Abstract
BackgroundCardiovascular 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 associat ed 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 E HRs especially for regularly monitored patients with diabetes, this improvement could easily be achieved.
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