Ignite Creation Date:
2024-10-26 @ 3:38 PM
Last Modification Date:
2024-10-26 @ 3:38 PM
Study NCT ID:
NCT06573905
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-08-23
Brief Title:
Mapping Diabetes in Quebec Validating Medico-administrative Algorithms for Type 1 Diabetes Type 2 Diabetes and LADA
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Mapping Diabetes in Quebec Validating Medico-administrative Algorithms for Type 1 Diabetes Type 2 Diabetes and LADA
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
VDA
Brief 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 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
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
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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None