babajide 1 , J. Ziemba 2 , Y. Fan 2 ,A. Selman Fermin 1 , G. Tasian 1 1 Children’s Hospital of Philadelphia, Philadelphia, PA, USA; 2 University of Pennsylvania, Philadelphia, PA, USA Introduction and Objective: Treatment decisions for patients with urinary stones depend on multiple factors including stone size, location, and renal anatomy. Manual human measurements introduce inconsistencies, are laborious, and time-consuming. Our objective was to evaluate the performance of a machine learning algorithm in measuring stone and anatomic features. Methods: Asample of 95 CT scans frompatients diagnosed with stones were included. Two raters manually measured stones in 3 orthogonal dimensions, renal pelvis width, and ureter diameter on 46 scans. A two-way random intraclass correlation (ICC) score was calculated to quantify intrarater agreement. The remaining 49 scans were used to train a deep learning model to segment stones. Times for manual andmachine calculations were recorded. Results: The sample included 19 scans with kidney stones, 17 with ureteral stones, and 10 with both. Median time in 3 dimensions was longer manually than with the algorithm (16.1 vs. 2.1 seconds). Intrarater reliability was poor for pelvis width (0.44, 95% CI 0.21 – 0.62) and ureter diameter (0.40, 95% CI 0.16 – 0.59), and good for stone size (0.79, 95% CI 0.75 – 0.83). The algorithm identified all stones present (100% sensitivity) with no false positive stones (100% specificity). At the individual voxel level, sensitivity of stone detection fell to 58%, while specificity remained at 100%, using manual measurements as ground truth. Although the algorithm reliably captured centers of stones, the total volume was smaller than identified by human raters. Conclusions: Manual measurements of ureteral stones and anatomy on CT are limited by the time required and poor reproducibility. The more rapid and accurate measurements provided by the algorithm can transform clinical care by enhancing and standardizing assessment across patients, institutions, and providers. MP2-02 MP1-15 12