{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Rosenfeld A"],"funding":["Cancer Research UK","Rosetrees Trust","National Institute for Health Research (NIHR)","Wellcome Trust"],"pagination":["E37-E48"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7056359"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["2(1)"],"pubmed_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 informati"],"journal":["The Lancet. Digital health"],"pubmed_title":["Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach."],"pmcid":["PMC7056359"],"funding_grant_id":["203145Z/16/Z","16893","M661","A12088"],"pubmed_authors":["Hagan D","Winstanley A","Lovat SJ","Heifetz EM","Lovat LB","Rodriguez Justo M","BEST2 study group","Sami SS","Noiman U","Wilson A","Sasieni P","Jevons S","Ariza J","Ahmad OF","Graham DG","Ben-Zecharia M","Rosenfeld A","Fitzgerald RC","Novelli M"],"additional_accession":[]},"is_claimable":false,"name":"Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach.","description":"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 informati","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Jan","modification":"2025-04-22T00:21:44.225Z","creation":"2020-05-22T12:09:07Z"},"accession":"S-EPMC7056359","cross_references":{"pubmed":["32133440"],"doi":["10.1016/s2589-7500(19)30216-x","10.1016/S2589-7500(19)30216-X"]}}