ArticleBMJ open2018
Using electronic health records to quantify and stratify the severity of type 2 diabetes in primary care in England: rationale and cohort study design.
Article in BMJ open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Correlation of Blood Glucose Control With Cystoid Macular Edema and Central Macular Thickness After Cataract Surgery in Diabetics.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Association between dysglycemia and the Charlson Comorbidity Index among hospitalized patients with diabetes.Journal of diabetes and its complications · 2022Article
- Current gaps in management and timely referral of cardiorenal complications among people with type 2 diabetes mellitus in the Middle East and African countries: Expert recommendations.Journal of diabetes · 2022Review
- LV Prasad Eye Institute EyeSmart electronic medical record-based analytics of big data: LEAD-Uveitis Report 1: Demographics and clinical features of uveitis in a multi-tier hospital based network in Southern India.Indian journal of ophthalmology · 2022Article
- Estimating the population health burden of musculoskeletal conditions using primary care electronic health records.Rheumatology (Oxford, England) · 2021Article
- Assessing the severity of cardiovascular disease in 213 088 patients with coronary heart disease: a retrospective cohort study.Open heart · 2021Article
- Does the Encounter Type Matter When Defining Diabetes Complications in Electronic Health Records?Medical care · 2020Article
- Development and validation of the DIabetes Severity SCOre (DISSCO) in 139 626 individuals with type 2 diabetes: a retrospective cohort study.BMJ open diabetes research & care · 2020Article
- Big data and the eyeSmart electronic medical record system - An 8-year experience from a three-tier eye care network in India.Indian journal of ophthalmology · 2020Article
- Conceptual Framework to Support Clinical Trial Optimization and End-to-End Enrollment Workflow.JCO clinical cancer informatics · 2019Article
- Value of using artificial intelligence derived clusters by health and social care need in primary care: A qualitative interview study with patients living with multiple long-term conditions, carers and health care professionals.Journal of multimorbidity and comorbidityArticle
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Authors and funding
15 authors.
Funding
Abstract
introductionThe increasing prevalence of type 2 diabetes mellitus (T2DM) presents a significant burden on affected individuals and healthcare systems internationally. There is, however, no agreed validated measure to infer diabetes severity from electronic health records (EHRs). We aim to quantify T2DM severity and validate it using clinical adverse outcomes. METHODS AND ANALYSIS: Primary care data from the Clinical Practice Research Datalink, linked hospitalisation and mortality records between April 2007 and March 2017 for patients with T2DM in England will be used to develop a clinical algorithm to grade T2DM severity. The EHR-based algorithm will incorporate main risk factors (severity domains) for adverse outcomes to stratify T2DM cohorts by baseline and longitudinal severity scores. Provisionally, T2DM severity domains, identified through a systematic review and expert opinion, are: diabetes duration, glycated haemoglobin, microvascular complications, comorbidities and coprescribed treatments. Severity scores will be developed by two approaches: (1) calculating a count score of severity domains; (2) through hierarchical stratification of complications. Regression models estimates will be used to calculate domains weights. Survival analyses for the association between weighted severity scores and future outcomes-cardiovascular events, hospitalisation (diabetes-related, cardiovascular) and mortality (diabetes-related, cardiovascular, all-cause mortality)-will be performed as statistical validation. The proposed EHR-based approach will quantify the T2DM severity for primary care performance management and inform the methodology for measuring severity of other primary care-managed chronic conditions. We anticipate that the developed algorithm will be a practical tool for practitioners, aid clinical management decision-making, inform stratified medicine, support future clinical trials and contribute to more effective service planning and policy-making. ETHICS AND DISSEMINATION: The study protocol was approved by the Independent Scientific Advisory Committee. Some data were presented at the National Institute for Health Research School for Primary Care Research Showcase, September 2017, Oxford, UK and the Diabetes UK Professional Conference March 2018, London, UK. The study findings will be disseminated in relevant academic conferences and peer-reviewed journals.
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