Enhanced Recovery After Surgery (ERAS) protocols have been developed in several fields to reduce hospitalization lengths and overall costs. There have also been developments in multimodal analgesia methods to curtail opioid usage after surgery. Herein, we present the results of our initiation of an ERAS protocol for robotic-assisted laparoscopic partial and radical nephrectomies, employing a quadratus lumborum (QL) regional anesthetic block. We retrospectively reviewed 614 patients in our Institutional Review Board approved database who underwent robotic-assisted laparoscopic partial or radical nephrectomies from January 2017 to February 2020. An ERAS protocol utilizing multimodal analgesia (acetaminophen and gabapentin) and a QL block was developed and introduced in February 2019. We then compared the opioid consumption and perioperative outcomes of patients before and after ERAS protocol initiation. 192 ERAS patients (February 2019 to February 2020) were compared to 422 non-ERAS patients (January 2017 to January 2019). Baseline characteristics and the proportion of preoperative opioids users were similar between the two groups. There were no statistically significant differences in surgery length, hospitalization length, or complication rates. There were statistically significant differences in our primary endpoint, opioid consumption, on post-operative days 0 (p < 0.001), 1 (p < 0.001), and 2 (p < 0.001). The total opioid requirements over the course of admission were lower in the ERAS group compared to the non-ERAS group (p = 0.03). The initiation of an ERAS protocol employing multimodal analgesia and a QL block, for patients undergoing robotic-assisted laparoscopic partial or radical nephrectomies, can decrease opioid requirements without compromising perioperative outcomes.

The growth and adoption of artificial intelligence has led to impressive results in urology. As artificial intelligence grows more ubiquitous, it is important to establish artificial intelligence literacy in the workforce. To this end, we present a narrative review of the literature of artificial intelligence and machine learning in urology and propose a checklist of reporting standards to improve readability and evaluate the current state of the literature. The listed article demonstrated heterogeneous reporting of methodologies and outcomes, limiting generalizability of research. We hope that this review serves as a foundation for future evaluation of medical research in artificial intelligence.

Introduction Sociodemographic factors have been shown to have significant impacts on bladder cancer (BC) outcomes, but there are conflicting data in the literature regarding certain non-modifiable factors. We sought to determine the effect of sociodemographic factors on survival outcomes after radical cystectomy (RC) for BC. Materials and methods A systematic review of population-based cohort studies published before March 2020 from Surveillance, Epidemiology, and End Results (SEER) and National Cancer Database (NCDB) was performed per Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines by searching PubMed ® , Scopus ® , and Web of Science ® . All full-text English-language articles assessing the impact of sociodemographic factors on BC survival after RC were obtained. Two investigators (WY and AC) independently screened all articles. Discrepancies were resolved by consensus. All studies reporting survival outcomes after RC based on any of the sociodemographic factors were included, except for systematic reviews, which were excluded. Primary end points were overall survival (OS) and disease-specific survival (DSS) after RC. Cohort studies reporting Cox proportional hazards or logistic regression analysis were independently screened. Available multivariable hazard ratios (HRs) were included in the quantitative analysis. Results Our search returned 147 studies, of which 14 studies (11 SEER and 3 NCDB) were included for cumulative analysis. Only race and gender were evaluable due to heterogeneity of other factors. Compared to White patients, Black patients have worse OS [HR 0.83; 95% confidence intervals (CIs) 0.75, 0.92; p < 0.01; I2 = 79%] and DSS (HR 0.83; 95% CI 0.69, 1.00; p = 0.05; I2 = 69%), Asian patients have worse OS (HR 0.84; 95% CI 0.77, 0.92; p < 0.01; I2 = 15%) but not DSS (HR 0.81; 95% CI 0.31, 2.10; p = 0.66), Hispanic patients have no difference in OS (HR 1.03; 95% CI 0.79, 1.34; p = 0.66; I2 = 72%) or DSS (HR 2.63; 95% CI 0.34, 20.34; p = 0.35), and Native American patients have no difference in OS (HR 2.16; 95% CI 0.80, 5.83; p = 0.13). Compared to men, women have no difference in OS (HR 1.03; 95% CI 0.93, 1.15; p = 0.53; I2 = 92%) nor DSS (HR 0.99; 95% CI 0.90, 1.08; p = 0.78; I2 = 1%). Conclusions Disparate BC survival outcomes after RC are present, with Black patients having poorer OS and DSS as compared to White patients. Asian patients have lower OS but not DSS. Survival outcomes do not appear to differentiate by gender. Significant heterogeneity in variable and outcome definitions limited our ability to perform meta-analyses involving other potentially important drivers and sources of disparate outcomes.

The field of artificial intelligence continues to advance rapidly. Improvements in both patient outcomes and the patient-doctor relationship may occur if physicians embrace this technology.

OBJECTIVE:To compare racial differences in male fertility history and treatment. DESIGN:Retrospective review of prospectively collected data. SETTING:North American reproductive urology centers. PATIENT(S):Males undergoing urologist fertility evaluation. INTERVENTION(S):None. MAIN OUTCOME MEASURE(S):Demographic and reproductive Andrology Research Consortium data. RESULT(S):The racial breakdown of 6,462 men was: 51% White, 20% Asian/Indo-Canadian/Indo-American, 6% Black, 1% Indian/Native, <1% Native Hawaiian/Other Pacific Islander, and 21% "Other". White males sought evaluation sooner (3.5 ± 4.7 vs. 3.8 ± 4.2 years), had older partners (33.3 ± 4.9 vs. 32.9 ± 5.2 years), and more had undergone vasectomy (8.4% vs. 2.9%) vs. all other races. Black males were older (38.0 ± 8.1 vs. 36.5 ± 7.4 years), sought fertility evaluation later (4.8 ± 5.1 vs. 3.6 ± 4.4 years), fewer had undergone vasectomy (3.3% vs. 5.9%), and fewer had partners who underwent intrauterine insemination (8.2% vs. 12.6%) compared with all other races. Asian/Indo-Canadian/Indo-American patients were younger (36.1 ± 7.2 vs. 36.7 ± 7.6 years), fewer had undergone vasectomy (1.2% vs. 6.9%), and more had partners who underwent intrauterine insemination (14.2% vs. 11.9%). Indian/Native males sought evaluation later (5.1 ± 6.8 vs. 3.6 ± 4.4 years) and more had undergone vasectomy (13.4% vs. 5.7%). CONCLUSION(S):Racial differences exist for males undergoing fertility evaluation by a reproductive urologist. Better understanding of these differences in history in conjunction with societal and biologic factors can guide personalized care, as well as help to better understand and address disparities in access to fertility evaluation and treatment.

Renal transplant recipients are at greater risk for malignancy than the general population. Historical management of renal cell carcinoma in a renal allograft has been open radical nephrectomy, but over time, graft-preserving and minimally-invasive techniques have been described. We present, in video format, our technique for robotic-assisted laparoscopic partial nephrectomy in a transplanted kidney.

The gold standard surgical treatment for muscle invasive bladder cancer is radical cystectomy and urinary diversion. This procedure has historically been performed as an open surgery. With the advances of robotic surgery, robotic cystectomy and urinary diversion has gained popularity with the ability to perform intracorporeal urinary diversions in addition to extirpative surgery. Herein, we detail our technique for intracorporeal ileal conduit.

No AccessJournal of UrologyJU Forum1 Dec 2020The Effect of Loss of Anonymity from Direct-to-Consumer DNA Databases on Sperm Donation Attitudes and Practices of American Sperm Donors Sigal Klipstein, Andrew Chen, and Mary Samplaski Sigal KlipsteinSigal Klipstein InVia Fertility Specialists and University of Chicago Pritzker School of Medicine, Northbrook, Illinois More articles by this author , Andrew ChenAndrew Chen Institute of Urology, University of Southern California, Los Angeles, California More articles by this author , and Mary SamplaskiMary Samplaski *Correspondence: 1441 Eastlake Ave., Health Sciences Campus, Los Angeles, California 90035 telephone: 323-865-3700; E-mail Address: [email protected] Institute of Urology, University of Southern California, Los Angeles, California More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000001141AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "The Effect of Loss of Anonymity from Direct-to-Consumer DNA Databases on Sperm Donation Attitudes and Practices of American Sperm Donors." The Journal of Urology, 204(6), pp. 1125–1126 References 1. : Direct-to-consumer DNA testing: the fallout for individuals and their families unexpectedly learning of their donor conception origins. Hum Fertil (Camb) 2018; 21: 225. Google Scholar 2. : The end of donor anonymity: how genetic testing is likely to drive anonymous gamete donation out of business. Hum Reprod 2016; 31: 1135. Google Scholar 3. : Consumer genomics will change your life, whether you get tested or not. Genome Biol 2018; 19: 120. Google Scholar 4. The Donor Sibling Registry. Available at https://www.donorsiblingregistry.com/. Google Scholar 5. : Motivations and attitudes of candidate sperm donors in Belgium. Fertil Steril 2017; 108: 539. Google Scholar © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 204Issue 6December 2020Page: 1125-1126 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Sigal Klipstein InVia Fertility Specialists and University of Chicago Pritzker School of Medicine, Northbrook, Illinois More articles by this author Andrew Chen Institute of Urology, University of Southern California, Los Angeles, California More articles by this author Mary Samplaski Institute of Urology, University of Southern California, Los Angeles, California *Correspondence: 1441 Eastlake Ave., Health Sciences Campus, Los Angeles, California 90035 telephone: 323-865-3700; E-mail Address: [email protected] More articles by this author Expand All Advertisement PDF DownloadLoading ...

Automated performance metrics objectively measure surgeon performance during a robot-assisted radical prostatectomy. Machine learning has demonstrated that automated performance metrics, especially during the vesico-urethral anastomosis of the robot-assisted radical prostatectomy, are predictive of long-term outcomes such as continence recovery time. This study focuses on automated performance metrics during the vesico-urethral anastomosis, specifically on stitch versus sub-stitch levels, to distinguish surgeon experience. During the vesico-urethral anastomosis, automated performance metrics, recorded by a systems data recorder (Intuitive Surgical, Sunnyvale, CA, USA), were reported for each overall stitch (Ctotal) and its individual components: needle handling/targeting (C1), needle driving (C2), and suture cinching (C3) (Fig 1, A). These metrics were organized into three datasets (GlobalSet [whole stitch], RowSet [independent sub-stitches], and ColumnSet [associated sub-stitches] (Fig 1, B) and applied to three machine learning models (AdaBoost, gradient boosting, and random forest) to solve two classifications tasks: experts (≥100 cases) versus novices (<100 cases) and ordinary experts (≥100 and <2,000 cases) versus super experts (≥2,000 cases). Classification accuracy was determined using analysis of variance. Input features were evaluated through a Jaccard index. From 68 vesico-urethral anastomoses, we analyzed 1,570 stitches broken down into 4,708 sub-stitches. For both classification tasks, ColumnSet best distinguished experts (n = 8) versus novices (n = 9) and ordinary experts (n = 5) versus super experts (n = 3) at an accuracy of 0.774 and 0.844, respectively. Feature ranking highlighted Endowrist articulation and needle handling/targeting as most important in classification. Surgeon performance measured by automated performance metrics on a granular sub-stitch level more accurately distinguishes expertise when compared with summary automated performance metrics over whole stitches.