Early Identification of Children With Asthma
ConditionAsthma in Children
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age24-71
SponsorUniversity Hospital, Rouen
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
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
Disqualifiers
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 population
Children aged 2 to 5 followed for health reasons
Trial design
Design model
Other
Time perspective
Retrospective
Treatments tested in this trial
Group of children identified by the algorithm as having asthma
Diagnostic test150 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 test150 medical files of children not identified by the algorithm as having asthma will be randomly selected for expert appraisal.
Treatment groups
No treatment groups listed
Trial outcomes
Primary outcomes
1
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)
Time frame
At enrollment visit2
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)
Time frame
At enrollment visit3
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)
Time frame
At enrollment visit4
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)
Time frame
At enrollment visitSecondary outcomes
1
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
Time frame
At enrollment visit2
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
Time frame
At enrollment visit3
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
Time frame
At enrollment visit4
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.
Time frame
At enrollment visitSponsors and contacts
Click on the lead sponsor to view all of their trials.
Source ClinicalTrials.gov
This trial is not recruiting at the moment. You can still explore other options: