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


OBJECTIVE
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.


CONCLUSIONS
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.
Division of Gastroenterology, Department of Clinical Sciences, L. Sacco University Hospital, Milano, Italy. fabio.pace@unimi.it
Fabio
F
Pace
Gabriele
G
Riegler
Annalisa
A
de Leone
Marina
M
Pace
Renzo
R
Cestari
Patrizia
P
Dominici
Enzo
E
Grossi
D000328:Adult; D000465:Algorithms; D003937:Diagnosis, Differential; D004382:Duodenitis / Q000175:diagnosis*; D005260:Female; D005764:Gastroesophageal Reflux / Q000175:diagnosis*; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011446:Prospective Studies; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D011795:Surveys and Questionnaires / Q000592:standards*