Evidence map›Paper›PMID 40632448›Full record

ArticleDiabetes therapy : research, treatment and education of diabetes and related disorders2025

Improving Diabetes Monitoring in People with Sub-optimally Controlled Diabetes: Implementing a Clinical Laboratory-Led Quality Improvement Initiative in General Practice.

David Holland, Ian Halsall, Adrian H Heald, Mike Stedman, Fahmy W F Hanna, Anthony A Fryer

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Article in Diabetes therapy : research, treatment and education of diabetes and related disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

David HollandThe Benchmarking Partnership, Smart Innovation Hub, Keele University, Keele, Staffordshire, ST5 5BG, UK.
Ian HalsallDepartment of Clinical Biochemistry, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK.
Adrian H HealdDepartment of Diabetes and Endocrinology, Salford Royal Hospital, The Northern Care Alliance NHS Foundation Trust, Salford, M6 8HD, UK.ORCID http://orcid.org/0000-0002-9537-4050
Mike StedmanRes Consortium, Andover, SP10 5RG, UK.ORCID http://orcid.org/0000-0002-0491-7823
Fahmy W F HannaDepartment of Diabetes and Endocrinology, University Hospitals of North Midlands NHS Trust, Stoke-on-Trent, Staffordshire, ST4 6QG, UK.ORCID http://orcid.org/0000-0001-5800-1442
Anthony A FryerSchool of Medicine, Keele University, Keele, Staffordshire, ST5 5BG, UK. a.a.fryer@keele.ac.uk.ORCID http://orcid.org/0000-0001-8678-0404

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe appropriate use of glycated haemoglobin (HbA1c), the international standard for assessing overall glycaemic status in diabetes mellitus, is critical to ensuring optimal clinical outcome and minimise complications. We describe a clinical laboratory-led general practice service development to facilitate targeted follow-up in high-risk patients.

methodsThe service development comprised monthly reports identifying high-risk individuals (HbA1c ≥ 58 mmol/mol) overdue HbA1c testing, sent by the Clinical Biochemistry Laboratory to general practices in the 'Intervention' group (n = 60). A 'Non-intervention' group comprised 51 practices not sent the reports. Comparisons comprised: (i) intervention vs non-intervention groups during two time periods (pre-intervention: 2017-2020; 275,843 tests; 59,206 patients, and post-intervention: 2020-2023; 307,525 tests; 65,449 patients), (ii) pre- versus post-intervention in each group.

resultsThe intervention group (vs non-intervention group) showed larger net change in proportion overdue a test (- 20.6% vs - 10.3%, p < 0.001), particularly in the > 75 mmol/mol category (7.8-fold larger improvement, p < 0.001). Improvements were also observed in median time overdue in the 58-75 mmol/mol group (- 14.3 vs - 4.3%; p = 0.027). Improvements were identified in median HbA1c (p = 0.012) and overall proportion with HbA1c ≥ 58 mmol/mol (p = 0.037), compared with the non-intervention group, despite performing more tests/patient/year (p < 0.001). All remained significant after adjustment for practice characteristics (age, sex, social deprivation, list size, diabetes prevalence) and pre-intervention levels.

conclusionsOur findings indicate that clinical laboratories can support general practices facilitating targeting monitoring to high-risk patients. Providing succinct reports that identify patients overdue for testing can reduce the number of such patients, thereby improving diabetes control and increasing the achievement of target levels.

Indexed as

Clinical biochemistryDiabetes mellitusGlycated haemoglobinHbA1cLaboratory

Identifiers

PMID40632448
PMCPMC12399494

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.