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

Fondazione IRCCS Policlinico San Matteo di Pavia60 enrolled

Overview

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.

Study Type

INTERVENTIONAL

Allocation

Purpose

OTHER

Masking

NONE

Enrollment

Conditions

EGPA - Eosinophilic Granulomatosis With Polyangiitis HES - Hypereosinophilic Syndrome

Interventions

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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

Central Contacts

Carlomaurizio Montecucco

CONTACT

390382502948 carlomaurizio.montecucco@unipv.it

Alessandra Milanesi

CONTACT

0382502498 al.milanesi@smatteo.pv.it

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

Overview

Conditions

Interventions

Eligibility

Locations (1)

Outcomes

Primary Outcomes

Central Contacts