biostudies-literatureRosenfeld ACancer Research UKRosetrees TrustNational Institute for Health Research (NIHR)Wellcome TrustE37-E48https://www.ebi.ac.uk/biostudies/studies/S-EPMC7056359biostudies-literatureUnknown2(1)<h4>Background</h4>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.<h4>Methods</h4>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 informatiThe Lancet. Digital healthDevelopment and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach.PMC7056359203145Z/16/Z16893M661A12088Hagan DWinstanley ALovat SJHeifetz EMLovat LBRodriguez Justo MBEST2 study groupSami SSNoiman UWilson ASasieni PJevons SAriza JAhmad OFGraham DGBen-Zecharia MRosenfeld AFitzgerald RCNovelli MfalseDevelopment and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach.<h4>Background</h4>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.<h4>Methods</h4>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 informati2020-01-01T00:00:00Z2020 Jan2025-04-22T00:21:44.225Z2020-05-22T12:09:07ZS-EPMC70563593213344010.1016/s2589-7500(19)30216-x10.1016/S2589-7500(19)30216-X