Does the PREVENT risk score improve ASCVD risk stratification compared to the WHO risk score in a multiethnic Brazilian cohort?
The PREVENT risk score demonstrated better calibration and risk classification for ASCVD events than the WHO score in a diverse Brazilian cohort, potentially improving preventive therapy allocation.
Background: Models predicting ASCVD risk often overestimate risk in diverse populations from low- and middle-income countries (LMICs), limiting their clinical utility and efficiency of resource allocation. Methods: We evaluated the performance of the PREVENT score in a large, multiethnic, population-based cohort of adults without baseline ASCVD, followed prospectively for adjudicated cardiovascular events. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was evaluated by predicted-to-observed (P/O) risk ratios. We compared PREVENT with the 2019 WHO cardiovascular risk score, a model specifically recalibrated for use in LMICs, and assessed reclassification using the net reclassification index (NRI), scaling the 10-year risk to 5-year estimates using exponential survival transformation. Additionally, we examined how risk reclassification would affect recommendations for preventive therapy. Results: Among 11,077 participants (age 53.1 ± 8.1 years, 55.3% female), 157 ASCVD events occurred over five years. Discrimination was similar for PREVENT (AUC 0.76, 95 % CI: 0.72-0.80) and WHO (0.75, 95 % CI: 0.71-0.78). PREVENT had better calibration (P/O 1.21 1.07-1.51 vs. 1.57 1.31-2.46 for WHO) and improved risk classification (NRI 0.19). This improvement was more pronounced among women (NRI = 0.24) and Black or mixed-race individuals (NRI = 0.28). In adults aged 40-75 with ≥1 cardiovascular risk factor, the PREVENT model appropriately up-classified more individuals who had an event to the group for which there is a recommendation for preventive treatment. Conclusions: PREVENT demonstrated better alignment between predicted and observed ASCVD risk compared with the WHO score in a large, diverse LMIC cohort. Its higher out-of-the-box calibration may enable more accurate risk stratification and efficient resource allocation in LMICs.
Pedroso et al. (Mon,) studied this question.
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