![]() ![]() Both these equations performed reasonably well for identifying patients with high CVD risk. A sample of 10,137 patients, of which 272 diagnosed with diabetes, was extracted from this cohort to evaluate the performance of the UKPDS risk engine compared to the Framingham risk equations in both general and diabetic population. The underlying risk equation was first validated in the Collaborative Atorvastatin Diabetes Study (CARDS) cohort, a primary prevention trial including 2838 patients with diabetes, and then in the European Prospective Investigation of Cancer (EPIC)-Norfolk Cohort, a prospective cohort study in which patients aged 40–79 years were recruited from general practitioners in the Norfolk region of the United Kingdom. In 2007, a new model was published that estimates CVD risk directly (defined as first occurrence of fatal or non-fatal myocardial infarction, sudden cardiac death, other ischaemic heart disease, fatal or non-fatal stroke, or fatal peripheral vascular disease). Risk factors included in the first UKPDS model and the main limitations of this equation are shown in Table 1. The UKPDS diabetes-specific approach included HbA1c as a continuous variable for the first time it also replaced age as a risk factor by two diabetes-specific variables: age at diagnosis of diabetes and time since the diagnosis of diabetes. Moreover, this and the other models for CVD risk evaluation in general population used dichotomous variables for glycaemia, such as the presence or absence of diabetes. On the contrary, the Framingham calculator that tended to underestimate risks for people with diabetes included relatively few diabetic subjects it was created using data from 5573 individuals followed for 12 years, but only 337 were known to have diabetes. It was developed on a cohort of 5102 patients with type 2 diabetes followed for a median of 10.7 years. The score was initially designed to estimate coronary heart disease (CHD) risk and stroke risk separately. The oldest and most commonly used prediction model is the UK Prospective Diabetes Study (UKPDS) risk engine. The UK Prospective Diabetes Study (UKPDS) risk engine To date, some authors support the need to create models exclusively from cohorts of persons affected with diabetes, while others prefer to adapt existing risk models developed in the general population to diabetes. In recent years, this approach has been called into question by several authors considering more appropriate to develop diabetes-specific risk models. In these models, all patients with diabetes mellitus as those with existing CVD are considered as people at high risk and treated as if they required secondary prevention of CVD. This approach has been confirmed by algorithms currently developed from both American and European cohorts, such as ATP-III guidelines, the European Systematic COronary Risk Evaluation (SCORE) algorithm and the Prospective Cardiovascular Munster (PROCAM) model. Following this observation, several studies supported this concept of diabetes as a “CVD risk equivalent”: the presence of diabetes mellitus is considered to confer a 10-year CVD risk similar to individuals without diabetes with a prior history of CVD. Three decades later, in the late 1990s, a study from Finland suggested for the first time that people suffering from diabetes, but without history of CVD, had a risk of CVD similar to that of people without diabetes who had survived a CVD event. In the following years, diabetes status was added to the model but only as categorical variable, and the tool was validated only in the general population. One of the main limitations of this model was that it did not consider diabetes status or any other indicator of chronic hyperglycaemia. The Framingham risk score was first developed based on a long-term community cohort study, and it is applicable to general population. The first studies aiming at developing reliable tools for evaluating CVD risk based on a combination of several risk factors were carried out in the United States by the Framingham investigators in the 1960s. Ĭonsidering that diabetes mellitus usually involves the coexistence of several cardiovascular risk factors, it was considered necessary to develop multifactorial approaches for CVD risk evaluation. ![]() Rationale for CVD risk prediction in people with diabetesĮstimates of CVD risk can be useful for both clinicians and patients: for clinicians, it is a prognostic information that can support them in the choice of therapeutic and preventive strategies and for patients, it can be a motivation tool to adopt healthy lifestyle measures and to observe prescribed risk-modifying treatments.
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