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NCT Number: NCT06573905

Mapping Diabetes in Quebec: Validating Medico-administrative Algorithms for Type 1 Diabetes, Type 2 Diabetes and LADA

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.

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Key information

Conditions

Diabetes Mellitus, Type 1 Autoimmune Diseases Diabete Type 2 Diabetes Mellitus Diabetes Mellitus, Type 2 Diabetes, Autoimmune Diabetes;Adult Onset Endocrine System Diseases Glucose Metabolism Disorders Immune System Diseases Metabolic Diseases Nutritional and Metabolic Diseases

Age range

1 year–40 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Philippe Corsenac

Montreal, Quebec, J8X 3X7, Canada

Location contact

philippe c corsenac, Ph.D

CONTACT

[email protected]

(+1)4384934299

philippe c corsenac, Ph.D

CONTACT

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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.

Exclusion criteria

  • Non-residents of Quebec during the study period.

Treatment and study plan

No intervention

Other

no intervention. this is observational study.

Primary outcomes

  1. 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.

  2. 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.

  3. 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.

Secondary outcomes

  1. Prevalence of Each Diabetes Phenotype (Proportion/Percentage)

    Time frame: Retrospective data from 1997 to 2024

    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.

  2. Incidence of Each Diabetes Phenotype

    Time frame: Retrospective data from 1997 to 2024

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

jeremie Riou, Ph.D

CONTACT

[email protected]

philippe C corsenac, Ph.D

CONTACT

[email protected]

(+1)4384934299

Sponsors and collaborators

Lead sponsor

Universite du Quebec en Outaouais

Other

Collaborators

  • Centre de Recherche du Centre Hospitalier de l'Université de Montréal
  • McGill University
  • University Hospital, Angers

Registry information

Acronym: VDA

Important dates

Study start
2025
Primary completion
2025
Study completion
2025
First posted
Aug 27, 2024
Registry last updated
Jan 1, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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