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

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.This research/study/project was funded by the Charles Wolfson Trust and Guts UK and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. This work was also supported by the CRUK Experimental Cancer Medicine Centre at UCL and the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) at UCL; [203145Z/16/Z]. BEST2 was funded by Cancer Research UK (12088 and 16893)