Classification of adults suffering from typical gastroesophageal reflux disease symptoms: contribution of latent class analysis in a European observational study
1 Institut des Maladies de l’Appareil Digestif – CHU Hôtel Dieu, 44093 Nantes Cedex, France
2 Università degli Studi, Spedali Civili, Brescia, Italy
3 Regional Clinic Hospital, Nizhny Novgorod, Russia
4 University General Hospital Attikon, Athens, Greece
5 Hospital Clinico San Carlos, Madrid, Spain
6 Janssen-Cilag, Barcarena, Portugal
7 Janssen-Cilag B.V., Tilburg, The Netherlands
8 Janssen-Cilag, Milan, Italy
9 Janssen-Cilag, Neuss, Germany
10 CHU Rouen, Rouen, France
BMC Gastroenterology 2014, 14:112 doi:10.1186/1471-230X-14-112Published: 26 June 2014
As illustrated by the Montreal classification, gastroesophageal reflux disease (GERD) is much more than heartburn and patients constitute a heterogeneous group. Understanding if links exist between patients’ characteristics and GERD symptoms, and classify subjects based on symptom-profile could help to better understand, diagnose, and treat GERD. The aim of this study was to identify distinct classes of GERD patients according to symptom profiles, using a specific statistical tool: Latent class analysis.
An observational single-visit study was conducted in 5 European countries in 7700 adults with typical symptoms. A latent class analysis was performed to identify “latent classes” and was applied to 12 indicator symptoms.
On 7434 subjects with non-missing indicators, latent class analysis yielded 5 latent classes. Class 1 grouped the highest severity of typical GERD symptoms during day and night, more digestive and non-digestive GERD symptoms, and bad sleep quality. Class 3 represented less frequent and less severe digestive and non-digestive GERD symptoms, and better sleep quality than in class 1. In class 2, only typical GERD symptoms at night occurred. Classes 4 and 5 represented daytime and nighttime regurgitation. In class 4, heartburn was also identified and more atypical digestive symptoms. Multinomial logistic regression showed that country, age, sex, smoking, alcohol use, low-fat diet, waist circumference, recent weight gain (>5 kg), elevated triglycerides, metabolic syndrome, and medical GERD treatment had a significant effect on latent classes.
Latent class analysis classified GERD patients based on symptom profiles which related to patients’ characteristics. Although further studies considering these proposed classes have to be conducted to determine the reproducibility of this classification, this new tool might contribute in better management and follow-up of patients with GERD.