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Rosenfeld A, Graham DG, Jevons S, Ariza J, Hagan D, Wilson A, Lovat SJ, Sami SS, Ahmad OF, Novelli M, Rodriguez Justo M, Winstanley A, Heifetz EM, Ben-Zecharia M, Noiman U, Fitzgerald RC, Sasieni P, Lovat LB. Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach. Lancet Digit Health 2020; 2:E37-E48. [PMID: 32133440 PMCID: PMC7056359 DOI: 10.1016/s2589-7500(19)30216-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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. Methods Questionnaires from 1299 patients in the BEST2 case-controlled study were analysed: 880 had BE including 40 with invasive oesophageal adenocarcinoma (OAC) and 419 were controls. This was randomly split into a training cohort of 776 patients and an internal validation cohort of 523 patients. External validation included 398 patients from the BOOST case-controlled study: 198 with BE (23 with OAC) and 200 controls. Identification of independently important diagnostic features was undertaken using machine learning techniques information gain (IG) and correlation based feature selection (CFS). Multiple classification tools were assessed to create a multi-variable risk prediction model. Internal validation was followed by external validation in the independent dataset. Findings The BEST2 study included 40 features. Of these, 24 added IG but following CFS, only 8 demonstrated independent diagnostic value including age, gender, smoking, waist circumference, frequency of stomach pain, duration of heartburn and acid taste and taking of acid suppression medicines. Logistic regression offered the highest prediction quality with AUC (area under the receiver operator curve) of 0.87. In the internal validation set, AUC was 0.86. In the BOOST external validation set, AUC was 0.81. Interpretation The diagnostic model offers valid predictions of diagnosis of BE in patients with symptomatic gastroesophageal reflux, assisting in identifying who should go forward to invasive testing. Overweight men who have been taking stomach medicines for a long time may merit particular consideration for further testing. The risk prediction tool is quick and simple to administer but will need further calibration and validation in a prospective study in primary care. Funding Charles Wolfson Trust and Guts UK.
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Affiliation(s)
- Avi Rosenfeld
- Department of Industrial Engineering Jerusalem College of Technology (JCT), Jerusalem, Israel
- GENIE GastroENterological IntervEntion Group, Department for Targeted Intervention, University College London (UCL), London, United Kingdom
| | - David G Graham
- GENIE GastroENterological IntervEntion Group, Department for Targeted Intervention, University College London (UCL), London, United Kingdom
- Gastrointestinal Services, University College London Hospital (UCLH), 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
- Gastrointestinal Services, University College London Hospital (UCLH), 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
- Gastrointestinal Services, University College London Hospital (UCLH), London, United Kingdom
| | - Omer F Ahmad
- GENIE GastroENterological IntervEntion Group, Department for Targeted Intervention, University College London (UCL), London, United Kingdom
- Gastrointestinal Services, University College London Hospital (UCLH), London, United Kingdom
| | - Marco Novelli
- 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
| | | | - Peter Sasieni
- Cancer Prevention Trials Unit, Queen Mary University of London, London, United Kingdom
- School of Cancer & Pharmaceutical Sciences, King's College London, London, United Kingdom
| | - Laurence B Lovat
- GENIE GastroENterological IntervEntion Group, Department for Targeted Intervention, University College London (UCL), London, United Kingdom
- Gastrointestinal Services, University College London Hospital (UCLH), London, United Kingdom
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