Hypereosinophilic Syndrome
Conditions
Keywords
decision algorithm
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
Interventions
OTHERScheduled exams and diagnosis
Scheduled exams and diagnosis circuit as imposed by the algorithm
Sponsors
University Hospital, Limoges
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Number of patients having correctly follow the diagnosis algorithm | 5 months | This outcome measure how many patients have correctly followed the diagnosis algorithm |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Rate of diagnosis | 5 months | Evaluate the rate of diagnosis using our diagnosis algorithm |
| Assess the time to diagnosis | 5 months | Assess the time to diagnosis |
| Description of diagnosis | 5 months | To compare the diagnosis found in our study to the published cohort. |
Countries
France
Contacts
PRINCIPAL_INVESTIGATORHoly BEZANAHARY
University Hospital, Limoges
Outcome results
None listed