Early Identification of Children With Asthma

Not yet recruiting

• Conditions: Asthma in Children

• Registration Number: NCT06988358
• Lead Sponsor: University Hospital, Rouen

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.

Study Timeline

• Status Verified: 2025-01
• Study First Submitted: 2025-05-07
• Study First Submitted QC: 2025-05-15
• Last Update Submitted: 2025-05-15
• Study First Posted: 2025-05-23
• Last Update Posted: 2025-05-23
• Study Start: 2025-06-30
• Primary Completion: 2026-12-31
• Study Completion: 2027-06-30

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 300

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Evaluating the sensitivity of an algorithm for the early identification of extracurricular children with asthma	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	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	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	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 Outcome Measures

Name	Time	Method
Estimate of the percentage of asthma patients identified using this algorithm who were not initially identified by their GPs	At enrollment visit	Number of patients newly identified by the algorithm and the expert group
Number of asthma patients newly detected thanks to the algorithm	At enrollment visit	Estimate the number of asthma patients newly detected thanks to the algorithm who were not initially detected.
Reliability of an algorithm for the early identification of children of pre-school age (2 years)	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
Reliability of an algorithm for the early identification of children of pre-school age (4 years)	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
Reliability of an algorithm for the early identification of children of pre-school age (5 years and 11 months)	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
Population with asthma identified by the algorithm	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.

Trial Locations

Locations (3)

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