Risk prediction models for atherosclerotic cardiovascular disease: a systematic assessment with particular reference to Qatar

الملخص EN

Background: Atherosclerotic cardiovascular disease (ASCVD) is a common disease in the State of Qatar and results in considerable morbidity, impairment of quality of life and mortality.

The American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE) is currently used in Qatar to identify those at high risk of ASCVD.

However, it is unclear if this is the optimal ASCVD risk prediction model for use in Qatar’s ethnically diverse population.

Aims: This systematic review aimed to identify, assess the methodological quality of and compare the properties of established ASCVD risk prediction models for the Qatari population.

Methods: Two reviewers performed head-to-head comparisons of established ASCVD risk calculators systematically.

Studies were independently screened according to predefined eligibility criteria and critically appraised using Prediction Model Risk Of Bias Assessment Tool.

Data were descriptively summarized and narratively synthesized with reporting of key statistical properties of the models.

Results: We identified 20,487 studies, of which 41 studies met our eligibility criteria.

We identified 16 unique risk prediction models.

Overall, 50% (n1⁄48) of the risk prediction models were judged to be at low risk of bias.

Only 13% of the studies (n1⁄42) were judged at low risk of bias for applicability, namely, PREDICT and QRISK3.Only the PREDICT risk calculator scored low risk in both domains.

Conclusions: There is no existing ASCVD risk calculator particularly well suited for use in Qatar’s ethnically diverse population.

Of the available models, PREDICT and QRISK3 appear most appropriate because of their inclusion of ethnicity.

In the absence of a locally derived ASCVD for Qatar, there is merit in a formal head-to-head comparison between PCE, which is currently in use, and PREDICT and QRISK3.

Keywords: atherosclerotic cardiovascular disease, risk prediction model, systematic review