Posterior Urethral Valves Outcomes Prediction (PUVOP): a machine learning tool to predict clinically relevant outcomes in boys with posterior urethral valves.

BACKGROUND: Early kidney and anatomic features may be predictive of future progression and need for additional procedures in patients with posterior urethral valve (PUV). The objective of this study was to use machine learning (ML) to predict clinically relevant outcomes in these patients. METHODS: Patients diagnosed with PUV with kidney function measurements at our institution between 2000 and 2020 were included. Pertinent clinical measures were abstracted, including estimated glomerular filtration rate (eGFR) at each visit, initial vesicoureteral reflux grade, and renal dysplasia at presentation. ML models were developed to predict clinically relevant outcomes: progression in CKD stage, initiation of kidney replacement therapy (KRT), and need for clean-intermittent catheterization (CIC). Model performance was assessed by concordance index (c-index) and the model was externally validated. RESULTS: A total of 103 patients were included with a median follow-up of 5.7 years. Of these patients, 26 (25%) had CKD progression, 18 (17%) required KRT, and 32 (31%) were prescribed CIC. Additionally, 22 patients were included for external validation. The ML model predicted CKD progression (c-index = 0.77; external C-index = 0.78), KRT (c-index = 0.95; external C-index = 0.89) and indicated CIC (c-index = 0.70; external C-index = 0.64), and all performed better than Cox proportional-hazards regression. The models have been packaged into a simple easy-to-use tool, available at https://share.streamlit.io/jcckwong/puvop/main/app.py CONCLUSION: ML-based approaches for predicting clinically relevant outcomes in PUV are feasible. Further validation is warranted, but this implementable model can act as a decision-making aid. A higher resolution version of the Graphical abstract is available as Supplementary information.

Understanding and Assessing Nontechnical Skills in Robotic Urological Surgery: A Systematic Review and Synthesis of the Validity Evidence.

OBJECTIVE: Robotic urological surgery (RUS) has seen widespread adoption across institutions in the last decade. To match this rapid growth, it is imperative to develop a structured RUS curriculum that addresses both technical and nontechnical competencies. Emerging evidence has shown that nontechnical skills form a critical component of RUS training. The purpose of this review is to examine the validity evidence of available nontechnical skills assessment tools in RUS. METHODS: A literature search of MEDLINE, EMBASE, and PsycINFO was conducted to identify primary articles using nontechnical skills assessment tools in RUS. Messick's validity framework and the Medical Education Research Study Quality Instrument were utilized to evaluate the quality of the validity evidence of the abstracted articles. RESULTS: Of the 566 articles identified, 12 used nontechnical skills assessment tools in RUS. The metrics used ranged from self-assessment using global rating scales, to objective measures such as electroencephalography. The setting of these evaluations ranged from immersive and virtual reality-based simulators to live surgery. CONCLUSIONS: Limited effort has been made to develop nontechnical skills assessment tools in RUS. Recently, there has been a shift from subjective to objective measures of nontechnical performance, as well as the development of assessments specific to RUS. However, the validity evidence supporting these nontechnical assessments is limited at this time, including their relationship to technical skills, and their impact on surgical outcomes.