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

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Abstract

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.

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Code availability

Code used in this study is available upon request to the corresponding author.

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Author information

Author notes

  1. Jethro CC. Kwong and Adree Khondker are co-first authors

Authors and Affiliations

  1. Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada

    Jethro CC. Kwong, Jin Kyu Kim & Armando J. Lorenzo

  2. Division of Urology, Department of Surgery, Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada

    Jethro CC. Kwong, Adree Khondker, Jin Kyu Kim, Michael Chua, Daniel T. Keefe, Joana Dos Santos, Mandy Rickard & Armando J. Lorenzo

  3. Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada

    Adree Khondker

  4. Centre for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada

    Marta Skreta & Lauren Erdman

  5. Division of Urology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Neeta D’Souza, Antoine Fermin Selman, John Weaver, Dana A. Weiss, Christopher Long & Gregory Tasian

  6. Division of Nephrology, Hospital for Sick Children, Toronto, ON, Canada

    Chia Wei Teoh

  7. Department of Paediatrics, University of Toronto, Toronto, ON, Canada

    Chia Wei Teoh

Authors
  1. Jethro CC. Kwong
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  2. Adree Khondker
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  3. Jin Kyu Kim
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  10. Antoine Fermin Selman
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  11. John Weaver
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  12. Dana A. Weiss
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  13. Christopher Long
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  14. Gregory Tasian
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  15. Chia Wei Teoh
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  16. Mandy Rickard
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  17. Armando J. Lorenzo
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Contributions

All authors contributed to project development, data collection, data analysis, and manuscript writing.

Corresponding author

Correspondence to Armando J. Lorenzo.

Ethics declarations

Research involving human participants

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.

Informed consent

Due to the retrospective nature of this study, informed consent was not required.

Competing interests

The authors declare no competing interests.

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Kwong, J.C., Khondker, A., Kim, J.K. et al. Posterior Urethral Valves Outcomes Prediction (PUVOP): a machine learning tool to predict clinically relevant outcomes in boys with posterior urethral valves. Pediatr Nephrol 37, 1067–1074 (2022). https://doi.org/10.1007/s00467-021-05321-3

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  • DOI: https://doi.org/10.1007/s00467-021-05321-3

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