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Background: To determine if the implementation of a posterior urethral valves (PUV) clinic and standardized management pathway improves the short-term kidney outcomes of infants with PUV. Methods: From 2016–2022, 50 consecutive patients were divided into groups after the implementation of the clinic (APUV, n = 29) and before (BPUV, n = 21) during a comparable timeframe. Assessed data included age at initial visit, timing and type of surgery, frequency of follow-up visits, medications, nadir creatinine, and development of CKD/kidney failure. Data are shown as median with interquartile range (IQR) and odds ratios (OR) with 95% confidence interval (CI). Results: APUV had higher rates of prenatal diagnoses (12/29 vs. 1/21; p = 0.0037), earlier initial surgical intervention (8 days; IQR 0, 105 vs. 33 days; IQR 4, 603; p < 0.0001), and higher rates of primary diversions (10/29 vs. 0/21; p = 0.0028). Standardized management led to earlier initiation of alpha blockers (326 days; IQR 6, 860 vs. 991; IQR 149, 1634; p = 0.0019) and anticholinergics (57 days; IQR 3, 860 vs. 1283 days; IQR 477, 1718; p < 0.0001). Nadir creatinine was reached at earlier ages in APUV (105 days; IQR 2, 303 vs. 164 days; IQR 21, 447; p = 0.0192 BPUV). One patient progressed to CKD5 in APUV compared to CKD3, CKD5 and one transplant in BPUV. Conclusion: Implementing the PUV clinic with standardized treatment expedited postnatal management and resulted in a higher number of cases detected prenatally, a shift in primary treatment, younger ages at initial treatment, reduced time to nadir creatinine, and timely initiation of supportive medications. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

Purpose: To develop a model that predicts whether a child will develop a recurrent obstruction after pyeloplasty, determine their survival risk score, and expected time to re-intervention using machine learning (ML). Methods: We reviewed patients undergoing pyeloplasty from 2008 to 2020 at our institution, including all children and adolescents younger than 18 years. We developed a two-stage machine learning model from 34 clinical fields, which included patient characteristics, ultrasound findings, and anatomical variation. We fit and trained with a logistic lasso model for binary cure model and subsequent survival model. Feature importance on the model was determined with post-selection inference. Performance metrics included area under the receiver-operating-characteristic (AUROC), concordance, and leave-one-out cross validation. Results: A total of 543 patients were identified, with a median preoperative and postoperative anteroposterior diameter of 23 and 10 mm, respectively. 39 of 232 patients included in the survival model required re-intervention. The cure and survival models performed well with a leave-one-out cross validation AUROC and concordance of 0.86 and 0.78, respectively. Post-selective inference showed that larger anteroposterior diameter at the second post-op follow-up, and anatomical variation in the form of concurrent anomalies were significant model features predicting negative outcomes. The model can be used at https://sickkidsurology.shinyapps.io/PyeloplastyReOpRisk/. Conclusion: Our ML-based model performed well in predicting the risk of and time to re-intervention after pyeloplasty. The implementation of this ML-based approach is novel in pediatric urology and will likely help achieve personalized risk stratification for patients undergoing pyeloplasty. Further real-world validation is warranted. [ABSTRACT FROM AUTHOR]