Effect of Robot-Assisted Radical Cystectomy With Intracorporeal Urinary Diversion vs Open Radical Cystectomy on 90-Day Morbidity and Mortality Among Patients With Bladder Cancer: A Randomized Clinical Trial.

Importance: Robot-assisted radical cystectomy is being performed with increasing frequency, but it is unclear whether total intracorporeal surgery improves recovery compared with open radical cystectomy for bladder cancer. Objectives: To compare recovery and morbidity after robot-assisted radical cystectomy with intracorporeal reconstruction vs open radical cystectomy. Design, Setting, and Participants: Randomized clinical trial of patients with nonmetastatic bladder cancer recruited at 9 sites in the UK, from March 2017-March 2020. Follow-up was conducted at 90 days, 6 months, and 12 months, with final follow-up on September 23, 2021. Interventions: Participants were randomized to receive robot-assisted radical cystectomy with intracorporeal reconstruction (n = 169) or open radical cystectomy (n = 169). Main Outcomes and Measures: The primary outcome was the number of days alive and out of the hospital within 90 days of surgery. There were 20 secondary outcomes, including complications, quality of life, disability, stamina, activity levels, and survival. Analyses were adjusted for the type of diversion and center. Results: Among 338 randomized participants, 317 underwent radical cystectomy (mean age, 69 years; 67 women [21%]; 107 [34%] received neoadjuvant chemotherapy; 282 [89%] underwent ileal conduit reconstruction); the primary outcome was analyzed in 305 (96%). The median number of days alive and out of the hospital within 90 days of surgery was 82 (IQR, 76-84) for patients undergoing robotic surgery vs 80 (IQR, 72-83) for open surgery (adjusted difference, 2.2 days [95% CI, 0.50-3.85]; P = .01). Thromboembolic complications (1.9% vs 8.3%; difference, -6.5% [95% CI, -11.4% to -1.4%]) and wound complications (5.6% vs 16.0%; difference, -11.7% [95% CI, -18.6% to -4.6%]) were less common with robotic surgery than open surgery. Participants undergoing open surgery reported worse quality of life vs robotic surgery at 5 weeks (difference in mean European Quality of Life 5-Dimension, 5-Level instrument scores, -0.07 [95% CI, -0.11 to -0.03]; P = .003) and greater disability at 5 weeks (difference in World Health Organization Disability Assessment Schedule 2.0 scores, 0.48 [95% CI, 0.15-0.73]; P = .003) and at 12 weeks (difference in WHODAS 2.0 scores, 0.38 [95% CI, 0.09-0.68]; P = .01); the differences were not significant after 12 weeks. There were no statistically significant differences in cancer recurrence (29/161 [18%] vs 25/156 [16%] after robotic and open surgery, respectively) and overall mortality (23/161 [14.3%] vs 23/156 [14.7%]), respectively) at median follow-up of 18.4 months (IQR, 12.8-21.1). Conclusions and Relevance: Among patients with nonmetastatic bladder cancer undergoing radical cystectomy, treatment with robot-assisted radical cystectomy with intracorporeal urinary diversion vs open radical cystectomy resulted in a statistically significant increase in days alive and out of the hospital over 90 days. However, the clinical importance of these findings remains uncertain. Trial Registration: ISRCTN Identifier: ISRCTN13680280; ClinicalTrials.gov Identifier: NCT03049410.

Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach.

Background: Screening for Barrett's Oesophagus (BE) relies on endoscopy which is invasive and has a low yield. This study aimed to develop and externally validate a simple symptom and risk-factor questionnaire to screen for patients with BE. Methods: Questionnaires from 1299 patients in the BEST2 case-controlled study were analysed: 880 had BE including 40 with invasive oesophageal adenocarcinoma (OAC) and 419 were controls. This was randomly split into a training cohort of 776 patients and an internal validation cohort of 523 patients. External validation included 398 patients from the BOOST case-controlled study: 198 with BE (23 with OAC) and 200 controls. Identification of independently important diagnostic features was undertaken using machine learning techniques information gain (IG) and correlation based feature selection (CFS). Multiple classification tools were assessed to create a multi-variable risk prediction model. Internal validation was followed by external validation in the independent dataset. Findings: The BEST2 study included 40 features. Of these, 24 added IG but following CFS, only 8 demonstrated independent diagnostic value including age, gender, smoking, waist circumference, frequency of stomach pain, duration of heartburn and acid taste and taking of acid suppression medicines. Logistic regression offered the highest prediction quality with AUC (area under the receiver operator curve) of 0.87. In the internal validation set, AUC was 0.86. In the BOOST external validation set, AUC was 0.81. Interpretation: The diagnostic model offers valid predictions of diagnosis of BE in patients with symptomatic gastroesophageal reflux, assisting in identifying who should go forward to invasive testing. Overweight men who have been taking stomach medicines for a long time may merit particular consideration for further testing. The risk prediction tool is quick and simple to administer but will need further calibration and validation in a prospective study in primary care. Funding: Charles Wolfson Trust and Guts UK.