Abstract
SummaryPurposeTo critically examine existing approaches to cardiovascular disease (CVD) risk evaluation in people with diabetes, and discuss the use of accurate and validated absolute CVD risk tools as an appropriate basis for CVD prevention in people with diabetes.MethodsThis was a narrative review using evidence from the ADVANCE study and all relevant publications identified via PubMed MEDLINE.ResultsThere is sufficient evidence that diabetes does not confer a CVD risk equivalent to that in non-diabetic people with existing CVD in all circumstances. In people with diabetes, CVD risk follows a gradient. Reliably capturing this gradient depends on an adequate combination of several risk factors. Many global CVD risk tools applicable to people with diabetes have been developed. Those derived from older cohorts are less accurate in contemporary populations and many newer tools have not been tested. The ADVANCE risk engine, recently developed from the large multinational ADVANCE study, showed acceptable performance on the ADVANCE population and largely outperformed the popular Framingham risk equation when tested on the multinational DIAB-HYCAR cohort of people with type 2 diabetes.ConclusionsThe high-risk status conferred by diabetes does not preclude estimation of absolute CVD risk using tools such as the ADVANCE risk engine and its use as the basis for initiating and intensifying CVD preventative measures. Adopting such an accurate and validated tool will likely improve prescriptions and outcomes of diabetes care.
Highlights
Overview of global cardiovascular risk assessmentGlobal cardiovascular risk assessment is based on the combination of predictive information from several cardiovascular risk factors using mathematical equations ( called models)
To critically examine existing approaches to cardiovascular disease (CVD) risk evaluation in people with diabetes, and discuss the use of accurate and validated absolute CVD risk tools as an appropriate basis for CVD prevention in people with diabetes
The ADVANCE risk engine, recently developed from the large multinational ADVANCE study, showed acceptable performance on the ADVANCE population and largely outperformed the popular Framingham risk equation when tested on the multinational DIAB-HYCAR cohort of people with type 2 diabetes
Summary
Global cardiovascular risk assessment is based on the combination of predictive information from several cardiovascular risk factors using mathematical equations ( called models) In those models, the coefficient of each included risk factor indicates its relative contribution to the overall (global) CVD risk.[2,15] A model can be used to estimate the risk that a disease is present (diagnostic model) or to estimate the risk that a particular disease or health event will occur within a given time period (prognostic models). Calibration describes the agreement between estimated and observed risks It is assessed by comparing absolute risk estimates from the model with the actual event rates in the test population.[1,2,15] For illustration, a 10-year estimated absolute risk of CVD of 20% for a patient indicates that, in a given group of patients with similar characteristics, 20% will experience a cardiovascular event within a 10-year period of follow up. Recalibration of the risk model by adjusting the baseline risk estimates to fit the target population may help correcting the over- or underestimation of risk.[1,15]
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