Journal
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
Volume 51, Issue 6, Pages 1813-1823Publisher
OXFORD UNIV PRESS
DOI: 10.1093/ije/dyac140
Keywords
Cardiovascular disease; risk prediction; type 2 diabetes; variability; repeated measurements; electronic health records
Categories
Funding
- UK Medical Research Council [MR/L003120/1]
- British Heart Foundation (BHF) [RG/13/13/30194, RG/18/13/33946]
- BHF Cambridge Centre for Research Excellence [RE/13/6/30180]
- National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre [BRC-1215-20014]
- Health Data Research UK - UK Medical Research Council
- Public Health Agency (Northern Ireland)
- British Heart Foundation
- Wellcome
- Chinese Scholarship Council
- British Heart Foundation Programme Grant [RG/18/13/33946]
- Medical Research Council (MRC)
- School of Clinical Medicine at University of Cambridge
- National Institute for Health Research Cambridge Biomedical Research Centre (BRC) [BRC-1215-20014]
- BHF PhD studentship [FS/18/56/34177]
- Cancer Research UK [C18081/A31373]
- Canary Center at Stanford University
- University of Cambridge
- University College London
- University of Manchester
- MRC [MC_UU_00002/5]
- British Heart Foundation Chair award [CH/12/2/29428]
- Innovative Medicines Initiative-2 Joint Undertaking [116074]
- BHF-Turing Cardiovascular Data Science Award [BCDSA\100005]
- Engineering and Physical Sciences Research Council
- Economic and Social Research Council
- Department of Health and Social Care (England)
- Chief Scientist Office of the Scottish Government Health and Social Care Directorates
- Health and Social Care Research and Development Division (Welsh Government)
- British Heart Foundation-Turing Cardiovascular Data Science Award
- Oregon Health & Science University (OHSU) Knight Cancer Institute
- Medical Research Council [MC_UU_00002/5] Funding Source: researchfish
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Incorporating variability of risk factors from electronic health records improves cardiovascular disease (CVD) risk discrimination for individuals with type 2 diabetes.
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 (HbA(1c)). Such models were compared against simpler models using single last observed values or means. Results The standard deviations (SDs) of SBP, HDL cholesterol and HbA(1c) 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), HbA(1c) (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.
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