Abstract
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. 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. 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. 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. Charles Wolfson Trust and Guts UK.
Highlights
Oesophageal cancer has a long-term survival rate of only 12%, but 59% of cases are preventable.[1]
The cytosponge test is less invasive and might add an important triaging step because it can be administered in general practice and is more acceptable to patients
Study design and participants In this prospective study, machine learning risk prediction in Barrett’s oesophagus (MARK-BE), we collected data from two case-control studies done in the UK to construct training, testing, and external validation datasets
Summary
Oesophageal cancer has a long-term survival rate of only 12%, but 59% of cases are preventable.[1] Early diagnosis is crucial to change disease outcome but symptoms in early oesophageal adenocarcinoma are often either absent or indistinguishable from uncomplicated gastro oesophageal reflux disease. Lancet Digital Health 2020 ; 2: e37–48. See Online for appendix www.thelancet.com/digital-health Vol 2 January 2020 e37. Evidence before this study We searched PubMed for publications in English from database inception until June 30, 2019, on models to identify the presence of Barrett’s oesophagus using the terms “Barrett’s esophagus”, “prediction model”, “risk factors”, and “risk prediction models”. Previous studies have identified multiple risk factors but—with two recent exceptions, one involving a small cohort of patients and the other looking at familial risk— they have either not synthesised the model to create a comprehensive risk factor panel or have not validated it in a completely independent dataset
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