Diabetes in Primary Care - Improving Classification

Recruiting

• Conditions: Diabetes Mellitus

• Registration Number: NCT06711718
• Lead Sponsor: University of Exeter

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.

Study Timeline

• Study Start: 2024-06-25
• Study First Submitted: 2024-07-04
• Status Verified: 2024-11
• Study First Submitted QC: 2024-11-26
• Last Update Submitted: 2024-11-26
• Study First Posted: 2024-12-02
• Last Update Posted: 2024-12-02
• Primary Completion: 2025-01
• Study Completion: 2025-04

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 45

Inclusion Criteria

Patients

• Able to give written informed consent
• 18 years of age or over at the time of study participation
• Diabetes diagnosed at or under the age of 50
• Registered with a participating GP practice
• Sufficient understanding of the English Language to enable completion of the interview

Staff

• Able to give written informed consent
• 18 years of age or over
• Working at a participating General Practice and directly involved with the study procedures (eg running the decision support tool, responsibility for clinical care of diabetes patients)

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Exclusion Criteria

Patients

• Unable to give written informed consent
• Under 18 years of age
• No diabetes or diabetes diagnosed over the age of 50.
• Not registered with a participating GP practice
• Insufficient understanding of the English Language to enable interview completion
• Have an opt-out code where patient has declined electronic medical records examined
• Considered by their General Practitioner(s) to be inappropriate to recruit due to psycho-social reasons, participating in another related clinical trial or significant health reasons, e.g. terminal illness/diagnosis.

Staff

• Unable to give written informed consent
• Under 18 years of age
• General Practice staff not involved with the study procedures.

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Number and Proportion of patients flagged by the tool	Months 12-18.	Number and Proportion of patients flagged by the tool at each practice. Mean number of patients flagged by the tool across the practices.

Secondary Outcome Measures

Name	Time	Method
Assessment of acceptability of the DePICtion Tool	Interviews to be conducted from 2 weeks to 6 months after implementing the tool in practice.	Semi-structured, one-to-one interviews with staff and patients to evaluate (A) experiences of implementing the tool in this study and (B) views regarding the use of a diabetes classification tool in primary care in the future, including the impact and potential benefits.

Trial Locations

Locations (9)

Tamar Valley Health; 🇬🇧 Plymouth, Cornwall, United Kingdom

Ivybridge Medical Practice (Beacon Medical Group); 🇬🇧 Ivybridge, Devon, United Kingdom

Roborough Surgery; 🇬🇧 Plymouth, Devon, United Kingdom

Pathfields Medical Group; 🇬🇧 Plymouth, Devon, United Kingdom

Plympton Health Centre (Beacon Medical Group); 🇬🇧 Plymouth, Devon, United Kingdom

Chaddlewood Surgery (Beacon Medical Group); 🇬🇧 Plymouth, Devon, United Kingdom

Hucknall Road Medical Centre; 🇬🇧 Nottingham, Nottinghamshire, United Kingdom

Parkside Medical Practice; 🇬🇧 Nottingham, Nottinghamshire, United Kingdom

Chilwell Valley and Meadows Practice; 🇬🇧 Nottingham, Nottinghamshire, United Kingdom