In-depth critical analysis of complications following robot-assisted radical cystectomy with intracorporeal urinary diversion

Background: Robot-assisted radical cystectomy with intracorporeal urinary diversion (iRARC) is an attractive option to open cystectomy, but the benefit in terms of improved outcomes is not established. Objective: To evaluate the early postoperative morbidity and mortality of patients undergoing iRARC and conduct a critical analysis of complications using standardised reporting criteria as stratified according to urinary diversion. Design, setting, and participants: A total of 134 patients underwent iRARC for bladder cancer at a single centre between June 2011 and July 2015. Intervention: Radical cystectomy with iRARC. Outcome measurements and statistical analysis: Patient demographics, pathologic data, and 90-d perioperative mortality and complications were recorded. Complications were reported according to the Clavien-Dindo (CD) classification and stratified according to urinary diversion type and either surgical or medical complications. The chi-square test and t test were used for categorical and continuous variables respectively. Multivariable logistic regression was performed on variables with significance in univariate analysis. Results and limitations: The 90-d all complication rate following ileal conduit and continent diversion was 68% and 82.4%, and major complications were 21.0% and 20.6% respectively. The 90-d mortality was 3% and 2.9% for ileal conduit and continent diversion patients, respectively. On multivariate analysis, the blood transfusion requirement was independently associated with major complications (p = 0.002) and all 30-d (p = 0.002) and 90-d (p = 0.012) major complications. Male patients were associated with 90-d major complications (p = 0.015). Critical analysis identified that surgical complications were responsible for 39.4% of all 90-d major complications. The incidence of surgical complications did not decline with increasing number of iRARC cases performed (p = 0.742, r = 0.31). Limitations of this study include its retrospective nature, limited sample size, and limited multivariate analysis due to the low number of major complications events. Conclusions: Although complications following iRARC are common, most are low grade. A critical analysis identified surgical complications as a cause of major complications. Addressing this issue could have a significant impact on lowering the morbidity associated with iRARC. Patient summary: We looked at the surgical outcomes in bladder cancer patients treated with minimally invasive robotic surgery. We found that surgical complications account for most major complications and previous surgical experience may be a confounding factor when interpreting results from a different centre even in a randomised trial setting

Benefits of robotic cystectomy with intracorporeal diversion for patients with low cardiorespiratory fitness: a prospective cohort study

Background: Patients undergoing radical cystectomy have associated comorbidities resulting in reduced cardiorespiratory fitness. Preoperative cardiopulmonary exercise testing (CPET) measures including anaerobic threshold (AT) can predict major adverse events (MAE) and hospital length of stay (LOS) for patients undergoing open and robotic cystectomy with extracorporeal diversion. Our objective was to determine the relationship between CPET measures and outcome in patients undergoing robotic radical cystectomy and intracorporeal diversion (intracorporeal robotic assisted radical cystectomy [iRARC]). Methods: A single institution prospective cohort study in patients undergoing iRARC for muscle invasive and high-grade bladder cancer. Inclusion: patients undergoing standardised CPET before iRARC. Exclusions: patients not consenting to data collection. Data on CPET measures (AT, ventilatory equivalent for carbon dioxide [VE/VCO2] at AT, peak oxygen uptake [VO2]), and patient demographics prospectively collected. Outcome measurements included hospital LOS; 30-day MAE and 90-day mortality data, which were prospectively recorded. Descriptive and regression analyses were used to assess whether CPET measures were associated with or predicted outcomes. Results: From June 2011 to March 2015, 128 patients underwent radical cystectomy (open cystectomy, n = 17; iRARC, n = 111). A total of 82 patients who underwent iRARC and CPET and consented to participation were included. Median (interquartile range): age = 65 (58–73); body mass index = 27 (23–30); AT = 10.0 (9–11), Peak VO2 = 15.0 (13–18.5), VE/VCO2 (AT) = 33.0 (30–38). 30-day MAE = 14/111 (12.6%): death = 2, multiorgan failure = 2, abscess = 2, gastrointestinal = 2, renal = 6; 90-day mortality = 3/111 (2.7%). AT, peak VO2, and VE/VCO2 (at AT) were not significant predictors of 30-day MAE or LOS. The results are limited by the absence of control group undergoing open surgery. Conclusions: Poor cardiorespiratory fitness does not predict increased hospital LOS or MAEs in patients undergoing iRARC. Overall, MAE and LOS comparable with other series

Bayesian Support Vector Regression for Traffic Speed Prediction with Error Bars

Abstract-Traffic prediction algorithms can help improve the performance of Intelligent Transportation Systems (ITS). To this end, ITS require algorithms with high prediction accuracy. For more robust performance, the traffic systems also require a measure of uncertainty associated with prediction data. Data driven algorithms such as Support Vector Regression (SVR) perform traffic prediction with overall high accuracy. However, they do not provide any information about the associated uncertainty. The prediction error can only be calculated once field data becomes available. Consequently, the applications which use prediction data, remain vulnerable to variations in prediction error. To overcome this issue, we propose Bayesian Support Vector Regression (BSVR). BSVR provides error bars along with the predicted traffic states. We perform sensitivity and specificity analysis to evaluate the efficiency of BSVR in anticipating variations in prediction error. We perform multi-horizon prediction and analyze the performance of BSVR for expressways as well as general road segments

Evaluation of objective tools and artificial intelligence in robotic surgery technical skills assessment: a systematic review

BACKGROUND: There is a need to standardize training in robotic surgery, including objective assessment for accreditation. This systematic review aimed to identify objective tools for technical skills assessment, providing evaluation statuses to guide research and inform implementation into training curricula. METHODS: A systematic literature search was conducted in accordance with the PRISMA guidelines. Ovid Embase/Medline, PubMed and Web of Science were searched. Inclusion criterion: robotic surgery technical skills tools. Exclusion criteria: non-technical, laparoscopy or open skills only. Manual tools and automated performance metrics (APMs) were analysed using Messick's concept of validity and the Oxford Centre of Evidence-Based Medicine (OCEBM) Levels of Evidence and Recommendation (LoR). A bespoke tool analysed artificial intelligence (AI) studies. The Modified Downs-Black checklist was used to assess risk of bias. RESULTS: Two hundred and forty-seven studies were analysed, identifying: 8 global rating scales, 26 procedure-/task-specific tools, 3 main error-based methods, 10 simulators, 28 studies analysing APMs and 53 AI studies. Global Evaluative Assessment of Robotic Skills and the da Vinci Skills Simulator were the most evaluated tools at LoR 1 (OCEBM). Three procedure-specific tools, 3 error-based methods and 1 non-simulator APMs reached LoR 2. AI models estimated outcomes (skill or clinical), demonstrating superior accuracy rates in the laboratory with 60 per cent of methods reporting accuracies over 90 per cent, compared to real surgery ranging from 67 to 100 per cent. CONCLUSIONS: Manual and automated assessment tools for robotic surgery are not well validated and require further evaluation before use in accreditation processes.PROSPERO: registration ID CRD42022304901

Virtual classroom proficiency-based progression for robotic surgery training (VROBOT): a randomised, prospective, cross-over, effectiveness study

Robotic surgery training has lacked evidence-based standardisation. We aimed to determine the effectiveness of adjunctive interactive virtual classroom training (VCT) in concordance with the self-directed Fundamentals of Robotic Surgery (FRS) curriculum. The virtual classroom is comprised of a studio with multiple audio-visual inputs to which participants can connect remotely via the BARCO weConnect platform. Eleven novice surgical trainees were randomly allocated to two training groups (A and B). In week 1, both groups completed a robotic skills induction. In week 2, Group A received training with the FRS curriculum and adjunctive VCT; Group B only received access to the FRS curriculum. In week 3, the groups received the alternate intervention. The primary outcome was measured using the validated robotic-objective structured assessment of technical skills (R-OSAT) at the end of week 2 (time-point 1) and 3 (time-point 2). All participants completed the training curriculum and were included in the final analyses. At time-point 1, Group A achieved a statistically significant greater mean proficiency score compared to Group B (44.80 vs 35.33 points, p = 0.006). At time-point 2, there was no significant difference in mean proficiency score in Group A from time-point 1. In contrast, Group B, who received further adjunctive VCT showed significant improvement in mean proficiency by 9.67 points from time-point 1 (95% CI 5.18-14.15, p = 0.003). VCT is an effective, accessible training adjunct to self-directed robotic skills training. With the steep learning curve in robotic surgery training, VCT offers interactive, expert-led learning and can increase training effectiveness and accessibility

Evolving robotic surgery training and improving patient safety, with the integration of novel technologies.

Funder: University College London (UCL)INTRODUCTION: Robot-assisted surgery is becoming increasingly adopted by multiple surgical specialties. There is evidence of inherent risks of utilising new technologies that are unfamiliar early in the learning curve. The development of standardised and validated training programmes is crucial to deliver safe introduction. In this review, we aim to evaluate the current evidence and opportunities to integrate novel technologies into modern digitalised robotic training curricula. METHODS: A systematic literature review of the current evidence for novel technologies in surgical training was conducted online and relevant publications and information were identified. Evaluation was made on how these technologies could further enable digitalisation of training. RESULTS: Overall, the quality of available studies was found to be low with current available evidence consisting largely of expert opinion, consensus statements and small qualitative studies. The review identified that there are several novel technologies already being utilised in robotic surgery training. There is also a trend towards standardised validated robotic training curricula. Currently, the majority of the validated curricula do not incorporate novel technologies and training is delivered with more traditional methods that includes centralisation of training services with wet laboratories that have access to cadavers and dedicated training robots. CONCLUSIONS: Improvements to training standards and understanding performance data have good potential to significantly lower complications in patients. Digitalisation automates data collection and brings data together for analysis. Machine learning has potential to develop automated performance feedback for trainees. Digitalised training aims to build on the current gold standards and to further improve the 'continuum of training' by integrating PBP training, 3D-printed models, telementoring, telemetry and machine learning

Morbidity and mortality after robot-assisted radical cystectomy with intracorporeal urinary diversion in octogenarians: results from the European Association of Urology Robotic Urology Section Scientific Working Group

OBJECTIVES: To evaluate the postoperative complication and mortality rate following laparoscopic radical cystectomy (RARC) with intracorporeal urinary diversion (ICUD) in octogenarians. PATIENTS AND METHODS: We conducted a retrospective analysis comparing postoperative complication and mortality rates depending on age in a consecutive series of 1890 patients who underwent RARC with ICUD for bladder cancer between 2004 and 2018 in 10 European centres. Outcomes of patients aged <80 years and those aged ≥80 years were compared with regard to postoperative complications (Clavien–Dindo grading) and mortality rate. Cancer-specific mortality (CSM) and other-cause mortality (OCM) after surgery were calculated using the non-parametric Aalen-Johansen estimator. RESULTS: A total of 1726 patients aged <80 years and 164 aged ≥80 years were included in the analysis. The 30- and 90-day rate for high-grade (Clavien–Dindo grades III–V) complications were 15% and 21% for patients aged <80 years compared to 11% and 13% for patients aged ≥80 years (P = 0.2 and P = 0.03), respectively. In a multivariable logistic regression analysis adjusting for pre- and postoperative variables, age ≥80 years was not an independent predictor of high-grade complications (odds ratio 0.6, 95% confidence interval 0.3–1.1; P = 0.12). The non-cancer-related 90-day mortality was 2.3% for patients aged ≥80 years and 1.8% for those aged <80 years, respectively (P = 0.7). The estimated 12-month CSM and OCM rates for those aged <80 years were 8% and 3%, and for those aged ≥80 years, 15% and 8%, respectively (P = 0.009 and P < 0.001). CONCLUSIONS: The minimally invasive approach to RARC with ICUD for bladder cancer in well-selected elderly patients (aged ≥80 years) achieved a tolerable high-grade complication rate; the 90-day postoperative mortality rate was driven by cancer progression and the non-cancer-related rate was equivalent to that of patients aged <80 years. However, an increased OCM rate in this elderly group after the first year should be taken into account. These results will support clinicians and patients when balancing cancer-related vs treatment-related risks and benefits

Characterisation of Inactivation Domains and Evolutionary Strata in Human X Chromosome through Markov Segmentation

Markov segmentation is a method of identifying compositionally different subsequences in a given symbolic sequence. We have applied this technique to the DNA sequence of the human X chromosome to analyze its compositional structure. The human X chromosome is known to have acquired DNA through distinct evolutionary events and is believed to be composed of five evolutionary strata. In addition, in female mammals all copies of X chromosome in excess of one are transcriptionally inactivated. The location of a gene is correlated with its ability to undergo inactivation, but correlations between evolutionary strata and inactivation domains are less clear. Our analysis provides an accurate estimate of the location of stratum boundaries and gives a high–resolution map of compositionally different regions on the X chromosome. This leads to the identification of a novel stratum, as well as segments wherein a group of genes either undergo inactivation or escape inactivation in toto. We identify oligomers that appear to be unique to inactivation domains alone