Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach.

Journal: The Lancet. Digital health
Published Date: Dec 5, 2019

Abstract

BACKGROUND: Screening for Barrett's Oesophagus (BE) relies on endoscopy which is invasive and has a low yield. This study aimed to develop and externally validate a simple symptom and risk-factor questionnaire to screen for patients with BE.

Authors

  • Avi Rosenfeld
    Department of Industrial Engineering Jerusalem College of Technology (JCT), Jerusalem, Israel.
  • David G Graham
    GENIE GastroENterological IntervEntion Group, Department for Targeted Intervention, University College London (UCL), London, United Kingdom.
  • Sarah Jevons
    GENIE GastroENterological IntervEntion Group, Department for Targeted Intervention, University College London (UCL), London, United Kingdom.
  • Jose Ariza
    GENIE GastroENterological IntervEntion Group, Department for Targeted Intervention, University College London (UCL), London, United Kingdom.
  • Daryl Hagan
    GENIE GastroENterological IntervEntion Group, Department for Targeted Intervention, University College London (UCL), London, United Kingdom.
  • Ash Wilson
    GENIE GastroENterological IntervEntion Group, Department for Targeted Intervention, University College London (UCL), London, United Kingdom.
  • Samuel J Lovat
    GENIE GastroENterological IntervEntion Group, Department for Targeted Intervention, University College London (UCL), London, United Kingdom.
  • Sarmed S Sami
    GENIE GastroENterological IntervEntion Group, Department for Targeted Intervention, University College London (UCL), London, United Kingdom.
  • Omer F Ahmad
    Department of Gastroenterology, University College London Hospitals NHS Foundation Trust, 250 Euston Road, London, NW1 2BU, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, United Kingdom. Electronic address: ofahmad123@gmail.com.
  • Marco Novelli
    Dept of Pathology, University College London Hospital (UCLH), London, United Kingdom.
  • Manuel Rodriguez Justo
    Dept of Pathology, University College London Hospital (UCLH), London, United Kingdom.
  • Alison Winstanley
    Dept of Pathology, University College London Hospital (UCLH), London, United Kingdom.
  • Eliyahu M Heifetz
    Department of Health Informatics, Jerusalem College of Technology (JCT), Jerusalem, Israel.
  • Mordehy Ben-Zecharia
    Department of Health Informatics, Jerusalem College of Technology (JCT), Jerusalem, Israel.
  • Uria Noiman
    Department of Health Informatics, Jerusalem College of Technology (JCT), Jerusalem, Israel.
  • Rebecca C Fitzgerald
    MRC Cancer Unit, University of Cambridge, Cambridge, United Kingdom.
  • Peter Sasieni
    Cancer Prevention Trials Unit, Queen Mary University of London, London, United Kingdom.
  • Laurence B Lovat
    Division of Surgery & Interventional Science, University College London, London, UK.

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