Prediction models for risk classification in cardiovascular disease

Risk stratification is an increasingly important tool for the management of patients with different diseases and also for decision making in subjects not yet with overt disease but who are at risk of disease in the short or long term or during their lifetime. Careful risk assessment in the individual patient, based on clinical, laboratory and imaging data, can be helpful for making decisions about treatment or other prevention strategies. As regards cardiovascular disease, many models have been suggested and are available for the prediction of diagnosis and prognosis and there are several algorithms for risk prediction. However, current risk screening methods are not perfect. This review evaluates relative strengths and limitations of traditional and more recent methods for assessing the performance of prediction models.