Early Identification of Children With Asthma

NCT06988358 | Not Yet Recruiting

• 1 other identifier: 2022/0349/HP
• Study Type: observational
• Target: 300
• Locations: 1 country
• Sites: 3

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.

Conditions

Asthma in Children

Keywords

Predictive algorithm

Outcome Measures

Primary Outcomes (4)

• 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)

  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)

  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)

  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)

  At enrollment visit

Secondary Outcomes (7)

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

  At enrollment visit

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

  At enrollment visit

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

  At enrollment visit

• Population with asthma identified by the algorithm

  At enrollment visit

• Number of asthma patients newly detected thanks to the algorithm

  At enrollment visit

Interventions

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.

Eligibility Criteria

• Age: 24 Months - 71 Months
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17)
• Sampling Method: Probability Sample

Study Population

Children aged 2 to 5 followed for health reasons

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• University Hospital, Rouen: lead

Study Sites (3)

Maison de Santé Amstrong

Le Grand-Quevilly, 76120, France

Location

Maison de Santé des Carmes

Rouen, 76000, France

Location

Maison de Santé de la Plaine

Val-de-Reuil, 27690, France

Location

Study Officials

• Charlotte CS SIEGFRIDT, Doctor

  Maison de santé pluriprofessionnelle de Romilly sur Andelle

  STUDY DIRECTOR

Central Study Contacts

David DM MALLET, Director

CONTACT

02 32 88 82 65; david.mallet@chu-rouen.fr

Vincent VF FERRANTI, ARC

CONTACT

02 32 88 82 65; vincent.ferranti@chu-rouen.fr

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 7, 2025
• First Posted: May 23, 2025
• Study Start: December 30, 2025
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): June 30, 2027
• Last Updated: October 2, 2025

Record last verified: 2025-01

Data Sharing

• IPD Sharing: Will not share

Locations

FR (3)