Clinical trial Recruiting at 1 site Interventional Ages 18 and up

Machine learning to distinguish EGPA from HES

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

Run by Fondazione IRCCS Policlinico San Matteo di Pavia · NCT07275190 · Data updated 2026-11-12

This project will test whether machine learning applied to clinical, lab, and instrument data can help tell EGPA apart from HES and predict how patients respond to treatment.

Quick facts

Study typeInterventional
Ages18 Years and up
SexAll
Enrollment60 (estimated)
SponsorFondazione IRCCS Policlinico San Matteo di Pavia
Academic / other
Locations1 site (Pavia)
StatusRecruiting
StartedJune 1, 2025
Main results expectedSeptember 2026
Study endsDecember 2026
Registry record updatedDecember 10, 2025
Trial IDNCT07275190 on ClinicalTrials.gov

Is this trial a fit for you?

Likely a good fit

Adults aged 18 or older with a confirmed diagnosis of EGPA or HES who can provide informed consent (or have an authorized representative) are eligible.

Probably not a fit

People without a confirmed EGPA or HES diagnosis, those with other known causes of eosinophilia, and individuals expecting direct therapeutic benefit are unlikely to gain personal medical benefit from participation.

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

Researchers will collect clinical history, laboratory results, instrumental data, and blood samples from adults diagnosed with EGPA or HES at a single center in Pavia, Italy. The collected data will be processed and analyzed with machine learning algorithms to identify patterns and features that differentiate the two conditions and to develop models predicting treatment response. Participation involves informed consent and blood draws for laboratory assessment; no investigational drugs or therapeutic interventions are given. The ultimate aim is to produce an algorithmic tool that could support clinicians in making more accurate and timely diagnoses.

Why it matters

Potential benefit: If successful, this approach could speed up and improve diagnostic accuracy between EGPA and HES, helping clinicians choose more appropriate treatments sooner.

How similar studies have gone: Machine learning has shown promise in differentiating related inflammatory and hematologic disorders, but applying it specifically to distinguish EGPA from HES is relatively novel and not yet widely validated.

Who can join: the full rules

Show full inclusion / exclusion criteria
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

Where it's running

  • Fondazione IRCCS Policlinico San Matteo, SC Reumatologia
    Pavia, Italy
    Recruiting

Study contacts

Similar studies

Other studies for the same condition or treatment. Recruiting studies are listed first.

How to take part

  1. Review the eligibility criteria above with your treating physician.
  2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
  3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.
Last reviewed 2026-11-14 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.