OTU-16 A machine learning-based model to predict the presence of barrett's oesophagus. (June 2019)

• Record Type: Journal Article
• Authors: Lovat, Laurence; Sami, Sarmed; Graham, David; Ariza, Jose; Hagan, Daryl; Ocampo, Frelyn; Novelli, Marco; Rodriguez-Justo, Manuel; Winstanley, Alison; Heifetz, Eliyahu; Ben-Zecharia, Mordehy; Noiman, Uria; Lovat, Samuel; Fitzgerald, Rebecca; Sasieni, Peter; Rosenfeld, Avi
• Is Part Of: Gut. Volume 68(2019)Supplement 2
• Page Start: A133
• Page End: A133
• Publication Date: 2019-06
• Subjects: Gastroenterology -- Periodicals; 616.33
• DOI: 10.1136/gutjnl-2019-BSGAbstracts.251
• Languages: English
• ISSNs: 0017-5749

Abstract

Abstract : Introduction: Barrett's oesophagus (BO) is the only known precursor of esophageal adenocarcinoma. The current gold-standard test for diagnosing BO is endoscopy which is expensive and impractical as a population screening tool. We aimed to develop a robust questionnaire that could be used in routine clinical practice to identify patients with different likelihoods of having BO. Methods: We retrospectively analyzed data from two independent prospective datasets: BEST2 and BOOST case-control studies with 1299 and 398 patients, respectively. The BEST2 dataset was split into testing (n=776) and independent validation (n=523) cohorts. Using machine learning techniques (information gain and correlation-based feature selection followed by logistic regression); we identified panels of markers which predicted the presence of BO and then cross validated them between the datasets in order to create a robust set of markers that could be used for future studies. Results: Panels comprising the same thirteen demographic and patient symptom features predicted the occurrence of BO in both datasets. These included three demographic features (age, gender and ethnicity), three general patient characteristics (weight, waist circumference, quantity of cigarettes smoked), and six symptoms (years since heartburn and acid taste developed, frequency of heartburn, acid taste symptoms, chest pain and taking stomach medicines). Although there were minor variations in logistic weights for each feature between the panels, they yielded accuracies with areas under the curve (AUC) of 0.91 in the BEST2 and 0.84 in the BOOST cohorts. Furthermore, despite differences in population composition, the panels validated across datasets. Validating the BEST2 model with BOOST yielded a ROC curve of 0.83 and validating the BOOST model against BEST2 yielded a ROC curve of 0.91. The final panel was also validated with the external BEST2 validation cohort (n=523) with AUC = 0.91 (table 1 ). Conclusions: This study identifies thirteen markers as a potential method for non-invasive screening for BO. These markers are consistent across different patient study groups. The panel needs to be validated in a prospective study.