Abstract / Description of output
Introduction and Aim Diabetes is a global health emergency with increasing prevalence and diabetes-associated morbidity and mortality. One of the challenges in optimising diabetes care is translating research advances in this heterogenous disease into routine clinical care. A potential solution is the introduction of precision medicine approaches into diabetes care.
We aim to develop a digital platform called ‘intelligent Diabetes’ (iDiabetes) to support a precision diabetes care model in Scotland and assess its impact on the primary composite outcome of all-cause mortality, hospitalisation rate, renal function decline and glycaemic control.
Methods and Analysis The impact of iDiabetes will be evaluated through a cluster-randomised controlled study, recruiting up to 22,500 patients with diabetes. Primary care general practices (GP) in the National Health Service Scotland Tayside Health Board are the units (clusters) of randomisation. Each primary care GP will form one cluster (approximately 400 patients per cluster), with up to 60 clusters recruited. Randomisation will be to iDiabetes (guideline support), iDiabetesPlus or usual diabetes care (control arm). Patients of participating primary care GPs are automatically enrolled to the study when they attend for their annual diabetes screening or are newly diagnosed with diabetes. A composite hierarchical primary outcome, evaluated using Win-Ratio statistical methodology, will consists of (I) all-cause mortality, (II) all-cause hospitalisation rate, (III) proportion with >40% eGFR reduction from baseline or new development of end-stage renal disease, (IV) proportion with absolute HbA1C reduction >0.5%. Comprehensive qualitative and health economic analyses will be conducted, assessing the cost-effectiveness, budget impact and user acceptability of the iDiabetes platform.
Ethics and Dissemination This study was reviewed by the NHS HRA and given a favourable opinion by a Research Ethics Committee (reference:23/ES/0008). Study findings will be disseminated via publications and presented at scientific conferences. Findings will be shared with patients and the public on the study website and social media.
We aim to develop a digital platform called ‘intelligent Diabetes’ (iDiabetes) to support a precision diabetes care model in Scotland and assess its impact on the primary composite outcome of all-cause mortality, hospitalisation rate, renal function decline and glycaemic control.
Methods and Analysis The impact of iDiabetes will be evaluated through a cluster-randomised controlled study, recruiting up to 22,500 patients with diabetes. Primary care general practices (GP) in the National Health Service Scotland Tayside Health Board are the units (clusters) of randomisation. Each primary care GP will form one cluster (approximately 400 patients per cluster), with up to 60 clusters recruited. Randomisation will be to iDiabetes (guideline support), iDiabetesPlus or usual diabetes care (control arm). Patients of participating primary care GPs are automatically enrolled to the study when they attend for their annual diabetes screening or are newly diagnosed with diabetes. A composite hierarchical primary outcome, evaluated using Win-Ratio statistical methodology, will consists of (I) all-cause mortality, (II) all-cause hospitalisation rate, (III) proportion with >40% eGFR reduction from baseline or new development of end-stage renal disease, (IV) proportion with absolute HbA1C reduction >0.5%. Comprehensive qualitative and health economic analyses will be conducted, assessing the cost-effectiveness, budget impact and user acceptability of the iDiabetes platform.
Ethics and Dissemination This study was reviewed by the NHS HRA and given a favourable opinion by a Research Ethics Committee (reference:23/ES/0008). Study findings will be disseminated via publications and presented at scientific conferences. Findings will be shared with patients and the public on the study website and social media.
Original language | English |
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Publisher | medRxiv |
Number of pages | 20 |
DOIs | |
Publication status | Published - 21 Mar 2024 |
Keywords / Materials (for Non-textual outputs)
- MeSH descriptors
- Precision medicine
- Diabetes mellitus
- Diabetes complications
- Cardiovascular diseases
- Heart failure
- Kidney failure
- chronic
- Non-alcoholic fatty liver disease
- Glycemic control
- Genetic risk score
- Biological variation
- population
- Primary health care
- Delivery of health care
- Health care economics and organizations
- Qualitative research