Abstract
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.
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Code availability
All code used in this study is publicly available at https://github.com/goldenberg-lab/Pyeloplasty.
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Funding
This research was supported by the Hospital for Sick Children, and the Urology Care Foundation. A.K. is the recipient of an AUA Summer Medical Student Fellowship (Herbert Brendler, MD, Research Fund; ID: 839859). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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ED, AK, JJK, JCCK, LE, MC, DTK, ML, JDS, GT, MR, and AJL: project development, data collection, data analysis, manuscript writing.
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This retrospective chart review study involving human participants was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The Research and Ethics Board of The Hospital for Sick Children approved this study.
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Drysdale, E., Khondker, A., Kim, J.K. et al. Personalized application of machine learning algorithms to identify pediatric patients at risk for recurrent ureteropelvic junction obstruction after dismembered pyeloplasty. World J Urol 40, 593–599 (2022). https://doi.org/10.1007/s00345-021-03879-z
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DOI: https://doi.org/10.1007/s00345-021-03879-z