Diabetes Mellitus, Type 1VDANCT06573905
Mapping Diabetes in Quebec: Validating Medico-administrative Algorithms for Type 1 Diabetes, Type 2 Diabetes and LADA
An observational study of Diabetes Mellitus, Type 1 and Diabete Type 2, run by Universite du Quebec en Outaouais at 1 site in Canada.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 1 year to 40 years
- Conditions the study is for
- Diabetes Mellitus, Type 1, Diabete Type 2, Diabetes;Adult Onset
- How many people
- 17,271 participants
- Where
- 1 siteCanada
- Study dates
- 1 Jan 2025to 30 Jun 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 1 year to 40 years
- All sexes
- No healthy volunteers
- Child, Adult
Study population: 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.
Who may be able to join
- Individuals diagnosed with Type 1, Type 2, or Latent Autoimmune Diabetes in Adults (LADA) based on clinical or self-reported data.
- Participants diagnosed between 1997 and 2024.
- Residents of Quebec with available medico-administrative records from 1997 to 2024.
Who cannot join
- Non-residents of Quebec during the study period.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
jeremie Riou, Ph.D contact
Canada1 site
Philippe Corsenac
Montreal, Quebec, J8X 3X7
philippe c corsenac, Ph.D, (+1)4384934299, philippe.corsenac@uqo.ca
philippe c corsenac, Ph.D
About this study
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:
Can 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.
Read the detailed description
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.
The main questions it aims to answer are:
Can 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.
The 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.
- Being tested
- no intervention
- Keywords
- medico-administrative algorithms, cohort study, observational study, diabetes
- Medical subject headings
- Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Diabetes Mellitus
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
no interventionOther
no intervention. this is observational study.
| Study group | Role | What they receive |
|---|---|---|
| type 1 diabetes | 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. Receives: Other: no intervention | |
| type 2 diabetes | 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. Receives: Other: no intervention | |
| Latent autoimmune diabete in adults | 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. Receives: Other: no intervention | |
| Non-diabetic | 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. | |
| other phenotypes | 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. Receives: Other: no intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy Measures (Percentages)
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.
1.1. Diagnostic Accuracy Measures (Percentages)
- Sensitivity (Se)
- Specificity (Sp)
- Positive Predictive Value (PPV)
- Negative Predictive Value (NPV) All reported as proportions or percentages.
These indicators will not be aggregated into a single value, but will be presented separately to respect their distinct units of measurement.
Measured: Retrospective data from 1997 to 2024
Classification Counts (Number of Cases)
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.
1.2. Classification Counts (Number of Cases)
- True Positives (TP)
- True Negatives (TN)
- False Positives (FP)
- False Negatives (FN) All reported as counts of participants.
These indicators will not be aggregated into a single value, but will be presented separately to respect their distinct units of measurement.
Measured: Retrospective data from 1997 to 2024
Likelihood Ratios (Unitless)
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.
1.3. Likelihood Ratios (Unitless)
- Positive Likelihood Ratio (LR+)
- Negative Likelihood Ratio (LR-) Reported as unitless ratios.
These indicators will not be aggregated into a single value, but will be presented separately to respect their distinct units of measurement.
Measured: Retrospective data from 1997 to 2024
Secondary outcomes (2)
Prevalence of Each Diabetes Phenotype (Proportion/Percentage)
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).
Unit of Measure: Proportion or percentage of the study population.
These 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.
These indicators will not be aggregated into a single value, but will be presented separately to respect their distinct units of measurement.
Measured: Retrospective data from 1997 to 2024
Incidence of Each Diabetes Phenotype
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).
Unit of Measure: Rate of new cases (e.g., per 1,000 person-years) or proportion (cases/total population).
These 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.
These indicators will not be aggregated into a single value, but will be presented separately to respect their distinct units of measurement.
Measured: Retrospective data from 1997 to 2024
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Aug 2024
- Study start1 Jan 2025 (estimated)
- Primary completion30 Jun 2025 (estimated)
- Study completion30 Jun 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Diabetes Mellitus, Type 1Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 17,271 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Universite du Quebec en Outaouais
- Sponsor type
- University, hospital or other
- Collaborators
- McGill University, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, University Hospital, Angers
- Responsible party
- Principal investigator, Corsenac Philippe, Universite du Quebec en Outaouais
- Investigators
- philippe C corsenac, Ph.D, Principal investigator, UQO
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06573905
- Sponsor's protocol ID
- F1-14464
- Acronym
- VDA
- Status last verified
- Dec 2024
- First submitted
- 23 Aug 2024
- Last updated
- 1 Jan 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: no planned
Publications
Corsenac P, Brazeau AS, Riou J, Taleb N, Jena SD, Barnett TA. Retrospective population-based cohorts for assessing the performance of algorithmic diabetes classification and for quantifying the true burden of type 1, type 2 and LADA phenotypes in Quebec: a study protocol. BMJ Open. 2026 Aug 31;16(8):e109209. doi: 10.1136/bmjopen-2025-109209.
PubMed 42674789
Study at a glance
From the registry record, last updated by the sponsor on 1 Jan 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Mapping Diabetes in Quebec: Validating Medico-administrative Algorithms for Type 1 Diabetes, Type 2 Diabetes and LADA
- ClinicalTrials.gov ID
- NCT06573905
- Acronym / sponsor ID
- VDA / F1-14464
- Keywords
- medico-administrative algorithms, cohort study, observational study, diabetes
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 17,271 participants (estimated)
- Lead sponsor
- Universite du Quebec en OutaouaisUniversity, hospital or other
- Collaborators
- McGill University, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, University Hospital, Angers
- Study start
- 1 Jan 2025 (estimated)
- Primary completion
- 30 Jun 2025 (estimated)
- Study completion
- 30 Jun 2025 (estimated)
- First posted
- 27 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
Can 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.
Detailed description
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.
The main questions it aims to answer are:
Can 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.
The 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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 5
| Arm | Type | Receives |
|---|---|---|
| type 1 diabetes | – | Other: no interventionThis 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. |
| type 2 diabetes | – | Other: no interventionThis 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. |
| Latent autoimmune diabete in adults | – | Other: no interventionThis 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. |
| Non-diabetic | – | –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. |
| other phenotypes | – | Other: no interventionThis 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. |
Who may qualify
- Ages
- 1 year to 40 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult
- Study population
- 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.
Key inclusion criteria (3 listed)
- Individuals diagnosed with Type 1, Type 2, or Latent Autoimmune Diabetes in Adults (LADA) based on clinical or self-reported data.
- Participants diagnosed between 1997 and 2024.
- Residents of Quebec with available medico-administrative records from 1997 to 2024.
Key exclusion criteria (1 listed)
- Non-residents of Quebec during the study period.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| no interventionno intervention. this is observational study. | Other | Latent autoimmune diabete in adults, other phenotypes, type 1 diabetes, type 2 diabetes |
Primary outcome
Diagnostic Accuracy Measures (Percentages)
Time frame: Retrospective data from 1997 to 2024
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. 1.1. Diagnostic Accuracy Measures (Percentages) * Sensitivity (Se) * Specificity (Sp) * Positive Predictive Value (PPV) * Negative Predictive Value (NPV) All reported as proportions or percentages. These indicators will not be aggregated into a single value, but will be presented separately to respect their distinct units of measurement.
Classification Counts (Number of Cases)
Time frame: Retrospective data from 1997 to 2024
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. 1.2. Classification Counts (Number of Cases) * True Positives (TP) * True Negatives (TN) * False Positives (FP) * False Negatives (FN) All reported as counts of participants. These indicators will not be aggregated into a single value, but will be presented separately to respect their distinct units of measurement.
Likelihood Ratios (Unitless)
Time frame: Retrospective data from 1997 to 2024
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. 1.3. Likelihood Ratios (Unitless) * Positive Likelihood Ratio (LR+) * Negative Likelihood Ratio (LR-) Reported as unitless ratios. These indicators will not be aggregated into a single value, but will be presented separately to respect their distinct units of measurement.
2 secondary outcomes
Prevalence of Each Diabetes Phenotype (Proportion/Percentage)
Time frame: Retrospective data from 1997 to 2024
Incidence of Each Diabetes Phenotype
Time frame: Retrospective data from 1997 to 2024
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Jan 2025.
- Derived from this study: PubMed 42674789Corsenac P, Brazeau AS, Riou J, Taleb N, Jena SD, Barnett TA. Retrospective population-based cohorts for assessing the performance of algorithmic diabetes classification and for quantifying the true burden of type 1, type 2 and LADA phenotypes in Quebec: a study protocol. BMJ Open. 2026 Aug 31;16(8):e109209. doi: 10.1136/bmjopen-2025-109209.
- Individual participant dataSharing planned: No. no planned
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
jeremie Riou, Ph.D contact
philippe C corsenac, Ph.D principal investigator
UQO
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Diabetes Mellitus, Type 1 and Diabete Type 2, run by Universite du Quebec en Outaouais at 1 site in Canada.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 1 Jan 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Canada. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact philippe C corsenac, Ph.D ((+1)4384934299, philippe.corsenac@uqo.ca), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy Measures (Percentages) (time frame: Retrospective data from 1997 to 2024). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06573905, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Jan 2025.