Kidney function and transplants in prune belly syndrome: a scoping review.

BACKGROUND: Children with prune belly syndrome (PBS) are at higher risk of developing kidney dysfunction and requiring kidney replacement therapy (KRT). While studies have described surgical and survival outcomes in these populations, there has yet to be a focused synthesis of evidence regarding kidney outcomes in this population. Here, the focus of this scoping review was to highlight knowledge gaps and report standards on kidney outcomes in PBS of all ages. METHODS: Following scoping review methodology, EMBASE, MEDLINE, and Scopus were searched for peer-reviewed literature that describe kidney outcomes in PBS. All studies with a broad set of kidney outcomes (such as kidney function measures, chronic kidney disease (CKD), KRT and associated outcomes) were included. Findings were summarized and qualitatively synthesized. RESULTS: Of the 436 unique records identified, 25 were included for synthesis. A total of 17 studies (441 patients) reported on kidney insufficiency outcomes, with an estimated prevalence of CKD ranging from 8 to 66%. A total of 15 studies (314 patients) described KRT, primary kidney transplant, and outcomes. Of these, the age for KRT ranged from 4 to 21 years, and graft survival ranged from 22 to 87% by last follow-up (range 1.3-27 years). CONCLUSIONS: There is significant variability in studies reporting kidney outcomes in PBS which limits meaningful synthesis. There is a need for future studies with comprehensive reporting of confounders and drivers for kidney insufficiency in PBS.

Predicting non-muscle invasive bladder cancer outcomes using artificial intelligence: a systematic review using APPRAISE-AI.

Accurate prediction of recurrence and progression in non-muscle invasive bladder cancer (NMIBC) is essential to inform management and eligibility for clinical trials. Despite substantial interest in developing artificial intelligence (AI) applications in NMIBC, their clinical readiness remains unclear. This systematic review aimed to critically appraise AI studies predicting NMIBC outcomes, and to identify common methodological and reporting pitfalls. MEDLINE, EMBASE, Web of Science, and Scopus were searched from inception to February 5th, 2024 for AI studies predicting NMIBC recurrence or progression. APPRAISE-AI was used to assess methodological and reporting quality of these studies. Performance between AI and non-AI approaches included within these studies were compared. A total of 15 studies (five on recurrence, four on progression, and six on both) were included. All studies were retrospective, with a median follow-up of 71 months (IQR 32-93) and median cohort size of 125 (IQR 93-309). Most studies were low quality, with only one classified as high quality. While AI models generally outperformed non-AI approaches with respect to accuracy, c-index, sensitivity, and specificity, this margin of benefit varied with study quality (median absolute performance difference was 10 for low, 22 for moderate, and 4 for high quality studies). Common pitfalls included dataset limitations, heterogeneous outcome definitions, methodological flaws, suboptimal model evaluation, and reproducibility issues. Recommendations to address these challenges are proposed. These findings emphasise the need for collaborative efforts between urological and AI communities paired with rigorous methodologies to develop higher quality models, enabling AI to reach its potential in enhancing NMIBC care.

An Autonomous Continuous Bladder Irrigation System.

Introduction and Objective: Continuous bladder irrigation (CBI) is used in a variety of clinical settings, including post-transurethral surgery and the emergency department. Currently, CBI administration relies on nurses to diligently monitor and switch irrigation bags, as well as titrate the inflow rate based on effluent color. Inappropriate administration can result in discomfort to patients, clot urinary retention, repeat injury to the pathologic or surgical site, extended hospital stays, and even operative management. Our objective was to create an autonomous CBI system that decreases the incidence of disrupted irrigation flow and monitors the outflow to alert clinicians of critical events. Methods: 3D printing and off-the-shelf microcontrollers were used to design a device to fit the needs identified by stakeholders at our institution. An in vitro model of the bladder was created to test our design. The mechanical, electrical, and software subsystems were adjusted accordingly to meet our design requirements. Results: Our in vitro CBI model was able to simulate routine CBI administration with sudden bleeding. Bovine blood was used to simulate the bleeding events. A device was created that met identified stakeholder needs. Accurate detection of critical bleeding events, catheter blockage, and empty irrigation bags were achieved. The device responds to bleeding appropriately by increasing the irrigation rate. When the catheter is blocked, it stops the irrigation and alerts the nurse. Our system accurately titrated the irrigation rate to match a set outflow blood level parameter, conserving irrigation and minimizing nursing workload. Continuous monitoring of CBI effluent was recorded. Conclusions: We anticipate our device will decrease the cognitive load on nurses in busy clinical settings and improve workflow. Moreover, the detection of critical events will likely decrease patient morbidity. Continuous monitoring of the CBI outflow may prove to be a new clinical decision-making tool for ongoing hematuria. Clinical trial is pending.

Existing trends and applications of artificial intelligence in urothelial cancer A scoping review.

INTRODUCTION: The use of artificial intelligence (AI) in urology is gaining significant traction. While previous reviews of AI applications in urology exist, there have been few attempts to synthesize existing literature on urothelial cancer (UC). METHODS: Comprehensive searches based on the concepts of "AI" and "urothelial cancer" were conducted in MEDLINE , EMBASE , Web of Science, and Scopus. Study selection and data abstraction were conducted by two independent reviewers. Two independent raters assessed study quality in a random sample of 25 studies with the prediction model risk of bias assessment tool (PROBAST) and the standardized reporting of machine learning applications in urology (STREAM-URO) framework. RESULTS: From a database search of 4581 studies, 227 were included. By area of research, 33% focused on image analysis, 26% on genomics, 16% on radiomics, and 15% on clinicopathology. Thematic content analysis identified qualitative trends in AI models employed and variables for feature extraction. Only 19% of studies compared performance of AI models to non-AI methods. All selected studies demonstrated high risk of bias for analysis and overall concern with Cohen's kappa (k)=0.68. Selected studies met 66% of STREAM-URO items, with k=0.76. CONCLUSIONS: The use of AI in UC is a topic of increasing importance; however, there is a need for improved standardized reporting, as evidenced by the high risk of bias and low methodologic quality identified in the included studies.

Primary ablation versus urinary diversion in posterior urethral valve: Systematic review and meta-analysis.

PURPOSE: To determine differences in long-term kidney and bladder outcomes in boys with posterior urethral valves (PUV) managed by a primary valve ablation or primary urinary diversion. MATERIALS AND METHODS: A systematic search was performed in March 2021. Comparative studies were evaluated according to Cochrane collaboration recommendations. Assessed measures included kidney outcomes (chronic kidney disease, end-stage renal disease, kidney function) and bladder outcomes. Odds ratios (OR) and mean difference (MD) with 95% confidence interval (CI) were extrapolated from available data for quantitative synthesis. Random-effects meta-analysis and meta-regression were performed according to study design, and potential covariates were assessed with subgroup analysis. The systematic review was prospectively registered on PROSPERO (CRD42021243967). RESULTS: Thirty unique studies describing 1547 boys with PUV were included in this synthesis. Overall effect estimates demonstrate that patients undergoing primary diversion have significantly increased odds of developing renal insufficiency [OR 0.60, 95% CI 0.44, 0.80; p < 0.001]. However, when adjusting for baseline kidney function between intervention groups, there was no significant difference in long term kidney outcomes [p = 0.09, 0.35], or the development of bladder dysfunction or requiring clean-intermittent catheterization with primary ablation rather than diversion [OR 0.89, 95% CI 0.49, 1.59; p = 0.68]. CONCLUSIONS: Current low-quality evidence suggests that medium-term kidney outcomes in children are similar between primary ablation and primary diversion after adjusting for baseline kidney function, while bladder outcomes are highly heterogenous. Further research with covariate control is warranted to investigate sources of heterogeneity. LEVEL OF EVIDENCE: Level III.

Health Care Students' Perspectives on Artificial Intelligence: Countrywide Survey in Canada.

BACKGROUND: Artificial intelligence (AI) is no longer a futuristic concept; it is increasingly being integrated into health care. As studies on attitudes toward AI have primarily focused on physicians, there is a need to assess the perspectives of students across health care disciplines to inform future curriculum development. OBJECTIVE: This study aims to explore and identify gaps in the knowledge that Canadian health care students have regarding AI, capture how health care students in different fields differ in their knowledge and perspectives on AI, and present student-identified ways that AI literacy may be incorporated into the health care curriculum. METHODS: The survey was developed from a narrative literature review of topics in attitudinal surveys on AI. The final survey comprised 15 items, including multiple-choice questions, pick-group-rank questions, 11-point Likert scale items, slider scale questions, and narrative questions. We used snowball and convenience sampling methods by distributing an email with a description and a link to the web-based survey to representatives from 18 Canadian schools. RESULTS: A total of 2167 students across 10 different health professions from 18 universities across Canada responded to the survey. Overall, 78.77% (1707/2167) predicted that AI technology would affect their careers within the coming decade and 74.5% (1595/2167) reported a positive outlook toward the emerging role of AI in their respective fields. Attitudes toward AI varied by discipline. Students, even those opposed to AI, identified the need to incorporate a basic understanding of AI into their curricula. CONCLUSIONS: We performed a nationwide survey of health care students across 10 different health professions in Canada. The findings would inform student-identified topics within AI and their preferred delivery formats, which would advance education across different health care professions.