Eosinophilic Esophagitis-Related Food Impaction: Distinct Demographics, Interventions, and Promising Predictive Models.

Abstract

Background: Eosinophilic esophagitis (EoE) is an increasingly common cause of food impaction.

Aims: This study aims to provide a nationwide analysis of food impaction in patients with or without EoE diagnosis, concentrating on patient demographics, interventions, outcomes, and development of predictive machine-learning models.

Methods: A retrospective assessment was conducted using Nationwide Emergency Department Sample data from January 1, 2018, to December 31, 2019. We compared patients with food impaction with an associated EoE diagnosis to those without EoE and derived machine-learning models to predict EoE using International Classification of Diseases codes at discharge for identification.

Results: Of 286,886,714 emergency department visits, 146,084 were for food impaction, with 7093 cases coinciding with an EoE diagnosis (4.9%). Patients with EoE were more commonly young men with fewer overall comorbidities but higher incidences of obesity, asthma, gastritis, and allergic rhinitis. A significantly larger proportion in the EoE group (89.6%) underwent esophagogastroduodenoscopy compared to the non-EoE group (51.1%; P < 0.001) and had a higher rate of biopsy during esophagogastroduodenoscopy in the emergency department (54.9% vs 13.4%; P < 0.001). Our machine-learning models, incorporating patient demographics, hospital attributes, and comorbidities, had a sensitivity of 86.1% and an area under the receiver operating characteristic curve of 0.828.

Conclusions: This nationwide study demonstrates that EoE in food impaction is associated with specific patient demographics, comorbidities, and elevated interventions. Our machine-learning models hold promise as screening tools for EoE, aiding medical practitioners in determining the need for biopsy.