Machine Learning Analysis of Videourodynamics to Predict Incident Hydronephrosis in Patients With Spina Bifida.

Journal: The Journal of urology
Published Date:

Abstract

PURPOSE: Variability in the interpretation of videourodynamics studies limits reliable classification of kidney injury risk for patients with spina bifida. We developed machine learning models to predict incident hydronephrosis in patients with spina bifida using videourodynamics data.

Authors

  • John K Weaver
    Division of Pediatric Urology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
  • Joseph Logan
    Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
  • Jason P Van Batavia
    Department of Urology, Columbia University Medical Center, 161 Fort Washington Avenue - Herbert Irving Pavilion, 11th Floor, New York, NY 10032, USA.
  • Dana A Weiss
    Division of Urology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, USA; Division of Urology, Hospital of the University of Pennsylvania, Perelman Center for Advanced Care, 3400 Civic Center Blvd, 3rd Floor West Pavilion, Philadelphia, PA 19104, USA.
  • Christopher J Long
    Division of Urology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, USA; Division of Urology, Hospital of the University of Pennsylvania, Perelman Center for Advanced Care, 3400 Civic Center Blvd, 3rd Floor West Pavilion, Philadelphia, PA 19104, USA.
  • Ariana L Smith
    Division of Urology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Stephen A Zderic
    Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
  • Zoe Gan
    Division of Urology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Karl Godlewski
    Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
  • Reiley Broms
    Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
  • Maria Antony
    Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
  • Maya Overland
    Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
  • Tyler Gaines
    Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
  • Dennis Head
    Division of Pediatric Urology, Cleveland Clinic Children's, Cleveland, Ohio.
  • Lauren Erdman
    Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada.
  • Bernarda Viteri
    Division of Nephrology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
  • Madalyne Martin-Olenski
    Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
  • Jing Huang
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Yong Fan
    CPB/ECMO Children's Hospital, Zhejiang University School of Medicine, 310052 Hangzhou, Zhejiang, China.
  • Gregory E Tasian
    Department of Surgery, Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States; Center for Pediatric Clinical Effectiveness, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States; Department of Biostatistics, Epidemiology, and Informatics, The University of Pennsylvania, Philadelphia, PA, 19104, United States.