Evidence map›Paper›PMID 41360624›Full record

Observational studyBMJ open diabetes research & care2025

Identifying interhospital variation in hyperosmolar hyperglycemic syndrome (HHS) care: development and outcomes of the DEKODE HHS model.

Tania M Kew, Aspasia Manta, Jhanvi Pravesh Sawlani, Amanda Ling Jie Yee, Amar Mann, Lakshmi Narayanan, Eleni Armeni, Gerry Rayman, Ketan Dhatariya, Punith Kempegowda and 1 more

Abstract readObservational StudyMulticenter Study
In one paragraph

Observational study in BMJ open diabetes research & care, 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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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Tania M KewDepartment of Diabetes and Endocrinology, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.ORCID http://orcid.org/0000-0001-9363-3760
Aspasia MantaDepartment of Applied Health Sciences, School of Health Sciences, College of Medicine and Health, University of Birmingham, Birmingham, UK.ORCID http://orcid.org/0000-0002-3420-1420
Jhanvi Pravesh SawlaniDepartment of Diabetes and Endocrinology, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.ORCID http://orcid.org/0000-0002-1054-5325
Amanda Ling Jie YeeBirmingham Medical School, University of Birmingham, Birmingham, UK.ORCID http://orcid.org/0000-0001-9235-2836
Amar MannDepartment of Diabetes and Endocrinology, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.ORCID http://orcid.org/0000-0002-7972-4794
Lakshmi NarayananDepartment of Diabetes and Endocrinology, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.ORCID http://orcid.org/0009-0009-7894-3133
Eleni ArmeniDepartment of Applied Health Sciences, School of Health Sciences, College of Medicine and Health, University of Birmingham, Birmingham, UK.
Gerry RaymanDepartment of Diabetes and Endocrinology, Ipswich Hospital, East Suffolk and North Essex NHS Foundation Trust, Colchester, UK.
Ketan DhatariyaDepartment of Diabetes and Endocrinology, Norfolk and Norwich University Hospital, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK.ORCID http://orcid.org/0000-0003-3619-9579
Punith KempegowdaDepartment of Diabetes and Endocrinology, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK p.kempegowda@bham.ac.uk.ORCID http://orcid.org/0000-0003-0954-6512
DEVI Collaboration

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionHyperosmolar hyperglycemic state (HHS) is a life-threatening metabolic emergency with high mortality rate. Yet, there is no national system in the UK to monitor clinical practice or outcomes. To address this, we implemented and evaluated a multicenter surveillance system for HHS, assessing interhospital variations in management, outcomes, and barriers to guideline implementation. RESEARCH DESIGN AND

methodsThis mixed-methods observational study was conducted across 12 NHS hospitals between 2021 and 2024. A standardized data collection tool was developed, capturing demographics, biochemistry, treatment, and outcomes of HHS care. Adults meeting the Joint British Diabetes Societies criteria for HHS were included. Quantitative analyses were conducted to investigate care variations compared with guidelines among centers and identify predictors of HHS outcomes. In parallel, stakeholder interviews were analyzed thematically to explore implementation experiences. The Reach, Effectiveness, Adoption, Implementation, Maintenance framework guided evaluation.

resultsIn our cohort, a total of 218 HHS episodes were included. Median patient age was 77 years; 84.4% had type 2 diabetes, with a high comorbidity burden. The median hospital stay was 10.3 days, and the mortality rate was 16.1%. Significant interhospital variation was observed in insulin dosing, glucose monitoring, and time to discharge. Multivariate analysis identified older age and elevated sodium as independent predictors of mortality. The Digital Evaluation of Ketosis and Other Diabetes Emergencies (DEKODE)-HHS model demonstrated feasibility, high user engagement, and potential for integration into routine quality improvement structures. Qualitative findings revealed barriers, including diagnostic misclassification and resource constraints, to the adoption of the DEKODE-HHS model. However, they also highlighted the educational impact and system usability once the model was adopted.

conclusionsThe DEKODE-HHS model represents the first UK multicenter surveillance initiative for HHS. It identifies variation in practice and outcome predictors while highlighting systemic barriers to guideline adherence. This model provides a scalable framework for continuous quality improvement in HHS management and may inform future updates to national guidance.

Indexed as

Hyperglycemic Hyperosmolar Nonketotic ComaAgedAged, 80 and overFemaleHospitalsHumansMaleMiddle AgedUnited KingdomDiabetes ComplicationsHyperglycemiaPatient CareQuality Improvement

Identifiers

PMID41360624
PMCPMC12684118

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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.