Clinical trial NCT02581514: Eosinophilia Diagnosis
Official title: Algorithm for the Early Diagnosis and Treatment of Patients With Eosinophilia
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Hypereosinophilic Syndrome
Keywords: decision algorithm
Lead sponsor: University Hospital, Limoges
Sponsor class: OTHER
First posted: 2015-10-21
Enrollment: 53
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

Brief summary: Eosinophilia, defined by a blood eosinophil granulocytes rate greater than 500 / mm3, is frequently encountered in internal medicine.

Its causes are varied: atopy, drug allergies, parasitic infections, autoimmune diseases and solid neoplasias. Over 200 etiologies have been reported, some difficult to diagnose and can be life-threatening Eosinophilia can be a diagnostic dilemma, as the etiologies are extensive and varied.

The aim of this study is to assess the feasibility of a diagnostic approach based on a decision algorithm in a group of patients with eosinophilia.

We assume that a procedure with a hierarchy of additional tests would increase the frequency of diagnosed cases while decreasing the time to diagnosis.

This procedure defined by an algorithm would even reduce the number of tests necessary to reach a diagnosis.

Detailed description: Eosinophilia, defined by a blood eosinophil granulocytes rate greater than 500 / mm3, is frequently encountered in internal medicine.

Its causes are varied: atopy, drug allergies, parasitic infections, autoimmune diseases and solid neoplasias. Over 200 etiologies have been reported, some difficult to diagnose and can be life-threatening

Eosinophilia can be a diagnostic dilemma, as the etiologies are extensive and varied.

The aim of this study is to assess the feasibility of a diagnostic approach based on a decision algorithm in a group of patients with eosinophilia.

The contribution to the diagnosis of a hierarchical strategy for prescribing additional tests , based on clinical examination as well as some simple diagnostic tests, has never been evaluated

We assume that a procedure with a hierarchy of additional tests would increase the frequency of diagnosed cases while decreasing the time to diagnosis.

This procedure defined by an algorithm would even reduce the number of tests necessary to reach a diagnosis.

All types of patients are tacked into account: those coming from the university hospital, referred by general practitioners or by other hospitals.

In addition we address the internal medicine patients ,but also those of Hematology and Infectious Diseases. A comparison of these various groups would be relevant, since disorders that may be different.

Once enrolled, the patient is drived by the investigator through the various steps and exams imposed by the algorithm.

Indeed, during 5 months (Day1 5, 43, 71 , 85 , 99 ,113 and month 5), patient is asked to comply to the various exams and assessment imposed by the algorithm and that should lead to a diagnosis

Eligibility criteria: Inclusion Criteria:

1. Patient having one of the three following criteria:

   * hypereosinophilia\> 1500 / mm3, checked on at least two samples (interval between 2 samples at the discretion of the clinician)
   * or hypereosinophilia\> 500 cells / mm3 and organ damage with infiltration NCB proven by pathological examination,
   * or hypereosinophilia\> 500 cells / mm3 and found consistently for at least six months (present on all controls carried out before inclusion).
2. Patient affiliated or beneficiary of a social security system
3. Patient who signed the informed consent

Exclusion Criteria:

1. Patient with solid tumors known (under chemotherapy or planned)
2. Patient unable to understand or to adhere to the Protocol
3. Patient unable to give consent
4. Pregnant or breastfeeding women
5. Patient already participating in an interventional trial

Primary outcomes:
measure: Number of patients having correctly follow the diagnosis algorithm
timeFrame: 5 months
Secondary outcomes:
measure: Rate of diagnosis
timeFrame: 5 months
measure: Assess the time to diagnosis
timeFrame: 5 months
measure: Description of diagnosis
timeFrame: 5 months
Other outcomes:
Locations:
city: Limoges
country: France
facility: Médecine Interne A
Study officials:
affiliation: University Hospital, Limoges
name: Holy BEZANAHARY
role: PRINCIPAL_INVESTIGATOR
References: