Key result
Among 16 unique ASCVD risk prediction models evaluated, only PREDICT and QRISK3 (13% of studies) were judged at low risk of bias for applicability in Qatar's ethnically diverse population.
Why the study?
The ACC/AHA Pooled Cohort Equations are used in Qatar, but it was unclear if this is the optimal ASCVD risk prediction model for Qatar's ethnically diverse population.
Which established ASCVD risk prediction model is most appropriate and has the lowest risk of bias for use in an ethnically diverse population such as Qatar?
Systematic Review (n=41)
Which established ASCVD risk prediction model is most appropriate and has the lowest risk of bias for use in an ethnically diverse population such as Qatar?
PREDICT and QRISK3 appear to be the most appropriate ASCVD risk prediction models for ethnically diverse populations like Qatar due to their inclusion of ethnicity, suggesting a need to reevaluate the current use of the Pooled Cohort Equations (PCE).
PREDICT and QRISK3 merit priority for ASCVD risk assessment in Qatar; challenges PCE reliance and extends validation for ethnicity-inclusive models.
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% (n = 8) of the risk prediction models were judged to be at low risk of bias. Only 13% of the studies (n = 2) 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.
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Sheikh et al. (2021) conducted a systematic review in Atherosclerotic cardiovascular disease (n=41). ASCVD risk prediction models was evaluated on Methodological quality and applicability (Risk of bias). Among 16 unique ASCVD risk prediction models evaluated, only PREDICT and QRISK3 (13% of studies) were judged at low risk of bias for applicability in Qatar's ethnically diverse population.
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