Ignite Creation Date:
2025-12-25 @ 4:58 AM
Ignite Modification Date:
2025-12-26 @ 3:58 AM
Study NCT ID:
NCT06711718
Status:
RECRUITING
Last Update Posted:
2025-09-23
First Post:
2024-07-04
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Diabetes in Primary Care - Improving Classification
Sponsor:
University of Exeter
Organization:
Study Overview
Official Title:
Diabetes in Primary Care - Improving Classification (DePICtion)
Status:
RECRUITING
Status Verified Date:
2025-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
DePICtion
Brief Summary:
This study aims to evaluate the clinical utility and acceptability to patients and practitioners of running diabetes classification algorithms on primary care data to help improve diagnosis of diabetes subtypes in adults diagnosed with diabetes under the age of 50. The outputs from this research will help provide initial data on how best to use these algorithms in primary care and the optimal design of a decision support tool that could be taken forward to a full trial.
Detailed Description:
Part 1:
Using a successful approach from previous research, and experience from existing online diabetes classification calculators, we will test the feasibility of developing a decision support tool that would run the algorithms in these calculators on electronic healthcare record data at participating GP sites. We will work with a company that will develop a decision support tool that will search and extract relevant healthcare data in GP systems, run our algorithms on these data, and produce a display, highlighting patient records where there is a potential misclassification of diabetes and/or records where there are potential data quality issues (e.g. mis-coding or missing information). The decision support tool will only run on extracted data (rather than being embedded in the GP system).
Participating GP sites will be offered an introductory education session on classification of diabetes subtypes and identification of MODY (Maturity Onset Diabetes of the Young) and training on running and interpreting the decision tool.
On receipt of the outputs from the decision support tool, practice staff will be advised to review the records of potentially misclassified patients to explore any mis-codings and to consider further testing/referrals as relevant and in line with the standard clinical care pathway for diabetes.
At the end of the study, the data extraction/decision support tool may be re-run to determine whether there have been changes and whether additional testing (eg C-peptide or islet autoantibody) or referral to a diabetes specialist team has been carried out.
Part 2:
To assess the acceptability of the diabetes classification tools to potential users, and to consider how best to implement them in clinical practice long term for maximum benefit, we will explore the views and experiences of general practice teams and people with diabetes on the use of the diabetes classification tools.
A sample of clinical and admin staff at participating GP sites, and diabetes patients flagged by the tool as misclassified, will be invited to take part in a semi-structured interview about their views \& experience.
Using a successful approach from previous research, and experience from existing online diabetes classification calculators, we will test the feasibility of developing a decision support tool that would run the algorithms in these calculators on electronic healthcare record data at participating GP sites. We will work with a company that will develop a decision support tool that will search and extract relevant healthcare data in GP systems, run our algorithms on these data, and produce a display, highlighting patient records where there is a potential misclassification of diabetes and/or records where there are potential data quality issues (e.g. mis-coding or missing information). The decision support tool will only run on extracted data (rather than being embedded in the GP system).
Participating GP sites will be offered an introductory education session on classification of diabetes subtypes and identification of MODY (Maturity Onset Diabetes of the Young) and training on running and interpreting the decision tool.
On receipt of the outputs from the decision support tool, practice staff will be advised to review the records of potentially misclassified patients to explore any mis-codings and to consider further testing/referrals as relevant and in line with the standard clinical care pathway for diabetes.
At the end of the study, the data extraction/decision support tool may be re-run to determine whether there have been changes and whether additional testing (eg C-peptide or islet autoantibody) or referral to a diabetes specialist team has been carried out.
Part 2:
To assess the acceptability of the diabetes classification tools to potential users, and to consider how best to implement them in clinical practice long term for maximum benefit, we will explore the views and experiences of general practice teams and people with diabetes on the use of the diabetes classification tools.
A sample of clinical and admin staff at participating GP sites, and diabetes patients flagged by the tool as misclassified, will be invited to take part in a semi-structured interview about their views \& experience.
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: