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Eosinophilic esophagitis (EoE) is a progressive type 2 inflammatory disease characterized by symptoms related to esophageal dysfunction and significant esophageal eosinophilic infiltration. It can affect patients from infancy through adulthood. Pediatric EoE has a multidimensional impact on the quality of life of both patients and their families. Nonspecific symptoms mimicking other gastrointestinal conditions, such as food refusal, failure to thrive, and feeding difficulties, may profoundly affect young children’s eating skills, growth, and psychosocial status, as well as impact family financial conditions. In adolescence, dysphagia and esophageal food impactions often lead to feeding-related anxiety and influence social lives. Delays in diagnosis, arising from lack of awareness among families and clinicians and compensatory eating behaviors, could increase the risk of fibrostenotic complications, which may ultimately add to the symptom burden. Currently available treatment options include proton pump inhibitors, dietary therapies, swallowed topical steroids, esophageal dilation, and biologic therapy. Despite the efficacy of these approaches, disease burden may be further impacted by their limitations, including poor adherence rates, refractory disease, potential long-term safety concerns, and high costs for care. Thus, there is a need for more timely diagnosis in clinical practice and novel targeted disease-modifying therapies better tailored to treat various phenotypes of EoE, aimed at reducing the physical and psychosocial burdens on patients and their caregivers.

Real-world evidence (RWE) has traditionally been used by regulatory or payer authorities to inform disease burden, background risk, or conduct post-launch pharmacovigilance, but in recent years RWE has been increasingly used to inform regulatory decision-making. However, RWE data sources remain fragmented, and datasets are disparate and often collected inconsistently. To this end, we have constructed an RWE-generation platform, Immunolab, to facilitate data-driven insights, hypothesis generation and research in immunological diseases driven by type 2 inflammation. Immunolab leverages a large, anonymized patient cohort from the Optum electronic health record and claims dataset containing over 17 million patient lives. Immunolab is an interactive platform that hosts three analytical modules: the Patient Journey Mapper, to describe the drug treatment patterns over time in patient cohorts; the Switch Modeler, to model treatment switching patterns and identify its drivers; and the Head-to-Head Simulator, to model the comparative effectiveness of treatments based on relevant clinical outcomes. The Immunolab modules utilize various analytic methodologies including machine learning algorithms for result generation which can then be presented in various formats for further analysis and interpretation.