Evaluating Cardiovascular Disease (CVD) risk scores for participants with known CVD and non-CVD in a multiracial/ethnic Caribbean sample.

Background: Cardiovascular Disease (CVD) risk prediction models have been useful in estimating if individuals are at low, intermediate, or high risk, of experiencing a CVD event within some established time frame, usually 10 years. Central to this is the concern in Trinidad and Tobago of using pre-existing CVD risk prediction methods, based on populations in the developed world (e.g. ASSIGN, Framingham and QRISK2), to establish risk for its multiracial/ethnic Caribbean population. The aim of this study was to determine which pre-existing CVD risk method is best suited for predicting CVD risk for individuals in this population.

Method: A survey was completed by 778 participants, 526 persons with no prior CVD, and 252 who previously reported a CVD event. Lifestyle and biometric data was collected from non-CVD participants, while for CVD participants, medical records were used to collect data at the first instance of CVD. The performances of three CVD risk prediction models (ASSIGN, Framingham and QRISK2) were evaluated using their calculated risk scores.

Results: All three models (ASSIGN, Framingham and QRISK2) identified less than 62% of cases (CVD participants) with a high proportion of false-positive predictions to true predictions as can be seen by positive predictabilities ranging from 78% (ASSIGN and Framingham) to 87% (QRISK2). Further, for all three models, individuals whose scores fell into the misclassification range were 2X more likely to be individuals who had experienced a prior CVD event as opposed to healthy individuals.

Conclusion: The ASSIGN, Framingham and QRISK2 models should be utilised with caution on a Trinidad and Tobago population of intermediate and high risk for CVD since these models were found to have underestimated the risk for individuals with CVD up to 2.5 times more often than they overestimated the risk for healthy persons.