The Use of Machine Learning Techniques for the Differential Diagnosis Between Eosinophilic Granulomatosis With Polyangiitis and Hypereosinophilic Syndrome

Institute of Hospitalization and Scientific Care (IRCCS)

Status: Enrolling

Conditions: EGPA - Eosinophilic Granulomatosis With Polyangiitis; HES - Hypereosinophilic Syndrome

Treatments: Procedure: Blood draw for the laboratory assessment

Study type: Interventional

Funder types: Other

Identifiers

NCT07275190

AI_EH_resub

Details and patient eligibility

About

The purpose of this study is to collect clinical, laboratory, and instrumental data from patients with eosinophilic granulomatosis with polyangiitis or hypereosinophilic syndrome, which will then be analyzed using artificial intelligence techniques with the aim of identifying characteristics that differentiate the two diseases and can predict the response to the treatment plan.

Enrollment: 60 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

1. 18 years of age or over
2. A diagnosis of EGPA or HES
3. Willing and able to give informed written consent, or willing to give permission for a nominated friend or relative to provide written informed assent if they are unable to do so because of physical disabilities

Exclusion criteria

1. Lack of a confirmed diagnosis of EGPA or HES.
2. Other causes of eosinophilia

Trial design

Primary purpose: Other

Allocation: N/A

Interventional model: Parallel Assignment

Masking: None (Open label)