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NCT Number: NCT06988358

Early Identification of Children With Asthma

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.

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Key information

Conditions

Asthma in Children

Age range

24 month–71 month

Sex eligibility

All sexes

Study type

Observational

Primary location

Maison de Santé Amstrong, Le Grand-Quevilly, France

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About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Group of children identified by the algorithm as having asthma

Diagnostic 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 asthma

Diagnostic Test

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

Primary outcomes

  1. 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)

  2. 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)

  3. 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)

  4. 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)

Secondary outcomes

  1. Reliability of an algorithm for the early identification of children of pre-school age (2 years)

    Time frame: At enrollment visit

    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

  2. Reliability of an algorithm for the early identification of children of pre-school age (4 years)

    Time frame: At enrollment visit

    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

  3. Reliability of an algorithm for the early identification of children of pre-school age (5 years and 11 months)

    Time frame: At enrollment visit

    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

  4. Population with asthma identified by the algorithm

    Time frame: At enrollment visit

    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.

  5. Number of asthma patients newly detected thanks to the algorithm

    Time frame: At enrollment visit

    Estimate the number of asthma patients newly detected thanks to the algorithm who were not initially detected.

  6. Estimate of the percentage of asthma patients identified using this algorithm who were not initially identified by their GPs

    Time frame: At enrollment visit

    Number of patients already identified by their GP

  7. Estimate of the percentage of asthma patients identified using this algorithm who were not initially identified by their GPs

    Time frame: At enrollment visit

    Number of patients newly identified by the algorithm and the expert group

Study contacts

Contact information is provided by the study sponsor or research team.

David DM MALLET, Director

CONTACT

[email protected]

02 32 88 82 65 ext. +33

Vincent VF FERRANTI, ARC

CONTACT

[email protected]

02 32 88 82 65 ext. +33

Sponsors and collaborators

Lead sponsor

University Hospital, Rouen

Other

Registry information

Official study title

Early Identification of Children With Asthma in Electronic Medical Records in Primary Care

Acronym: IDEA

Important dates

Study start
2025
Primary completion
2026
Study completion
2027
First posted
May 23, 2025
Registry last updated
Oct 2, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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