Clinical trial NCT06988358: Early Identification of Children With Asthma
Official title: Early Identification of Children With Asthma in Electronic Medical Records in Primary Care
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Asthma in Children
Keywords: Predictive algorithm
Lead sponsor: University Hospital, Rouen
Sponsor class: OTHER
First posted: 2025-05-23
Enrollment: 300
Sex: ALL
Minimum age (years): 24
Maximum age (years): 71
Standard ages: CHILD
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Children aged 2 to 5 followed for health reasons

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.

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

Primary outcomes:
measure: Evaluating the sensitivity of an algorithm for the early identification of extracurricular children with asthma
timeFrame: At enrollment visit
measure: Assessing the specificity of an algorithm for the early identification of pre-school children with asthma
timeFrame: At enrollment visit
measure: Assessing the positive predictive value of an algorithm for the early identification of pre-school children with asthma
timeFrame: At enrollment visit
measure: Assessing the negative predictive value of an algorithm for the early identification of pre-school children with asthma
timeFrame: At enrollment visit
Secondary outcomes:
measure: Reliability of an algorithm for the early identification of children of pre-school age (2 years)
timeFrame: At enrollment visit
measure: Reliability of an algorithm for the early identification of children of pre-school age (4 years)
timeFrame: At enrollment visit
measure: Reliability of an algorithm for the early identification of children of pre-school age (5 years and 11 months)
timeFrame: At enrollment visit
measure: Population with asthma identified by the algorithm
timeFrame: At enrollment visit
measure: Number of asthma patients newly detected thanks to the algorithm
timeFrame: At enrollment visit
measure: Estimate of the percentage of asthma patients identified using this algorithm who were not initially identified by their GPs
timeFrame: At enrollment visit
measure: Estimate of the percentage of asthma patients identified using this algorithm who were not initially identified by their GPs
timeFrame: At enrollment visit
Other outcomes:
Locations:
city: Le Grand Quevilly
country: France
facility: Maison de Santé Amstrong
city: Rouen
country: France
facility: Maison de Santé des Carmes
city: Val de Reuil
country: France
facility: Maison de Santé de la Plaine
Study officials:
affiliation: Maison de santé pluriprofessionnelle de Romilly sur Andelle
name: Charlotte CS SIEGFRIDT, Doctor
role: STUDY_DIRECTOR
References: