Ignite Creation Date:
2025-12-25 @ 3:21 AM
Ignite Modification Date:
2025-12-26 @ 2:00 AM
Study NCT ID:
NCT06573905
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-01-01
First Post:
2024-08-23
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Mapping Diabetes in Quebec: Validating Medico-administrative Algorithms for Type 1 Diabetes, Type 2 Diabetes and LADA
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003922', 'term': 'Diabetes Mellitus, Type 1'}, {'id': 'D003924', 'term': 'Diabetes Mellitus, Type 2'}, {'id': 'D003920', 'term': 'Diabetes Mellitus'}], 'ancestors': [{'id': 'D044882', 'term': 'Glucose Metabolism Disorders'}, {'id': 'D008659', 'term': 'Metabolic Diseases'}, {'id': 'D009750', 'term': 'Nutritional and Metabolic Diseases'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}, {'id': 'D001327', 'term': 'Autoimmune Diseases'}, {'id': 'D007154', 'term': 'Immune System Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 17271}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-01-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-12', 'completionDateStruct': {'date': '2025-06-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-12-30', 'studyFirstSubmitDate': '2024-08-23', 'studyFirstSubmitQcDate': '2024-08-23', 'lastUpdatePostDateStruct': {'date': '2025-01-01', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-08-27', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-06-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Diagnostic Accuracy Measures (Percentages)', 'timeFrame': 'Retrospective data from 1997 to 2024', 'description': 'The primary outcome measure is the accuracy of the medico-administrative algorithms in correctly classifying participants into one of the following diabetes phenotypes: Type 1, Type 2, LADA, or Other Phenotypes, compared to clinical or self-reported diagnoses.\n\n1.1. Diagnostic Accuracy Measures (Percentages)\n\n* Sensitivity (Se)\n* Specificity (Sp)\n* Positive Predictive Value (PPV)\n* Negative Predictive Value (NPV) All reported as proportions or percentages.\n\nThese indicators will not be aggregated into a single value, but will be presented separately to respect their distinct units of measurement.'}, {'measure': 'Classification Counts (Number of Cases)', 'timeFrame': 'Retrospective data from 1997 to 2024', 'description': 'The primary outcome measure is the accuracy of the medico-administrative algorithms in correctly classifying participants into one of the following diabetes phenotypes: Type 1, Type 2, LADA, or Other Phenotypes, compared to clinical or self-reported diagnoses.\n\n1.2. Classification Counts (Number of Cases)\n\n* True Positives (TP)\n* True Negatives (TN)\n* False Positives (FP)\n* False Negatives (FN) All reported as counts of participants.\n\nThese indicators will not be aggregated into a single value, but will be presented separately to respect their distinct units of measurement.'}, {'measure': 'Likelihood Ratios (Unitless)', 'timeFrame': 'Retrospective data from 1997 to 2024', 'description': 'The primary outcome measure is the accuracy of the medico-administrative algorithms in correctly classifying participants into one of the following diabetes phenotypes: Type 1, Type 2, LADA, or Other Phenotypes, compared to clinical or self-reported diagnoses.\n\n1.3. Likelihood Ratios (Unitless)\n\n* Positive Likelihood Ratio (LR+)\n* Negative Likelihood Ratio (LR-) Reported as unitless ratios.\n\nThese indicators will not be aggregated into a single value, but will be presented separately to respect their distinct units of measurement.'}], 'secondaryOutcomes': [{'measure': 'Prevalence of Each Diabetes Phenotype (Proportion/Percentage)', 'timeFrame': 'Retrospective data from 1997 to 2024', 'description': 'Prevalences of each diabetes phenotype (Type 1, Type 2, LADA, and Other Phenotypes) within the study population : Determines the proportion of individuals who have each specific diabetes phenotype (Type 1, Type 2, LADA, or Other Phenotypes) at a given point in time (Reported as a percentage or proportion).\n\nUnit of Measure: Proportion or percentage of the study population.\n\nThese calculations will provide insights into the distribution and emergence of different diabetes phenotypes within the Quebec population from 1997 to 2024, allowing for a better understanding of disease patterns and informing public health strategies and resource allocation.\n\nThese indicators will not be aggregated into a single value, but will be presented separately to respect their distinct units of measurement.'}, {'measure': 'Incidence of Each Diabetes Phenotype', 'timeFrame': 'Retrospective data from 1997 to 2024', 'description': 'Incidences of each diabetes phenotype (Type 1, Type 2, LADA, and Other Phenotypes) within the study population: Incidence (I): Calculates the rate at which new cases of each diabetes phenotype occur in the study population over the defined period (Reported as a rate or proportion).\n\nUnit of Measure: Rate of new cases (e.g., per 1,000 person-years) or proportion (cases/total population).\n\nThese calculations will provide insights into the distribution and emergence of different diabetes phenotypes within the Quebec population from 1997 to 2024, allowing for a better understanding of disease patterns and informing public health strategies and resource allocation.\n\nThese indicators will not be aggregated into a single value, but will be presented separately to respect their distinct units of measurement.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['medico-administrative algorithms', 'cohort study', 'observational study', 'diabetes'], 'conditions': ['Diabetes Mellitus, Type 1', 'Diabete Type 2', 'Diabetes;Adult Onset', 'Diabetes, Autoimmune']}, 'descriptionModule': {'briefSummary': 'The goal of this observational study is to validate medico-administrative algorithms that classify diabetes phenotypes (Type 1, Type 2, and Latent Autoimmune Diabetes in Adults - LADA) in a population-based cohort in Quebec, including children, adolescents, and young adults up to 40 years old with diagnosed diabetes. The main questions it aims to answer are:\n\nCan these algorithms accurately distinguish between Type 1, Type 2, and LADA across different age groups? What is the prevalence and incidence of each diabetes phenotype in Quebec? Participants will have their medical and administrative data analyzed, including data on medication usage and healthcare visits, to validate the accuracy of the algorithms. The study will involve comparing these algorithm-based classifications with clinical diagnoses or self-reported data to ensure reliability.', 'detailedDescription': 'The goal of this observational study is to validate the effectiveness of medico-administrative algorithms developed to classify diabetes phenotypes, specifically Type 1, Type 2, and Latent Autoimmune Diabetes in Adults (LADA), in a population-based cohort in Quebec. The study focuses on children, adolescents, and young adults up to 40 years old who have been diagnosed with diabetes.\n\nThe main questions it aims to answer are:\n\nCan these algorithms accurately differentiate between Type 1, Type 2, and LADA across various age groups? What are the prevalence and incidence rates of these diabetes phenotypes in the Quebec population? Participants, who are already diagnosed with one of the three diabetes types and receiving standard medical care, will have their data collected from existing medical and administrative records. This data includes information on medication usage, healthcare visits, and self-reported health outcomes.\n\nThe study will involve a retrospective analysis where the classifications made by the algorithms will be compared with clinical diagnoses and self-reported data to determine the accuracy and reliability of the algorithms. This validation process is crucial for improving diabetes management and public health strategies by ensuring that these algorithms can be reliably used in broader epidemiological studies.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT'], 'maximumAge': '40 Years', 'minimumAge': '1 Year', 'genderBased': True, 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'The study population consists of individuals diagnosed with Type 1, Type 2, or LADA diabetes, as well as other diabetes-related phenotypes, within the province of Quebec. The population includes children, adolescents, and young adults up to 40 years of age at the time of diagnosis. The cohort is drawn from a comprehensive dataset of medical, self-reported, and medico-administrative records spanning from 1997 to 2024. This diverse population allows for a robust validation of the diabetes classification algorithms across different age groups and phenotypes, providing valuable insights into the epidemiology of diabetes in Quebec.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Individuals diagnosed with Type 1, Type 2, or Latent Autoimmune Diabetes in Adults (LADA) based on clinical or self-reported data.\n* Participants diagnosed between 1997 and 2024.\n* Residents of Quebec with available medico-administrative records from 1997 to 2024.\n\nExclusion Criteria:\n\n* Non-residents of Quebec during the study period.'}, 'identificationModule': {'nctId': 'NCT06573905', 'acronym': 'VDA', 'briefTitle': 'Mapping Diabetes in Quebec: Validating Medico-administrative Algorithms for Type 1 Diabetes, Type 2 Diabetes and LADA', 'organization': {'class': 'OTHER', 'fullName': 'Universite du Quebec en Outaouais'}, 'officialTitle': 'Mapping Diabetes in Quebec: Validating Medico-administrative Algorithms for Type 1 Diabetes, Type 2 Diabetes and LADA', 'orgStudyIdInfo': {'id': 'F1-14464'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'type 1 diabetes', 'description': "This group comprises participants diagnosed with Type 1 diabetes according to self-reported data. The primary goal of comparing this group with medico-administrative records is to validate the algorithm's ability to accurately classify individuals with Type 1 diabetes, ensuring that they are correctly identified as such without being misclassified into other categories.", 'interventionNames': ['Other: no intervention']}, {'label': 'type 2 diabetes', 'description': "This group includes participants diagnosed with Type 2 diabetes based on clinical data. The validation process focuses on assessing the algorithm's accuracy in identifying individuals with Type 2 diabetes, ensuring correct classification and minimizing the risk of misclassification as other diabetes phenotypes or non-diabetic.", 'interventionNames': ['Other: no intervention']}, {'label': 'Latent autoimmune diabete in adults', 'description': "This group consists of participants diagnosed with Latent Autoimmune Diabetes in Adults (LADA) according to self-reported data. The validation process for this group focuses on assessing the algorithm's ability to accurately identify individuals with LADA, which is often challenging due to its characteristics that overlap with both Type 1 and Type 2 diabetes. Accurate classification of LADA is crucial for improving treatment strategies and understanding its epidemiology.", 'interventionNames': ['Other: no intervention']}, {'label': 'Non-diabetic', 'description': "This group includes participants who, according to self-reported data from individuals, do not have any phenotypes of diabetes. The comparison of this group's data with medico-administrative records is crucial for identifying false positives and ensuring that the algorithms accurately exclude non-diabetic individuals from being misclassified as having diabetes."}, {'label': 'other phenotypes', 'description': "This group contains participants diagnosed with diabetes-related phenotypes other than Type 1, Type 2, or LADA, as well as those with rarer forms of the disease (based on clinical data). The validation aims to determine the algorithm's effectiveness in correctly identifying and classifying these less common phenotypes, which is critical for ensuring comprehensive and accurate diabetes classification.", 'interventionNames': ['Other: no intervention']}], 'interventions': [{'name': 'no intervention', 'type': 'OTHER', 'description': 'no intervention. this is observational study.', 'armGroupLabels': ['Latent autoimmune diabete in adults', 'other phenotypes', 'type 1 diabetes', 'type 2 diabetes']}]}, 'contactsLocationsModule': {'locations': [{'zip': 'J8X 3X7', 'city': 'Montreal', 'state': 'Quebec', 'country': 'Canada', 'contacts': [{'name': 'philippe c corsenac, Ph.D', 'role': 'CONTACT', 'email': 'philippe.corsenac@uqo.ca', 'phone': '(+1)4384934299'}, {'name': 'philippe c corsenac, Ph.D', 'role': 'CONTACT'}], 'facility': 'Philippe Corsenac', 'geoPoint': {'lat': 45.50884, 'lon': -73.58781}}], 'centralContacts': [{'name': 'philippe C corsenac, Ph.D', 'role': 'CONTACT', 'email': 'philippe.corsenac@uqo.ca', 'phone': '(+1)4384934299'}, {'name': 'jeremie Riou, Ph.D', 'role': 'CONTACT', 'email': 'jeremie.riou@univ-angers.fr'}], 'overallOfficials': [{'name': 'philippe C corsenac, Ph.D', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'UQO'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'no planned'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Universite du Quebec en Outaouais', 'class': 'OTHER'}, 'collaborators': [{'name': 'McGill University', 'class': 'OTHER'}, {'name': "Centre de Recherche du Centre Hospitalier de l'Université de Montréal", 'class': 'OTHER'}, {'name': 'University Hospital, Angers', 'class': 'OTHER_GOV'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Dr in epidemiology and immunology', 'investigatorFullName': 'Corsenac Philippe', 'investigatorAffiliation': 'Universite du Quebec en Outaouais'}}}}