Early Identification of Children With Asthma

NCT ID: NCT06988358

Last Updated: 2025-10-02

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 300 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-12-30
• Study Completion Date: 2027-06-30

Brief Summary

GPs are one of the key players in the early diagnosis of chronic diseases, such as asthma in pre-school children, by detecting symptoms of illness as early as possible. Patient health data is collected on an ongoing basis in GPs' electronic medical records, but remains little exploited despite its potential.

Helping GPs to identify asthma in pre-school children, based on the information in their electronic medical records, could help them to diagnose the condition early and thereby reduce the morbidity and mortality associated with it.

An algorithm developed and evaluated in a primary care data warehouse should help GPs to identify children with a diagnosis of asthma at an early stage.

Detailed Description

Asthma is the most common chronic disease affecting children. It is defined by repeated episodes of heterogeneous respiratory symptoms, such as wheezing, breathlessness, chest tightness and cough, which vary in time and intensity, as well as variable expiratory flow limitation. Asthma in pre-school children corresponds to asthma in children under the age of 6.

Diagnosis in children is particularly complex, due to the difficulty of performing respiratory tests such as spirometry, and the fact that symptoms often diminish with age. Diagnosis is based on a number of factors, including response to treatment and the absence of a differential diagnosis. Although asthma in pre-school children is frequent and sometimes serious, it is under-diagnosed and not optimally treated. GPs are among the key players in the early diagnosis of chronic diseases, by detecting symptoms of illness as early as possible. Patient health data is collected on an ongoing basis in GPs' electronic medical records, but remains little exploited despite its potential.

Helping GPs to identify asthma in pre-school children, based on the information in their electronic medical records, could help them to diagnose the condition at an early stage, thereby reducing the morbidity and mortality associated with it.

An algorithm, developed and evaluated in a primary care data warehouse, should help GPs to identify children with a diagnosis of asthma at an early stage.

Conditions

Asthma in Children

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: RETROSPECTIVE

Interventions

Group of children identified by the algorithm as having asthma

150 medical files of children identified by the algorithm as having asthma will be randomly selected for expert appraisal.

Intervention Type: DIAGNOSTIC_TEST

Group of children not identified by the algorithm as having asthma

150 medical files of children not identified by the algorithm as having asthma will be randomly selected for expert appraisal.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Children aged 2 years 0 days to 5 years 11 months and 30 days inclusive
• Consultation in one of the 4 Maisons de Santé Pluriprofessionnelle connected to the PRIMEGE Normandie primary care data warehouse: Neufchâtel-en-Bray, Val-de-Reuil, Le Grand-Quevilly and Rouen Carmes.
• At least two consultations between the ages of 2 and 5, with a general practitioner in the same care setting
• Parents having been informed of the use of data from electronic medical records and having expressed no objection to the use of this data

Exclusion Criteria

• Children under 2 years of age
• Children aged 6 years 0 days and over
• Recourse by a patient's legal representative to one of the RGPD rights restricting the use of their data in the context of research

• Minimum Eligible Age: 24 Months
• Maximum Eligible Age: 71 Months
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

University Hospital, Rouen

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Charlotte CS SIEGFRIDT, Doctor

Role: STUDY_DIRECTOR

Maison de santé pluriprofessionnelle de Romilly sur Andelle

Locations

Maison de Santé Amstrong

Le Grand-Quevilly, , France

Maison de Santé des Carmes

Rouen, , France

Maison de Santé de la Plaine

Val-de-Reuil, , France

Countries

France

Central Contacts

David DM MALLET, Director

Role: CONTACT

david.mallet@chu-rouen.fr

02 32 88 82 65 ext. +33

Vincent VF FERRANTI, ARC

Role: CONTACT

vincent.ferranti@chu-rouen.fr

02 32 88 82 65 ext. +33

Other Identifiers

2022/0349/HP

Identifier Type: -

Identifier Source: org_study_id