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.

Development and prospective external validation of a tool to predict poor recovery at 9 months after acute ankle sprain in UK emergency departments: the SPRAINED prognostic model.

OBJECTIVES: To develop and externally validate a prognostic model for poor recovery after ankle sprain. SETTING AND PARTICIPANTS: Model development used secondary data analysis of 584 participants from a UK multicentre randomised clinical trial. External validation used data from 682 participants recruited in 10 UK emergency departments for a prospective observational cohort. OUTCOME AND ANALYSIS: Poor recovery was defined as presence of pain, functional difficulty or lack of confidence in the ankle at 9 months after injury. Twenty-three baseline candidate predictors were included together in a multivariable logistic regression model to identify the best predictors of poor recovery. Relationships between continuous variables and the outcome were modelled using fractional polynomials. Regression parameters were combined over 50 imputed data sets using Rubin's rule. To minimise overfitting, regression coefficients were multiplied by a heuristic shrinkage factor and the intercept re-estimated. Incremental value of candidate predictors assessed at 4 weeks after injury was explored using decision curve analysis and the baseline model updated. The final models included predictors selected based on the Akaike information criterion (p<0.157). Model performance was assessed by calibration and discrimination. RESULTS: Outcome rate was lower in the development (6.7%) than in the external validation data set (19.9%). Mean age (29.9 and 33.6 years), body mass index (BMI; 26.3 and 27.1 kg/m2), pain when resting (37.8 and 38.5 points) or bearing weight on the ankle (75.4 and 71.3 points) were similar in both data sets. Age, BMI, pain when resting, pain bearing weight, ability to bear weight, days from injury until assessment and injury recurrence were the selected predictors. The baseline model had fair discriminatory ability (C-statistic 0.72; 95% CI 0.66 to 0.79) but poor calibration. The updated model presented better discrimination (C-statistic 0.78; 95% CI 0.72 to 0.84), but equivalent calibration. CONCLUSIONS: The models include predictors easy to assess clinically and show benefit when compared with not using any model. TRIAL REGISTRATION NUMBER: ISRCTN12726986; Results.

Prognostic models for identifying risk of poor outcome in people with acute ankle sprains: the SPRAINED development and external validation study.

BACKGROUND: Ankle sprains are very common injuries. Although recovery can occur within weeks, around one-third of patients have longer-term problems. OBJECTIVES: To develop and externally validate a prognostic model for identifying people at increased risk of poor outcome after an acute ankle sprain. DESIGN: Development of a prognostic model in a clinical trial cohort data set and external validation in a prospective cohort study. SETTING: Emergency departments (EDs) in the UK. PARTICIPANTS: Adults with an acute ankle sprain (within 7 days of injury). SAMPLE SIZE: There were 584 clinical trial participants in the development data set and 682 recruited for the external validation study. PREDICTORS: Candidate predictor variables were chosen based on availability in the clinical data set, clinical consensus, face validity, a systematic review of the literature, data quality and plausibility of predictiveness of the outcomes. MAIN OUTCOME MEASURES: Models were developed to predict two composite outcomes representing poor outcome. Outcome 1 was the presence of at least one of the following symptoms at 9 months after injury: persistent pain, functional difficulty or lack of confidence. Outcome 2 included the same symptoms as outcome 1, with the addition of recurrence of injury. Rates of poor outcome in the external data set were lower than in the development data set, 7% versus 20% for outcome 1 and 16% versus 24% for outcome 2. ANALYSIS: Multiple imputation was used to handle missing data. Logistic regression models, together with multivariable fractional polynomials, were used to select variables and identify transformations of continuous predictors that best predicted the outcome based on a nominal alpha of 0.157, chosen to minimise overfitting. Predictive accuracy was evaluated by assessing model discrimination (c-statistic) and calibration (flexible calibration plot). RESULTS: (1) Performance of the prognostic models in development data set - the combined c-statistic for the outcome 1 model across the 50 imputed data sets was 0.74 [95% confidence interval (CI) 0.70 to 0.79], with good model calibration across the imputed data sets. The combined c-statistic for the outcome 2 model across the 50 imputed data sets was 0.70 (95% CI 0.65 to 0.74), with good model calibration across the imputed data sets. Updating these models, which used baseline data collected at the ED, with an additional variable at 4 weeks post injury (pain when bearing weight on the ankle) improved the discriminatory ability (c-statistic 0.77, 95% CI 0.73 to 0.82, for outcome 1 and 0.75, 95% CI 0.71 to 0.80, for outcome 2) and calibration of both models. (2) Performance of the models in the external data set - the combined c-statistic for the outcome 1 model across the 50 imputed data sets was 0.73 (95% CI 0.66 to 0.79), with a calibration plot intercept of -0.91 (95% CI -0.98 to 0.44) and slope of 1.13 (95% CI 0.76 to 1.50). The combined c-statistic for the outcome 2 model across the 50 imputed data sets was 0.63 (95% CI 0.58 to 0.69), with a calibration plot intercept of -0.25 (95% CI -0.27 to 0.11) and slope of 1.03 (95% CI 0.65 to 1.42). The updated models with the additional pain variable at 4 weeks had improved discriminatory ability over the baseline models but not better calibration. CONCLUSIONS: The SPRAINED (Synthesising a clinical Prognostic Rule for Ankle Injuries in the Emergency Department) prognostic models performed reasonably well, and showed benefit compared with not using any model; therefore, the models may assist clinical decision-making when managing and advising ankle sprain patients in the ED setting. The models use predictors that are simple to obtain. LIMITATIONS: The data used were from a randomised controlled trial and so were not originally intended to fulfil the aim of developing prognostic models. However, the data set was the best available, including data on the symptoms and clinical events of interest. FUTURE WORK: Further model refinement, including recalibration or identifying additional predictors, may be required. The effect of implementing and using either model in clinical practice, in terms of acceptability and uptake by clinicians and on patient outcomes, should be investigated. TRIAL REGISTRATION: Current Controlled Trials ISRCTN12726986. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 22, No. 64. See the NIHR Journals Library website for further project information. Funding was also recieved from the NIHR Collaboration for Leadership in Applied Health Research, Care Oxford at Oxford Health NHS Foundation Trust, NIHR Biomedical Research Centre, Oxford, and the NIHR Fellowship programme.