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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]

Objective: To describe the evolution of practice patterns for pediatric pyeloplasty and determine how these changes have impacted length of stay (LOS), reoperation rates and return emergency department (ER) visits. Methods: We reviewed our pyeloplasty database from 2008 to 2020 at a quaternary pediatric referral center and we included children 0–18 years undergoing pyeloplasty. Variables captured included: age, sex, baseline and follow-up anteroposterior diameter (APD) and differential renal function (DRF). We also collected data on the use of drains, catheters and/or stents, nausea and vomiting prophylaxis, opioids, regional anesthesia, and non-opioid analgesia. Outcomes were LOS, reoperation rates and ER visits. Results: A total of 554 patients (565 kidneys) were included. Reoperation rate was 7%, redo rate 4% and ER visits 17%. There was a trend towards less opioids, indwelling catheters and internal stents and increasing non-opioid analgesia, externalized stents, and regional anesthesia during the study period. Same-day discharge (SDD) was possible for 88 (16%) children with no differences in reoperation or readmission rates between SDD and admitted (ADM). There was a difference in ER visits (21 [24%] vs. 26 [6%]; p = 0.04) for SDD vs. ADM, respectively. On multivariate analysis, the only predictor of ER visits was younger age. Patients < 7 months were more likely to present to ER (15/41; 37% vs. 6/47, 13%; p = 0.009). Multivariate analysis determined indwelling catheters and opioids were associated with ADM while dexamethasone and ketorolac with SDD. Conclusion: Progressive changes in care have contributed to a shorter LOS and increasing rates of SDD for pyeloplasty patients. SDD appears to be feasible and does not result in higher complication rates. These data support the development of a pediatric pyeloplasty ERAS protocol to maximize quicker recovery and foster SDD as a goal. [ABSTRACT FROM AUTHOR]