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Incremental value of risk factor variability for cardiovascular risk prediction in individuals with type 2 diabetes: results from UK primary care electronic health records.

Accepted version
Peer-reviewed

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Authors

Arnold, Matthew 
Sun, Luanluan 
Stevens, David 
Chung, Ryan 

Abstract

BACKGROUND: Cardiovascular disease (CVD) risk prediction models for individuals with type 2 diabetes are important tools to guide intensification of interventions for CVD prevention. We aimed to assess the added value of incorporating risk factors variability in CVD risk prediction for people with type 2 diabetes. METHODS: We used electronic health records (EHRs) data from 83 910 adults with type 2 diabetes but without pre-existing CVD from the UK Clinical Practice Research Datalink for 2004-2017. Using a landmark-modelling approach, we developed and validated sex-specific Cox models, incorporating conventional predictors and trajectories plus variability of systolic blood pressure (SBP), total and high-density lipoprotein (HDL) cholesterol, and glycated haemoglobin (HbA1c). Such models were compared against simpler models using single last observed values or means. RESULTS: The standard deviations (SDs) of SBP, HDL cholesterol and HbA1c were associated with higher CVD risk (P < 0.05). Models incorporating trajectories and variability of continuous predictors demonstrated improvement in risk discrimination (C-index = 0.659, 95% CI: 0.654-0.663) as compared with using last observed values (C-index = 0.651, 95% CI: 0.646-0.656) or means (C-index = 0.650, 95% CI: 0.645-0.655). Inclusion of SDs of SBP yielded the greatest improvement in discrimination (C-index increase = 0.005, 95% CI: 0.004-0.007) in comparison to incorporating SDs of total cholesterol (C-index increase = 0.002, 95% CI: 0.000-0.003), HbA1c (C-index increase = 0.002, 95% CI: 0.000-0.003) or HDL cholesterol (C-index increase= 0.003, 95% CI: 0.002-0.005). CONCLUSION: Incorporating variability of predictors from EHRs provides a modest improvement in CVD risk discrimination for individuals with type 2 diabetes. Given that repeat measures are readily available in EHRs especially for regularly monitored patients with diabetes, this improvement could easily be achieved.

Description

Keywords

Cardiovascular disease, electronic health records, repeated measurements, risk prediction, type 2 diabetes, variability, Adult, Male, Female, Humans, Risk Factors, Diabetes Mellitus, Type 2, Cardiovascular Diseases, Cholesterol, HDL, Glycated Hemoglobin, Electronic Health Records, Heart Disease Risk Factors, Primary Health Care, United Kingdom

Journal Title

Int J Epidemiol

Conference Name

Journal ISSN

0300-5771
1464-3685

Volume Title

Publisher

Oxford University Press (OUP)
Sponsorship
Medical Research Council (MR/L003120/1)
British Heart Foundation (RG/18/13/33946)
National Institute for Health and Care Research (IS-BRC-1215-20014)
British Heart Foundation (FS/18/56/34177)
British Heart Foundation (CH/12/2/29428)
British Heart Foundation (None)
British Heart Foundation (None)