Prediction models in angina: a review

The assessment of pre-test probability is an important gatekeeper for knowing which patients to select in diagnostic testing for coronary artery disease. The European Society of Cardiology (also known as The ESC) recommends evaluating pre-test probability from factors such as sex, age and type of symptoms that the patients experience. In 2019, The ESC guidelines recommended upgrading pre-test probability based on clinical risk factors. However, no estimates of how these factors affect pre-test probability were provided. In this study, a prediction model for coronary artery disease, including cardiovascular risk factors, was validated. This model can aid in reducing the need for diagnostic testing. It could be combined with risk factor control and optimal medical therapy for the patients. However, in this study, definitions of coronary artery disease differed between populations, so this may cause some variations in understanding the model.

This review by Bjerking LH et al aimed to validate pre-test probability models for coronary artery disease.

Key learnings

This study has validated pre-test probability models which include cardiovascular risk factors; models like these can help to reduce the necessity for diagnostic testing, and patients in waiting lists could use this model combined with risk factor control and optimal medical therapy.