Early Identification of Children With Asthma
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Group of children identified by the algorithm as having asthma, Group of children not identified by the algorithm as having asthma.
- Who it may be relevant to
- Registry conditions: Asthma in Children. Basic parameters: 24 months — 71 months · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- France
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Early Identification of Children With Asthma in Electronic Medical Records in Primary Care
Overview
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.
Interventions
- Diagnostic test Group of children identified by the algorithm as having asthma
150 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 asthma
150 medical files of children not identified by the algorithm as having asthma will be randomly selected for expert appraisal.
Primary outcome measures
- Evaluating the sensitivity of an algorithm for the early identification of extracurricular children with asthma [Time frame: At enrollment visit]
- Assessing the specificity of an algorithm for the early identification of pre-school children with asthma [Time frame: At enrollment visit]
- Assessing the positive predictive value of an algorithm for the early identification of pre-school children with asthma [Time frame: At enrollment visit]
- Assessing the negative predictive value of an algorithm for the early identification of pre-school children with asthma [Time frame: At enrollment visit]
Secondary outcome measures (7)
- 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]
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Other
Study locations
France · 3 centers
- Maison de Santé Amstrong — Le Grand-Quevilly
- Maison de Santé des Carmes — Rouen
- Maison de Santé de la Plaine — Val-de-Reuil
Identifiers
NCT: NCT06988358 · 2022/0349/HP