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      Cardiovascular risk assessment scores for people with diabetes: a systematic review

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          Abstract

          People with type 2 diabetes have an increased risk of cardiovascular disease (CVD). Multivariate cardiovascular risk scores have been used in many countries to identify individuals who are at high risk of CVD. These risk scores include those originally developed in individuals with diabetes and those developed in a general population. This article reviews the published evidence for the performance of CVD risk scores in diabetic patients by: (1) examining the overall rationale for using risk scores; (2) systematically reviewing the literature on available scores; and (3) exploring methodological issues surrounding the development, validation and comparison of risk scores. The predictive performance of cardiovascular risk scores varies substantially between different populations. There is little evidence to suggest that risk scores developed in individuals with diabetes estimate cardiovascular risk more accurately than those developed in the general population. The inconsistency in the methods used in evaluation studies makes it difficult to compare and summarise the predictive ability of risk scores. Overall, CVD risk scores rank individuals reasonably accurately and are therefore useful in the management of diabetes with regard to targeting therapy to patients at highest risk. However, due to the uncertainty in estimation of true risk, care is needed when using scores to communicate absolute CVD risk to individuals.

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          Most cited references 58

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          Prediction of coronary heart disease using risk factor categories.

          The objective of this study was to examine the association of Joint National Committee (JNC-V) blood pressure and National Cholesterol Education Program (NCEP) cholesterol categories with coronary heart disease (CHD) risk, to incorporate them into coronary prediction algorithms, and to compare the discrimination properties of this approach with other noncategorical prediction functions. This work was designed as a prospective, single-center study in the setting of a community-based cohort. The patients were 2489 men and 2856 women 30 to 74 years old at baseline with 12 years of follow-up. During the 12 years of follow-up, a total of 383 men and 227 women developed CHD, which was significantly associated with categories of blood pressure, total cholesterol, LDL cholesterol, and HDL cholesterol (all P or =130/85). The corresponding multivariable-adjusted attributable risk percent associated with elevated total cholesterol (> or =200 mg/dL) was 27% in men and 34% in women. Recommended guidelines of blood pressure, total cholesterol, and LDL cholesterol effectively predict CHD risk in a middle-aged white population sample. A simple coronary disease prediction algorithm was developed using categorical variables, which allows physicians to predict multivariate CHD risk in patients without overt CHD.
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            Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.

            Identification of key factors associated with the risk of developing cardiovascular disease and quantification of this risk using multivariable prediction algorithms are among the major advances made in preventive cardiology and cardiovascular epidemiology in the 20th century. The ongoing discovery of new risk markers by scientists presents opportunities and challenges for statisticians and clinicians to evaluate these biomarkers and to develop new risk formulations that incorporate them. One of the key questions is how best to assess and quantify the improvement in risk prediction offered by these new models. Demonstration of a statistically significant association of a new biomarker with cardiovascular risk is not enough. Some researchers have advanced that the improvement in the area under the receiver-operating-characteristic curve (AUC) should be the main criterion, whereas others argue that better measures of performance of prediction models are needed. In this paper, we address this question by introducing two new measures, one based on integrated sensitivity and specificity and the other on reclassification tables. These new measures offer incremental information over the AUC. We discuss the properties of these new measures and contrast them with the AUC. We also develop simple asymptotic tests of significance. We illustrate the use of these measures with an example from the Framingham Heart Study. We propose that scientists consider these types of measures in addition to the AUC when assessing the performance of newer biomarkers.
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              Mortality from coronary heart disease in subjects with type 2 diabetes and in nondiabetic subjects with and without prior myocardial infarction.

              Type 2 (non-insulin-dependent) diabetes is associated with a marked increase in the risk of coronary heart disease. It has been debated whether patients with diabetes who have not had myocardial infarctions should be treated as aggressively for cardiovascular risk factors as patients who have had myocardial infarctions. To address this issue, we compared the seven-year incidence of myocardial infarction (fatal and nonfatal) among 1373 nondiabetic subjects with the incidence among 1059 diabetic subjects, all from a Finnish population-based study. The seven-year incidence rates of myocardial infarction in nondiabetic subjects with and without prior myocardial infarction at base line were 18.8 percent and 3.5 percent, respectively (P<0.001). The seven-year incidence rates of myocardial infarction in diabetic subjects with and without prior myocardial infarction at base line were 45.0 percent and 20.2 percent, respectively (P<0.001). The hazard ratio for death from coronary heart disease for diabetic subjects without prior myocardial infarction as compared with nondiabetic subjects with prior myocardial infarction was not significantly different from 1.0 (hazard ratio, 1.4; 95 percent confidence interval, 0.7 to 2.6) after adjustment for age and sex, suggesting similar risks of infarction in the two groups. After further adjustment for total cholesterol, hypertension, and smoking, this hazard ratio remained close to 1.0 (hazard ratio, 1.2; 95 percent confidence interval, 0.6 to 2.4). Our data suggest that diabetic patients without previous myocardial infarction have as high a risk of myocardial infarction as nondiabetic patients with previous myocardial infarction. These data provide a rationale for treating cardiovascular risk factors in diabetic patients as aggressively as in nondiabetic patients with prior myocardial infarction.
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                Author and article information

                Affiliations
                MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Box 285, Hills Road, Cambridge, CB2 0QQ UK
                Contributors
                nick.wareham@mrc-epid.cam.ac.uk
                Journal
                Diabetologia
                Diabetologia
                Springer-Verlag (Berlin/Heidelberg )
                0012-186X
                1432-0428
                24 July 2009
                October 2009
                : 52
                : 10
                : 2001-2014
                2744770
                19629430
                1454
                10.1007/s00125-009-1454-0
                © The Author(s) 2009
                Categories
                Review
                Custom metadata
                © Springer-Verlag 2009

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