Is it possible to clinically differentiate erosive from nonerosive reflux disease patients? A study using an artificial neural networks-assisted algorithm

Abstract

Background: The use of either symptom questionnaires or artificial neural networks (ANNs) has proven to improve the accuracy in diagnosing gastroesophageal reflux disease (GERD). However, the differentiation between the erosive and nonerosive reflux disease based upon symptoms at presentation still remains inconclusive.

Aim: To assess the capability of a combined approach, that is, the use of a novel GERD questionnaire - the QUestionario Italiano Diagnostico (QUID) questionnaire - and of an ANNs-assisted algorithm, to discriminate between nonerosive gastroesophageal reflux disease (NERD) and erosive esophagitis (EE) patients.

Methods: Five hundred and fifty-seven adult outpatients with typical GERD symptoms and 94 asymptomatic adult patients, were submitted to the QUID questionnaire. GERD patients were then submitted to upper gastrointestinal endoscopy to differentiate them between EE and NERD patients.

Results: The QUID score resulted significantly (P<0.001) higher in GERD patients versus controls, but it was not statistically significantly different between EE and NERD patients. ANNs assisted diagnosis had greater specificity, sensitivity and accuracy compared with the linear discriminant analysis only to differentiate GERD patients from controls. However, no single technique was able to satisfactorily discriminate between EE and NERD patients.

Conclusion: Our study suggests that the combination between QUID questionnaire and an ANNs-assisted algorithm is useful only to differentiate GERD patients from healthy individuals but fails to further discriminate erosive from nonerosive patients.