The effect of enhanced recovery after surgery on oncologic outcome following radical cystectomy for urothelial bladder carcinoma.

INTRODUCTION: Limited data are available regarding the effect of enhanced recovery after surgery (ERAS) protocols on the long-term outcomes of radical cystectomy (RC) in bladder cancer patients. The aim of this study is to evaluate the oncological outcomes in patients who underwent RC with ERAS protocol. METHODS: We reviewed the records of patients who underwent RC for primary urothelial bladder carcinoma with curative intent from January 2003 to August 2022. The primary and secondary outcomes were recurrence-free (RFS) and overall survival (OS). Multivariable Cox regression analysis was performed to evaluate the effect of ERAS on oncological outcomes. RESULTS: A total of 967 ERAS patients and 1144 non-ERAS patients were included in this study. The RFS rates at 1, 3, and 5 years after RC were 81%, 71.5%, and 69% in the ERAS cohort, respectively. This rate in the non-ERAS group was 81%, 71%, and 67% at 1, 3, and 5 years after RC, respectively (P = 0.50). However, ERAS patients had significantly better OS with 86%, 73%, and 67% survival rates at 1, 3, and 5 years compared to 84%, 68%, and 59.5% survival rates in the non-ERAS group, respectively (P = 0.002). In multivariable analysis adjusting for other relevant factors, ERAS was no longer independently associated with recurrence-free (HR = 0.96, 95% CI 0.76-1.22, P = 0.75) or overall survival (HR = 0.84, 95% CI 0.66-1.09, P = 0.28) following RC. CONCLUSION: ERAS protocols are associated with a shorter hospital stay, yet with no impact on long-term oncologic outcomes in patients undergoing RC for bladder cancer.

Clinical applications of artificial intelligence in robotic surgery.

Artificial intelligence (AI) is revolutionizing nearly every aspect of modern life. In the medical field, robotic surgery is the sector with some of the most innovative and impactful advancements. In this narrative review, we outline recent contributions of AI to the field of robotic surgery with a particular focus on intraoperative enhancement. AI modeling is allowing surgeons to have advanced intraoperative metrics such as force and tactile measurements, enhanced detection of positive surgical margins, and even allowing for the complete automation of certain steps in surgical procedures. AI is also Query revolutionizing the field of surgical education. AI modeling applied to intraoperative surgical video feeds and instrument kinematics data is allowing for the generation of automated skills assessments. AI also shows promise for the generation and delivery of highly specialized intraoperative surgical feedback for training surgeons. Although the adoption and integration of AI show promise in robotic surgery, it raises important, complex ethical questions. Frameworks for thinking through ethical dilemmas raised by AI are outlined in this review. AI enhancements in robotic surgery is some of the most groundbreaking research happening today, and the studies outlined in this review represent some of the most exciting innovations in recent years.

Competency in Robotic Surgery: Standard Setting for Robotic Suturing Using Objective Assessment and Expert Evaluation.

OBJECTIVE: Surgical skill assessment tools such as the End-to-End Assessment of Suturing Expertise (EASE) can differentiate a surgeon's experience level. In this simulation-based study, we define a competency benchmark for intraoperative robotic suturing using EASE as a validated measure of performance. DESIGN: Participants conducted a dry-lab vesicourethral anastomosis (VUA) exercise. Videos were each independently scored by 2 trained, blinded reviewers using EASE. Inter-rater reliability was measured with prevalence-adjusted bias-adjusted Kappa (PABAK) using 2 example videos. All videos were reviewed by an expert surgeon, who determined if the suturing skills exhibited were at a competency level expected for residency graduation (pass or fail). The Contrasting Group (CG) method was then used to set a pass/fail score at the intercept of the pass and fail cohorts' EASE score distributions. SETTING: Keck School of Medicine, University of Southern California. PARTICIPANTS: Twenty-six participants: 8 medical students, 8 junior residents (PGY 1-2), 7 senior residents (PGY 3-5) and 3 attending urologists. RESULTS: After 1 round of consensus-building, average PABAK across EASE subskills was 0.90 (Range 0.67-1.0). The CG method produced a competency benchmark EASE score of >35/39, with a pass rate of 10/26 (38%); 27% were deemed competent by expert evaluation. False positives and negatives were defined as medical students who passed and attendings who failed the assessment, respectively. This pass/fail score produced no false positives or negatives, and fewer JR than SR were considered competent by both the expert and CG benchmark. CONCLUSIONS: Using an absolute standard setting method, competency scores were set to identify trainees who could competently execute a standardized dry-lab robotic suturing exercise. This standard can be used for high stakes decisions regarding a trainee's technical readiness for independent practice. Future work includes validation of this standard in the clinical environment through correlation with clinical outcomes.

A Multi-institution Study on the Association of Virtual Reality Skills with Continence Recovery after Robot-assisted Radical Prostatectomy.

BACKGROUND: Virtual reality (VR) simulators are increasingly being used for surgical skills training. It is unclear what skills are best improved via VR, translate to live surgical skills, and influence patient outcomes. OBJECTIVE: To assess surgeons in VR and live surgery using a suturing assessment tool and evaluate the association between technical skills and a clinical outcome. DESIGN, SETTING, AND PARTICIPANTS: This prospective five-center study enrolled participants who completed VR suturing exercises and provided live surgical video. Graders provided skill assessments using the validated End-To-End Assessment of Suturing Expertise (EASE) suturing evaluation tool. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: A hierarchical Poisson model was used to compare skill scores among cohorts and evaluate the association of scores with clinical outcomes. Spearman's method was used to assess correlation between VR and live skills. RESULTS AND LIMITATIONS: Ten novices, ten surgeons with intermediate expertise (median 64 cases, interquartile range [IQR] 6-80), and 26 expert surgeons (median 850 cases, IQR 375-3000) participated in this study. Intermediate and expert surgeons were significantly more likely to have ideal scores in comparison to novices for the subskills needle hold angle, wrist rotation, and wrist rotation needle withdrawal (p < 0.01). For both intermediate and expert surgeons, there was positive correlation between VR and live skills for needle hold angle (p < 0.05). For expert surgeons, there was a positive association between ideal scores for VR needle hold angle and driving smoothness subskills and 3-mo continence recovery (p < 0.05). Limitations include the size of the intermediate surgeon sample and clinical data limited to expert surgeons. CONCLUSIONS: EASE can be used in VR to identify skills to improve for trainee surgeons. Technical skills that influence postoperative outcomes may be assessable in VR. PATIENT SUMMARY: This study provides insights into surgical skills that translate from virtual simulation to live surgery and that have an impact on urinary continence after robot-assisted removal of the prostate. We also highlight the usefulness of virtual reality in surgical education.

Development of a Classification System for Live Surgical Feedback.

Importance: Live feedback in the operating room is essential in surgical training. Despite the role this feedback plays in developing surgical skills, an accepted methodology to characterize the salient features of feedback has not been defined. Objective: To quantify the intraoperative feedback provided to trainees during live surgical cases and propose a standardized deconstruction for feedback. Design, Setting, and Participants: In this qualitative study using a mixed methods analysis, surgeons at a single academic tertiary care hospital were audio and video recorded in the operating room from April to October 2022. Urological residents, fellows, and faculty attending surgeons involved in robotic teaching cases during which trainees had active control of the robotic console for at least some portion of a surgery were eligible to voluntarily participate. Feedback was time stamped and transcribed verbatim. An iterative coding process was performed using recordings and transcript data until recurring themes emerged. Exposure: Feedback in audiovisual recorded surgery. Main Outcomes and Measures: The primary outcomes were the reliability and generalizability of a feedback classification system in characterizing surgical feedback. Secondary outcomes included assessing the utility of our system. Results: In 29 surgical procedures that were recorded and analyzed, 4 attending surgeons, 6 minimally invasive surgery fellows, and 5 residents (postgraduate years, 3-5) were involved. For the reliability of the system, 3 trained raters achieved moderate to substantial interrater reliability in coding cases using 5 types of triggers, 6 types of feedback, and 9 types of responses (prevalence-adjusted and bias-adjusted κ range: a 0.56 [95% CI, 0.45-0.68] minimum for triggers to a 0.99 [95% CI, 0.97-1.00] maximum for feedback and responses). For the generalizability of the system, 6 types of surgical procedures and 3711 instances of feedback were analyzed and coded with types of triggers, feedback, and responses. Significant differences in triggers, feedback, and responses reflected surgeon experience level and surgical task being performed. For example, as a response, attending surgeons took over for safety concerns more often for fellows than residents (prevalence rate ratio [RR], 3.97 [95% CI, 3.12-4.82]; P = .002), and suturing involved more errors that triggered feedback than dissection (RR, 1.65 [95% CI, 1.03-3.33]; P = .007). For the utility of the system, different combinations of trainer feedback had associations with rates of different trainee responses. For example, technical feedback with a visual component was associated with an increased rate of trainee behavioral change or verbal acknowledgment responses (RR, 1.11 [95% CI, 1.03-1.20]; P = .02). Conclusions and Relevance: These findings suggest that identifying different types of triggers, feedback, and responses may be a feasible and reliable method for classifying surgical feedback across several robotic procedures. Outcomes suggest that a system that can be generalized across surgical specialties and for trainees of different experience levels may help galvanize novel surgical education strategies.

A vision transformer for decoding surgeon activity from surgical videos.

The intraoperative activity of a surgeon has substantial impact on postoperative outcomes. However, for most surgical procedures, the details of intraoperative surgical actions, which can vary widely, are not well understood. Here we report a machine learning system leveraging a vision transformer and supervised contrastive learning for the decoding of elements of intraoperative surgical activity from videos commonly collected during robotic surgeries. The system accurately identified surgical steps, actions performed by the surgeon, the quality of these actions and the relative contribution of individual video frames to the decoding of the actions. Through extensive testing on data from three different hospitals located in two different continents, we show that the system generalizes across videos, surgeons, hospitals and surgical procedures, and that it can provide information on surgical gestures and skills from unannotated videos. Decoding intraoperative activity via accurate machine learning systems could be used to provide surgeons with feedback on their operating skills, and may allow for the identification of optimal surgical behaviour and for the study of relationships between intraoperative factors and postoperative outcomes.

Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer.

PURPOSE OF REVIEW: This review aims to explore the current state of research on the use of artificial intelligence (AI) in the management of prostate cancer. We examine the various applications of AI in prostate cancer, including image analysis, prediction of treatment outcomes, and patient stratification. Additionally, the review will evaluate the current limitations and challenges faced in the implementation of AI in prostate cancer management. RECENT FINDINGS: Recent literature has focused particularly on the use of AI in radiomics, pathomics, the evaluation of surgical skills, and patient outcomes. AI has the potential to revolutionize the future of prostate cancer management by improving diagnostic accuracy, treatment planning, and patient outcomes. Studies have shown improved accuracy and efficiency of AI models in the detection and treatment of prostate cancer, but further research is needed to understand its full potential as well as limitations.

Assessing the efficacy of dissection gestures in robotic surgery.

Our group previously defined a dissection gesture classification system that deconstructs robotic tissue dissection into its most elemental yet meaningful movements. The purpose of this study was to expand upon this framework by adding an assessment of gesture efficacy (ineffective, effective, or erroneous) and analyze dissection patterns between groups of surgeons of varying experience. We defined three possible gesture efficacies as ineffective (no meaningful effect on the tissue), effective (intended effect on the tissue), and erroneous (unintended disruption of the tissue). Novices (0 prior robotic cases), intermediates (1-99 cases), and experts (≥ 100 cases) completed a robotic dissection task in a dry-lab training environment. Video recordings were reviewed to classify each gesture and determine its efficacy, then dissection patterns between groups were analyzed. 23 participants completed the task, with 9 novices, 8 intermediates with median caseload 60 (IQR 41-80), and 6 experts with median caseload 525 (IQR 413-900). For gesture selection, we found increasing experience associated with increasing proportion of overall dissection gestures (p = 0.009) and decreasing proportion of retraction gestures (p = 0.009). For gesture efficacy, novices performed the greatest proportion of ineffective gestures (9.8%, p < 0.001), intermediates commit the greatest proportion of erroneous gestures (26.8%, p < 0.001), and the three groups performed similar proportions of overall effective gestures, though experts performed the greatest proportion of effective retraction gestures (85.6%, p < 0.001). Between groups of experience, we found significant differences in gesture selection and gesture efficacy. These relationships may provide insight into further improving surgical training.

Surgical gestures as a method to quantify surgical performance and predict patient outcomes.

How well a surgery is performed impacts a patient's outcomes; however, objective quantification of performance remains an unsolved challenge. Deconstructing a procedure into discrete instrument-tissue "gestures" is a emerging way to understand surgery. To establish this paradigm in a procedure where performance is the most important factor for patient outcomes, we identify 34,323 individual gestures performed in 80 nerve-sparing robot-assisted radical prostatectomies from two international medical centers. Gestures are classified into nine distinct dissection gestures (e.g., hot cut) and four supporting gestures (e.g., retraction). Our primary outcome is to identify factors impacting a patient's 1-year erectile function (EF) recovery after radical prostatectomy. We find that less use of hot cut and more use of peel/push are statistically associated with better chance of 1-year EF recovery. Our results also show interactions between surgeon experience and gesture types-similar gesture selection resulted in different EF recovery rates dependent on surgeon experience. To further validate this framework, two teams independently constructe distinct machine learning models using gesture sequences vs. traditional clinical features to predict 1-year EF. In both models, gesture sequences are able to better predict 1-year EF (Team 1: AUC 0.77, 95% CI 0.73-0.81; Team 2: AUC 0.68, 95% CI 0.66-0.70) than traditional clinical features (Team 1: AUC 0.69, 95% CI 0.65-0.73; Team 2: AUC 0.65, 95% CI 0.62-0.68). Our results suggest that gestures provide a granular method to objectively indicate surgical performance and outcomes. Application of this methodology to other surgeries may lead to discoveries on methods to improve surgery.

Association of Suturing Technical Skill Assessment Scores Between Virtual Reality Simulation and Live Surgery.

Introduction: Robotic surgical performance, in particular suturing, has been linked to postoperative clinical outcomes. Before attempting live surgery, virtual reality (VR) simulators afford opportunities for training surgeons to learn fundamental technical skills. Herein, we evaluate the association of suturing technical skill assessments between VR simulation and live surgery, and functional clinical outcomes. Materials and Methods: Twenty surgeons completed a VR suturing exercise on the Mimic™ Flex VR simulator and the anterior vesicourethral anastomosis during robot-assisted radical prostatectomy (RARP). Three independent and blinded graders provided technical skill scores using a validated assessment tool. Correlations between VR and live scores were assessed by Spearman's correlation coefficients (ρ). In addition, 117 historic RARP cases from participating surgeons were extracted, and the association between VR technical skill scores and urinary continence recovery was assessed by a multilevel mixed-effects model. Results: A total of 20 (6 training and 14 expert) surgeons participated. Statistically significant correlations for scores provided between VR simulation and live surgery were found for overall and needle driving scores (ρ = 0.555, p = 0.011; ρ = 0.570, p = 0.009, respectively). A subanalysis performed on training surgeons found significant correlations for overall scores between VR simulation and live surgery (ρ = 0.828, p = 0.042). Expert cases with high VR needle driving scores had significantly greater continence recovery rates at 24 months after RARP (98.5% vs 84.9%, p = 0.028). Conclusions: Our study found significant correlations in technical scores between VR and live surgery, especially among training surgeons. In addition, we found that VR needle driving scores were associated with continence recovery after RARP. Our data support the association of skill assessments between VR simulation and live surgery and potential implications for clinical outcomes.

Prognostic Value of Tumor Size in Patients with Upper Tract Urothelial Carcinoma: A Systematic Review and Meta-analysis.

Context: The role of tumor size in predicting prognosis in upper tract urothelial carcinoma (UTUC) patients remains poorly defined. Objective: To assess the prognostic value of tumor size in patients with UTUC through a systematic review and meta-analysis. Evidence acquisition: A comprehensive literature search of the PubMed and Embase databases were performed to identify all relevant articles published up to December 2021 according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. Available hazard ratios (HRs) and corresponding 95% confidence intervals (95% CIs) were analyzed to evaluate the association between tumor size and survival outcomes. Evidence synthesis: A total of 35 articles representing 32 292 patients met the eligibility criteria and were finally included for the meta-analysis. Tumor size was significantly associated with poor outcomes in terms of overall survival (HR = 1.42, 95% CI = 1.28-1.58), cancer-specific survival (HR = 1.66, 95% CI = 1.47-1.88), recurrence-free survival (HR = 1.25, 95% CI = 1.13-1.38), and intravesical recurrence (HR = 1.12, 95% CI = 1.04-1.20). There was between-study heterogeneity in the effect of tumor size on all these meta-analyses, with p < 0.10 and I2 generally >50%. Subgroup analyses illustrated that the association of tumor size with adverse prognosis in UTUC patients is not affected by treatment modalities. Segmental resection of ureter, whether receiving lymph node dissection, cutoff of tumor size, and region of population were potential sources of heterogeneity. The funnel plot test indicated no significant publication bias in the meta-analysis of survival outcomes. Conclusions: This study shows that larger tumor size is associated with an increased risk of overall and cancer-specific mortality, and disease recurrence in UTUC. Integration of tumor size with other prognostic indicators may help in risk stratification and individualized treatment of UTUC. Patient summary: Through a systematic review and meta-analysis, this study found that larger tumor size is associated with an increased risk of overall and cancer-specific mortality, and disease recurrence in patients with upper tract urothelial carcinoma.

Tailored Feedback Based on Clinically Relevant Performance Metrics Expedites the Acquisition of Robotic Suturing Skills-An Unblinded Pilot Randomized Controlled Trial.

PURPOSE: Previously, we identified 8 objective suturing performance metrics highly predictive of urinary continence recovery after robotic-assisted radical prostatectomy. Here, we aimed to test the feasibility of providing tailored feedback based upon these clinically relevant metrics and explore the impact on the acquisition of robotic suturing skills. MATERIALS AND METHODS: Training surgeons were recruited and randomized to a feedback group or a control group. Both groups completed a baseline, midterm and final dry laboratory vesicourethral anastomosis (VUA) and underwent 4 intervening training sessions each, consisting of 3 suturing exercises. Eight performance metrics were recorded during each exercise: 4 automated performance metrics (derived from kinematic and system events data of the da Vinci® Robotic System) representing efficiency and console manipulation competency, and 4 suturing technical skill scores. The feedback group received tailored feedback (a visual diagram+verbal instructions+video examples) based on these metrics after each session. Generalized linear mixed model was used to compare metric improvement (Δ) from baseline to the midterm and final VUA. RESULTS: Twenty-three participants were randomized to the feedback group (11) or the control group (12). Demographic data and baseline VUA metrics were comparable between groups. The feedback group showed greater improvement than the control group in aggregate suturing scores at midterm (mean Δ feedback group 4.5 vs Δ control group 1.1) and final VUA (Δ feedback group 5.3 vs Δ control group 4.9). The feedback group also showed greater improvement in the majority of the included metrics at midterm and final VUA. CONCLUSIONS: Tailored feedback based on specific, clinically relevant performance metrics is feasible and may expedite the acquisition of robotic suturing skills.

Machine Learning to Delineate Surgeon and Clinical Factors That Anticipate Positive Surgical Margins After Robot-Assisted Radical Prostatectomy.

Purpose: Automated performance metrics (APMs), derived from instrument kinematic and systems events data during robotic surgery, are validated objective measures of surgeon performance. Our previous studies showed that APMs are strong outcome predictors of urinary continence after robot-assisted radical prostatectomy (RARP). We now use machine learning to investigate how surgeon performance (i.e., APMs) and clinical factors can predict positive surgical margins (PSMs) after RARP. Methods: We prospectively collected data of patients undergoing RARP at our institution from 2016 to 2019. Random Forest model predicted PSMs based on 15 clinical factors and 38 APMs from 11 standardized RARP steps. Out-of-bag Gini impurity index determined the top 10 variables of importance (VOI). APMs in the top 10 VOI were assessed for confounding effects by extracapsular extension (ECE) and pathologic T (pT) through Poisson regression with Generalized Estimating Equation. Results: 55/236 (23.3%) cases had PSMs. Of the 55 cases with PSMs, 9 (16.4%) were pT2 and 46 (83.6%), pT3. The full model, including clinical factors and APMs, achieved area under the curve (AUC) 0.74. When assessing clinical factors or APMs alone, the model achieved AUC 0.72 and 0.64, respectively. The strongest PSM predictors were ECE and pT stage, followed by APMs in specific steps. After adjusting for ECE and pT stage, most APMs remained as independent predictors of PSM. Conclusion: Using machine learning methods, we found that the strongest predictors of PSMs after RARP are nonmodifiable, disease-driven factors (ECE and pT). While APMs provide minimal additional insight into when PSMs may occur, they are nonetheless capable of independently predicting PSMs based on objective measures of surgeon performance.

Survival Analysis Using Surgeon Skill Metrics and Patient Factors to Predict Urinary Continence Recovery After Robot-assisted Radical Prostatectomy.

BACKGROUND: It has been shown that metrics recorded for instrument kinematics during robotic surgery can predict urinary continence outcomes. OBJECTIVE: To evaluate the contributions of patient and treatment factors, surgeon efficiency metrics, and surgeon technical skill scores, especially for vesicourethral anastomosis (VUA), to models predicting urinary continence recovery following robot-assisted radical prostatectomy (RARP). DESIGN, SETTING, AND PARTICIPANTS: Automated performance metrics (APMs; instrument kinematics and system events) and patient data were collected for RARPs performed from July 2016 to December 2017. Robotic Anastomosis Competency Evaluation (RACE) scores during VUA were manually evaluated. Training datasets included: (1) patient factors; (2) summarized APMs (reported over RARP steps); (3) detailed APMs (reported over suturing phases of VUA); and (4) technical skills (RACE). Feature selection was used to compress the dimensionality of the inputs. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The study outcome was urinary continence recovery, defined as use of 0 or 1 safety pads per day. Two predictive models (Cox proportional hazards [CoxPH] and deep learning survival analysis [DeepSurv]) were used. RESULTS AND LIMITATIONS: Of 115 patients undergoing RARP, 89 (77.4%) recovered their urinary continence and the median recovery time was 166 d (interquartile range [IQR] 82-337). VUAs were performed by 23 surgeons. The median RACE score was 28/30 (IQR 27-29). Among the individual datasets, technical skills (RACE) produced the best models (C index: CoxPH 0.695, DeepSurv: 0.708). Among summary APMs, posterior/anterior VUA yielded superior model performance over other RARP steps (C index 0.543-0.592). Among detailed APMs, metrics for needle driving yielded top-performing models (C index 0.614-0.655) over other suturing phases. DeepSurv models consistently outperformed CoxPH; both approaches performed best when provided with all the datasets. Limitations include feature selection, which may have excluded relevant information but prevented overfitting. CONCLUSIONS: Technical skills and "needle driving" APMs during VUA were most contributory. The best-performing model used synergistic data from all datasets. PATIENT SUMMARY: One of the steps in robot-assisted surgical removal of the prostate involves joining the bladder to the urethra. Detailed information on surgeon performance for this step improved the accuracy of predicting recovery of urinary continence among men undergoing this operation for prostate cancer.

Technical Skill Impacts the Success of Sequential Robotic Suturing Substeps.

Introduction: Robotic surgical performance, in particular suturing, has been associated with postoperative clinical outcomes. Suturing can be deconstructed into substep components (needle positioning, needle entry angle, needle driving, and needle withdrawal) allowing for the provision of more specific feedback while teaching suturing and more precision when evaluating suturing technical skill and prediction of clinical outcomes. This study evaluates if the technical skill required for particular substeps of the suturing process is associated with the execution of subsequent substeps in terms of technical skill, accuracy, and efficiency. Materials and Methods: Training and expert surgeons completed standardized sutures on the Mimic™ Flex virtual reality robotic simulator. Video recordings were deidentified, time annotated, and provided technical skill scores for each of the four suturing substeps. Hierarchical Poisson regression with generalized estimating equation was used to examine the association of technical skill rating categories between substeps. Results: Twenty-two surgeons completed 428 suturing attempts with 1669 individual technical skill assessments made. Technical skill scores between substeps of the suturing process were found to be significantly associated. When needle positioning was ideal, needle entry angle was associated with a significantly greater chance of being ideal (risk ratio [RR] = 1.12, p = 0.05). In addition, ideal needle entry angle and needle driving technical skill scores were each significantly associated with ideal needle withdrawal technical skill scores (RR = 1.27, p = 0.03; RR = 1.3, p = 0.03, respectively). Our study determined that ideal technical skill was associated with increased accuracy and efficiency of select substeps. Conclusions: Our study found significant associations in the technical skill required for completing substeps of suturing, demonstrating inter-relationships within the suturing process. Together with the known association between technical skill and clinical outcomes, training surgeons should focus on mastering not just the overall suturing process, but also each substep involved. Future machine learning efforts can better evaluate suturing, knowing that these inter-relationships exist.

The Relationship Between Technical Skills, Cognitive Workload, and Errors During Robotic Surgical Exercises.

Purpose: We attempt to understand the relationship between surgeon technical skills, cognitive workload, and errors during a simulated robotic dissection task. Materials and Methods: Participant surgeons performed a robotic surgery dissection exercise. Participants were grouped based on surgical experience. Technical skills were evaluated utilizing the validated Global Evaluative Assessment of Robotic Skills (GEARS) assessment tool. The dissection task was evaluated for errors during active dissection or passive retraction maneuvers. We quantified cognitive workload of surgeon participants as an index of cognitive activity (ICA), derived from task-evoked pupillary response metrics; ICA ranged 0 to 1, with 1 representing maximum ICA. Generalized estimating equation (GEE) was used for all modelings to establish relationships between surgeon technical skills, cognitive workload, and errors. Results: We found a strong association between technical skills as measured by multiple GEARS domains (depth perception, force sensitivity, and robotic control) and passive errors, with higher GEARS scores associated with a lower relative risk of errors (all p < 0.01). For novice surgeons, as average GEARS scores increased, the average estimated ICA decreased. In contrast, as average GEARS increased for expert surgeons, the average estimated ICA increased. When exhibiting optimal technical skill (maximal GEARS scores), novices and experts reached a similar range of ICA scores (ICA: 0.47 and 0.42, respectively). Conclusions: This study found that there is an optimal cognitive workload level for surgeons of all experience levels during our robotic surgical exercise. Select technical skill domains were strong predictors of errors. Future research will explore whether an ideal cognitive workload range truly optimizes surgical training and reduces surgical errors.

Bilateral Nephroureterectomy Versus Unilateral Nephroureterectomy for Treating De Novo Upper Tract Urothelial Carcinoma After Renal Transplantation: A Comparison of Surgical and Oncological outcomes.

BACKGROUND: There is currently no consensus on the optimal management of de novo unilateral upper tract urothelial carcinoma (UTUC) in renal transplant recipients. We aimed to compare the surgical and oncological outcomes of simultaneous bilateral radical nephroureterectomy (SBRNU) and unilateral radical nephroureterectomy (URNU) to determine the appropriate surgical method. METHODS: Patients who developed de novo UTUC after renal transplantation and underwent surgical treatment at our center were included in the study. Outcomes were compared between the SBRNU group (underwent bilateral RNU within 3 months) and the URNU group using the Mann-Whitney U-test for continuous variables, Pearson's chi-square test for categorical variables, and the log-rank test for survival data. RESULTS: A total of 48 patients were identified, including 21 and 27 patients in the SBRNU and URNU groups, respectively. Comparison of perioperative data showed that the SBRNU group had a significantly longer operative time (P < .001) and hospital stay (P = .040) than the URNU group but no statistically significant difference in the blood loss (P = .171) and morbidity rate (P = .798). After a median follow-up of 65 months, the SBRNU group had a significantly longer disease-free survival (P = .009), longer cancer-specific survival (P = .032), marginally longer overall survival (P = .066), and similar intravesical recurrence-free survival (P = .274) than the URNU group. CONCLUSIONS: Our data suggest that SBRNU contributes to improved survival without significantly compromising the perioperative outcomes compared with URNU. SBRNU can be considered a feasible option for de novo UTUC after renal transplantation in specialized centers. Prospective studies should be conducted to further explore the best treatment options for this group of patients.

Virtual Reality vs Dry Laboratory Models: Comparing Automated Performance Metrics and Cognitive Workload During Robotic Simulation Training.

Background: This study compares surgical performance during analogous vesico-urethral anastomosis (VUA) tasks in two robotic training environments, virtual reality (VR) and dry laboratory (DL), to investigate transferability of skill assessment across the two platforms. Utilizing computer-generated performance metrics and pupillary data, we evaluated the two environments to distinguish surgical expertise and ultimately whether performance in the VR simulation correlates with performance in live robotic surgery in the DL. Materials and Methods: Experts (≥300 cases) and trainees (<300 cases) performed analogous VUAs during VR and DL sessions on a da Vinci robotic console following an Institutional Review Board (IRB) approved protocol (HS-16-00318). Twenty-two metrics were generated in each environment (kinematic metrics, tissue metrics, and biometrics). The DL included 18 previously validated automated performance metrics (APMs) (kinematics and event metrics) captured by an Intuitive system data recorder. In both settings, Tobii Pro Glasses 2 recorded the task-evoked pupillary response (reported as Index of Cognitive Activity [ICA]) to indicate cognitive workload, analyzed by EyeTracking cognitive workload software. Pearson correlation, Mann-Whitney, and independent t-tests were used for the comparative analyses. Results: Our study included six experts (median caseload 1300 [interquartile range 400-3000]) and 11 trainees (25 [0-250]). A total of 8/9 metrics directly comparable between VR and DL showed significant positive correlation (r ≥ 0.554, p ≤ 0.032); 5/22 VR metrics distinguished expertise, including task time (p = 0.031), clutch usage (p = 0.040), unnecessary needle piercing (p = 0.026), and suspected injury to the endopelvic fascia (p = 0.040). This contrasts with 14/22 APMs in DL (p ≤ 0.038), including linear velocities of all three instruments (p ≤ 0.038) and dominant-hand instrument wrist articulation (p = 0.013). Trainees experienced higher cognitive workload (ICA) in both environments when compared with experts (p < 0.036). Conclusions: Most performance metrics between VR and DL exhibited moderate to strong correlations, showing transferability of skills across the platforms. Comparing training environments, APMs during DL tasks are better able to distinguish expertise than VR-generated metrics.

Surgeon Automated Performance Metrics as Predictors of Early Urinary Continence Recovery After Robotic Radical Prostatectomy-A Prospective Bi-institutional Study.

BACKGROUND: During robotic surgeries, kinematic metrics objectively quantify surgeon performance. OBJECTIVE: To determine whether clinical factors confound the ability of surgeon performance metrics to anticipate urinary continence recovery after robot-assisted radical prostatectomies (RARPs). DESIGN SETTING AND PARTICIPANTS: Clinical data (patient characteristics, continence recovery, and treatment factors) and surgeon data from RARPs performed between July 2016 and November 2018 were prospectively collected. Surgeon data included 40 automated performance metrics (APMs) derived from robot systems (instrument kinematics and events) and summarized over each standardized RARP step. The data were collected from two high-volume robotic centers in the USA and Germany. Surgeons from both institutions performed RARPs. The inclusion criteria were consecutive RARPs having both clinical and surgeon data. INTERVENTION: RARP with curative intent to treat prostate cancer. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The outcome was 3- and 6-mo urinary continence recovery status. Continence was defined as the use of zero or one safety pad per day. Random forest (SAS HPFOREST) was utilized. RESULTS AND LIMITATIONS: A total of 193 RARPs performed by 20 surgeons were included. Of the patients, 56.7% (102/180) and 73.3% (129/176) achieved urinary continence by 3 and 6 mo after RARP, respectively. The model anticipated continence recovery (area under the curve = 0.74, 95% confidence interval [CI] 0.66-0.81 for 3-mo, and area under the curve = 0.67, 95% CI 0.58-0.76 for 6 mo). Clinical factors, including pT stage, confounded APMs during prediction of continence recovery at 3 mo after RARP (Δß median -13.3%, interquartile range [-28.2% to -6.5%]). After adjusting for clinical factors, 11/20 (55%) top-ranking APMs remained significant and independent predictors (ie, velocity and wrist articulation during the vesicourethral anastomosis). Limitations included heterogeneity of surgeon/patient data between institutions, although it was accounted for during multivariate analysis. CONCLUSIONS: Clinical factors confound surgeon performance metrics during the prediction of urinary continence recovery after RARP. Nonetheless, many surgeon factors are still independent predictors of early continence recovery. PATIENT SUMMARY: Both patient factors and surgeon kinematic metrics, recorded during robotic prostatectomies, impact early urinary continence recovery after robot-assisted radical prostatectomy.