An Enhanced Recovery After Surgery protocol for robotic-assisted laparoscopic nephrectomies utilizing a quadratus lumborum block.

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.

Artificial Intelligence Applications in Urology: Reporting Standards to Achieve Fluency for Urologists.

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.

Robotic Intracorporeal Ileal Conduit Urinary Diversion Technique.

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.

One size does not fit all: variations by ethnicity in demographic characteristics of men seeking fertility treatment across North America.

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.

Artificial Intelligence Will (MAY) Make Doctors Expendable (IN GOOD WAYS): Pro.

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.

Machine learning analyses of automated performance metrics during granular sub-stitch phases predict surgeon experience.

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.