Asthma in ChildrenIDEANCT06988358
Early Identification of Children With Asthma
An observational study of Asthma in Children, run by University Hospital, Rouen at 3 sites in France.
Trial 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. Read more about this study
- Who is it for?
- Ages 24 to 71 months
- Conditions the study is for
- Asthma in Children
- What is being tested?
- Group of children identified by the algorithm as having asthma and Group of children not identified by the algorithm as having asthma
- How many people
- 300 participants
- Where
- 3 sitesFrance
- Study dates
- 30 Dec 2025to 30 Jun 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 24 to 71 months
- All sexes
- No healthy volunteers
- Child
Study population: Children aged 2 to 5 followed for health reasons
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
3 sites in 1 country. Contact the study team or a site near you to ask about taking part.
France3 sites
Maison de Santé Amstrong
Le Grand-Quevilly, 76120
Maison de Santé des Carmes
Rouen, 76000
Maison de Santé de la Plaine
Val-de-Reuil, 27690
About this study
Official title: Early Identification of Children With Asthma in Electronic Medical Records in Primary Care
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.
Read the 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
- Being tested
- Group of children identified by the algorithm as having asthma, Group of children not identified by the algorithm as having asthma
- Keywords
- Predictive algorithm
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Group of children identified by the algorithm as having asthmaDiagnostic test
150 medical files of children identified by the algorithm as having asthma will be randomly selected for expert appraisal.
Group of children not identified by the algorithm as having asthmaDiagnostic test
150 medical files of children not identified by the algorithm as having asthma will be randomly selected for expert appraisal.
What is being measured
The primary outcome is the main question the study is designed to answer.
Evaluating the sensitivity of an algorithm for the early identification of extracurricular children with asthma
Evaluate the algorithm's predictions against expert opinion to estimate the Sensitivity of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives)
Measured: At enrollment visit
Assessing the specificity of an algorithm for the early identification of pre-school children with asthma
Evaluate the algorithm's predictions against expert opinion to estimate the Specificity of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives)
Measured: At enrollment visit
Assessing the positive predictive value of an algorithm for the early identification of pre-school children with asthma
Evaluate the algorithm's predictions against expert opinion to estimate the positive predictive value of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives)
Measured: At enrollment visit
Assessing the negative predictive value of an algorithm for the early identification of pre-school children with asthma
Evaluate the algorithm's predictions against expert opinion to estimate the negative predictive value of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives)
Measured: At enrollment visit
Secondary outcomes (7)
Reliability of an algorithm for the early identification of children of pre-school age (2 years)
Evaluate the algorithm's predictions against expert opinion to estimate the negative predictive value, the positive value, the specificity and the sensibilité of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives) of children of pre-school age (2 years) with asthma
Measured: At enrollment visit
Reliability of an algorithm for the early identification of children of pre-school age (4 years)
Evaluate the algorithm's predictions against expert opinion to estimate the negative predictive value, the positive value, the specificity and the sensibilité of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives) of children of pre-school age (4 years) with asthma
Measured: At enrollment visit
Reliability of an algorithm for the early identification of children of pre-school age (5 years and 11 months)
Evaluate the algorithm's predictions against expert opinion to estimate the negative predictive value, the positive value, the specificity and the sensibilité of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives) of children of pre-school age (5 years and 11 months) with asthma
Measured: At enrollment visit
Population with asthma identified by the algorithm
Describe the population identified by the algorithm and the experts, and compare it with patients already identified as having asthma (in their history) by their GP.
Measured: At enrollment visit
Number of asthma patients newly detected thanks to the algorithm
Estimate the number of asthma patients newly detected thanks to the algorithm who were not initially detected.
Measured: At enrollment visit
Estimate of the percentage of asthma patients identified using this algorithm who were not initially identified by their GPs
Number of patients already identified by their GP
Measured: At enrollment visit
Estimate of the percentage of asthma patients identified using this algorithm who were not initially identified by their GPs
Number of patients newly identified by the algorithm and the expert group
Measured: At enrollment visit
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 May 2025
- Study start30 Dec 2025 (estimated)
- Primary completion31 Dec 2026 (estimated)
- Study completion30 Jun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Asthma in ChildrenStudies of the same condition, open and under way.
- More trials run by University Hospital, RouenThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University Hospital, Rouen
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Charlotte CS SIEGFRIDT, Doctor, Study director, Maison de santé pluriprofessionnelle de Romilly sur Andelle
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06988358
- Sponsor's protocol ID
- 2022/0349/HP
- Acronym
- IDEA
- Status last verified
- Jan 2025
- First submitted
- 7 May 2025
- Last updated
- 2 Oct 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The data provided will be the property of the sponsor and will be used solely for its own research activities.
Study at a glance
From the registry record, last updated by the sponsor on 2 Oct 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Early Identification of Children With Asthma in Electronic Medical Records in Primary Care
- ClinicalTrials.gov ID
- NCT06988358
- Acronym / sponsor ID
- IDEA / 2022/0349/HP
- Conditions
- Asthma in Children
- Keywords
- Predictive algorithm
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- University Hospital, RouenUniversity, hospital or other
- Study start
- 30 Dec 2025 (estimated)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 30 Jun 2027 (estimated)
- First posted
- 23 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Ages
- 24 to 71 months
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child
- Study population
- Children aged 2 to 5 followed for health reasons
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Group of children identified by the algorithm as having asthma150 medical files of children identified by the algorithm as having asthma will be randomly selected for expert appraisal. | Diagnostic test | – |
| Group of children not identified by the algorithm as having asthma150 medical files of children not identified by the algorithm as having asthma will be randomly selected for expert appraisal. | Diagnostic test | – |
Primary outcome
Evaluating the sensitivity of an algorithm for the early identification of extracurricular children with asthma
Time frame: At enrollment visit
Evaluate the algorithm's predictions against expert opinion to estimate the Sensitivity of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives)
Assessing the specificity of an algorithm for the early identification of pre-school children with asthma
Time frame: At enrollment visit
Evaluate the algorithm's predictions against expert opinion to estimate the Specificity of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives)
Assessing the positive predictive value of an algorithm for the early identification of pre-school children with asthma
Time frame: At enrollment visit
Evaluate the algorithm's predictions against expert opinion to estimate the positive predictive value of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives)
Assessing the negative predictive value of an algorithm for the early identification of pre-school children with asthma
Time frame: At enrollment visit
Evaluate the algorithm's predictions against expert opinion to estimate the negative predictive value of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives)
7 secondary outcomes
Reliability of an algorithm for the early identification of children of pre-school age (2 years)
Time frame: At enrollment visit
Reliability of an algorithm for the early identification of children of pre-school age (4 years)
Time frame: At enrollment visit
Reliability of an algorithm for the early identification of children of pre-school age (5 years and 11 months)
Time frame: At enrollment visit
Population with asthma identified by the algorithm
Time frame: At enrollment visit
Number of asthma patients newly detected thanks to the algorithm
Time frame: At enrollment visit
Estimate of the percentage of asthma patients identified using this algorithm who were not initially identified by their GPs
Time frame: At enrollment visit
Estimate of the percentage of asthma patients identified using this algorithm who were not initially identified by their GPs
Time frame: At enrollment visit
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Oct 2025.
- Individual participant dataSharing planned: No. The data provided will be the property of the sponsor and will be used solely for its own research activities.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Charlotte CS SIEGFRIDT, Doctor study director
Maison de santé pluriprofessionnelle de Romilly sur Andelle
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Asthma in Children, run by University Hospital, Rouen at 3 sites in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 2 Oct 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact David DM MALLET, Director (02 32 88 82 65 ext. +33, david.mallet@chu-rouen.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Evaluating the sensitivity of an algorithm for the early identification of extracurricular children with asthma (time frame: At enrollment visit). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Oct 2025.