NCT06711718

Diabetes in Primary Care - Improving Classification

Diabetes in Primary Care - Improving Classification (DePICtion)

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 45 (estimated)
• Sponsor: University of Exeter · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

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.

Conditions

• Diabetes Mellitus

Interventions

Type	Name	Description
OTHER	Qualitative interview (patients)	Qualitative interviews to be carried out by an experienced researcher with patients who have had their diabetes diagnosis reviewed as a result of the running of the tool.
OTHER	Qualitative interview (staff)	Qualitative interviews to be carried out by an experienced researcher with practice staff who have run the tool and reviewed patients flagged by the tool.

Timeline

• Start date: 2024-06-25
• Primary completion: 2025-10-01
• Completion: 2026-03-31
• First posted: 2024-12-02
• Last updated: 2025-09-23

Locations

9 sites across 1 country: United Kingdom