Ignite Creation Date:
2025-12-25 @ 12:14 AM
Ignite Modification Date:
2025-12-25 @ 10:16 PM
Study NCT ID:
NCT06988358
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-10-02
First Post:
2025-05-07
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Early Identification of Children With Asthma
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 300}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-12-30', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-01', 'completionDateStruct': {'date': '2027-06-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-09-26', 'studyFirstSubmitDate': '2025-05-07', 'studyFirstSubmitQcDate': '2025-05-15', 'lastUpdatePostDateStruct': {'date': '2025-10-02', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-05-23', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Evaluating the sensitivity of an algorithm for the early identification of extracurricular children with asthma', 'timeFrame': 'At enrollment visit', 'description': "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)"}, {'measure': 'Assessing the specificity of an algorithm for the early identification of pre-school children with asthma', 'timeFrame': 'At enrollment visit', 'description': "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)"}, {'measure': 'Assessing the positive predictive value of an algorithm for the early identification of pre-school children with asthma', 'timeFrame': 'At enrollment visit', 'description': "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)"}, {'measure': 'Assessing the negative predictive value of an algorithm for the early identification of pre-school children with asthma', 'timeFrame': 'At enrollment visit', 'description': "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)"}], 'secondaryOutcomes': [{'measure': 'Reliability of an algorithm for the early identification of children of pre-school age (2 years)', 'timeFrame': 'At enrollment visit', 'description': "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"}, {'measure': 'Reliability of an algorithm for the early identification of children of pre-school age (4 years)', 'timeFrame': 'At enrollment visit', 'description': "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"}, {'measure': 'Reliability of an algorithm for the early identification of children of pre-school age (5 years and 11 months)', 'timeFrame': 'At enrollment visit', 'description': "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"}, {'measure': 'Population with asthma identified by the algorithm', 'timeFrame': 'At enrollment visit', 'description': '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.'}, {'measure': 'Number of asthma patients newly detected thanks to the algorithm', 'timeFrame': 'At enrollment visit', 'description': 'Estimate the number of asthma patients newly detected thanks to the algorithm who were not initially detected.'}, {'measure': 'Estimate of the percentage of asthma patients identified using this algorithm who were not initially identified by their GPs', 'timeFrame': 'At enrollment visit', 'description': 'Number of patients already identified by their GP'}, {'measure': 'Estimate of the percentage of asthma patients identified using this algorithm who were not initially identified by their GPs', 'timeFrame': 'At enrollment visit', 'description': 'Number of patients newly identified by the algorithm and the expert group'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Predictive algorithm'], 'conditions': ['Asthma in Children']}, 'descriptionModule': {'briefSummary': "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.\n\nHelping 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.\n\nAn 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.", 'detailedDescription': "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.\n\nDiagnosis 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.\n\nHelping 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.\n\nAn 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."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD'], 'maximumAge': '71 Months', 'minimumAge': '24 Months', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Children aged 2 to 5 followed for health reasons', 'healthyVolunteers': False, 'eligibilityCriteria': "Inclusion Criteria:\n\n* Children aged 2 years 0 days to 5 years 11 months and 30 days inclusive\n* 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.\n* At least two consultations between the ages of 2 and 5, with a general practitioner in the same care setting\n* Parents having been informed of the use of data from electronic medical records and having expressed no objection to the use of this data\n\nExclusion Criteria:\n\n* Children under 2 years of age\n* Children aged 6 years 0 days and over\n* Recourse by a patient's legal representative to one of the RGPD rights restricting the use of their data in the context of research"}, 'identificationModule': {'nctId': 'NCT06988358', 'acronym': 'IDEA', 'briefTitle': 'Early Identification of Children With Asthma', 'organization': {'class': 'OTHER', 'fullName': 'University Hospital, Rouen'}, 'officialTitle': 'Early Identification of Children With Asthma in Electronic Medical Records in Primary Care', 'orgStudyIdInfo': {'id': '2022/0349/HP'}}, 'armsInterventionsModule': {'interventions': [{'name': 'Group of children identified by the algorithm as having asthma', 'type': 'DIAGNOSTIC_TEST', 'description': '150 medical files of children identified by the algorithm as having asthma will be randomly selected for expert appraisal.'}, {'name': 'Group of children not identified by the algorithm as having asthma', 'type': 'DIAGNOSTIC_TEST', 'description': '150 medical files of children not identified by the algorithm as having asthma will be randomly selected for expert appraisal.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '76120', 'city': 'Le Grand-Quevilly', 'country': 'France', 'facility': 'Maison de Santé Amstrong', 'geoPoint': {'lat': 49.40076, 'lon': 1.04582}}, {'zip': '76000', 'city': 'Rouen', 'country': 'France', 'facility': 'Maison de Santé des Carmes', 'geoPoint': {'lat': 49.44313, 'lon': 1.09932}}, {'zip': '27690', 'city': 'Val-de-Reuil', 'country': 'France', 'facility': 'Maison de Santé de la Plaine', 'geoPoint': {'lat': 49.27385, 'lon': 1.21021}}], 'centralContacts': [{'name': 'David DM MALLET, Director', 'role': 'CONTACT', 'email': 'david.mallet@chu-rouen.fr', 'phone': '02 32 88 82 65', 'phoneExt': '+33'}, {'name': 'Vincent VF FERRANTI, ARC', 'role': 'CONTACT', 'email': 'vincent.ferranti@chu-rouen.fr', 'phone': '02 32 88 82 65', 'phoneExt': '+33'}], 'overallOfficials': [{'name': 'Charlotte CS SIEGFRIDT, Doctor', 'role': 'STUDY_DIRECTOR', 'affiliation': 'Maison de santé pluriprofessionnelle de Romilly sur Andelle'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'The data provided will be the property of the sponsor and will be used solely for its own research activities.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University Hospital, Rouen', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}