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

Abstract

Background Screening for Barrett's oesophagus relies on endoscopy, which is invasive and few who undergo the procedure are found to have the condition. We aimed to use machine learning techniques to develop and externally validate a simple risk prediction panel to screen individuals for Barrett's oesophagus. MethodsIn this prospective study, machine learning risk prediction in Barrett's oesophagus (MARK-BE), we used data from two case-control studies, BEST2 and BOOST, to compile training and validation datasets. From the BEST2 study, we analysed questionnaires from 1299 patients, of whom 880 (67·7%) had Barrett's oesophagus, including 40 with invasive oesophageal adenocarcinoma, and 419 (32·3%) were controls. We randomly split (6:4) the cohort using a computer algorithm into a training dataset of 776 patients and a testing dataset of 523 patients. We compiled an external validation cohort from the BOOST study, which included 398 patients, comprising 198 patients with Barrett's oesophagus (23 with oesophageal adenocarcinoma) and 200 controls. We identified independently important diagnostic features of Barrett's oesophagus using the machine learning techniques information gain and correlationbased feature selection. We assessed multiple classification tools to create a multivariable risk prediction model. Internal validation of the model using the BEST2 testing dataset was followed by external validation using the BOOST external validation dataset. From these data we created a prediction panel to identify at-risk individuals.Findings The BEST2 study included 40 diagnostic features. Of these, 19 added information gain but after correlationbased feature selection only eight showed independent diagnostic value including age, sex, cigarette smoking, waist circumference, frequency of stomach pain, duration of heartburn and acidic taste, and taking antireflux medication, of which all were associated with increased risk of Barrett's oesophagus, except frequency of stomach pain, with was inversely associated in a case-control population. Logistic regression offered the highest prediction quality with an area under the receiver-operator curve (AUC) of 0·87 (95% CI 0·84-0·90; sensitivity set at 90%; specificity of 68%). In the testing dataset, AUC was 0·86 (0·83-0·89; sensitivity set at 90%; specificity of 65%). In the external validation dataset, the AUC was 0·81 (0·74-0·84; sensitivity set at 90%; specificity of 58%). Interpretation Our diagnostic model offers valid predictions of diagnosis of Barrett's oesophagus in patients withsymptomatic gastro-oesophageal reflux disease, assisting in identifying who should go forward to invasive confirmatory testing. Our predictive panel suggests that overweight men who have been taking antireflux medication for a long time might merit particular consideration for further testing. Our risk prediction panel is quick and simple to administer but will need further calibration and validation in a prospective study in primary care.

EndoScreen Chip: Serum Complement C9 Immunoassays Improve Risk Prediction and Early Diagnosis of Esophageal Adenocarcinoma

“…EAC is thought to develop as a consequence of chronic gastro-esophageal reflux disease (GERD), with the metaplastic condition Barrett's esophagus (BE) being the only known precursor condition [2,3]. To detect EAC at an early stage, BE patients and those with multiple risk factors for BE and EAC, such as age > 50 years, male sex, GERD history, acid suppression medication and high body mass index (BMI) are recommended to undergo endoscopic screening [4][5][6][7][8]. Despite improved risk factor identification and a BE surveillance program, temporal epidemiological data show that there has been no change in the proportion of people diagnosed into each stage of EAC since the 1970s [9].…”

Risk Prediction of Barrett’s Esophagus in a Taiwanese Health Examination Center Based on Regression Models

“…Compared with that, the study had larger sample sizes and our ANN model had better performance [ 15 ]. In addition, different from previous studies, the participants in our study were not only from physicians’ referral [ 17 , 18 , 20 ] and those with subjective symptoms [ 15 ], but also from individuals without symptoms. Rubenstein et al developed a prediction model from participants without a referral for a clinical indication; however, the study population was limited to older males (50–79 years) [ 11 ].…”

“…However, these guidelines do not provide quantitative data to stratify the risk of BE while combining multiple risk factors. A number of risk prediction models for other diseases are widely employed to help clinicians make individualized medical decisions for their patients [ 11 , 12 , 13 , 14 ]; however, most of the published BE prediction models were constructed for Western populations and have yet to be verified for Asian populations [ 11 , 15 , 16 , 17 , 18 , 19 , 20 ], a relevant issue given that BE presents different patterns for Asian and Western populations [ 21 ].…”

Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine

“…Since there is no generally accepted method to estimate the sample size requirements for a derivation study of the risk prediction model, all accessible data were used to maximise the power and generalizability of results [24]. The reliability of the ML-based model was further examined by exploring an external validation dataset.…”