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Cardiovascular risk stratification without recalibration: A comparative study of the PREVENT and WHO risk scores in a
Aline F Pedroso1, Luisa C C Brant2, Antonio L P Ribeiro2
1Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Insights
The PREVENT score better predicts cardiovascular risk in diverse populations from low- and middle-income countries (LMICs) compared to the WHO score. This improved accuracy aids in efficient resource allocation for preventive therapies.
Area of Science:
- Cardiovascular epidemiology
- Health equity in LMICs
- Clinical risk prediction models
Background:
- Cardiovascular disease (ASCVD) risk models often overestimate risk in low- and middle-income countries (LMICs).
- This overestimation limits clinical utility and efficient resource allocation in these diverse populations.
- Accurate risk prediction is crucial for effective public health strategies.
Purpose of the Study:
- To evaluate the performance of the PREVENT cardiovascular risk score.
- To compare PREVENT with the 2019 WHO cardiovascular risk score in a large, multiethnic LMIC cohort.
- To assess the impact of risk reclassification on preventive therapy recommendations.
Main Methods:
- Prospective cohort study of 11,077 adults without baseline ASCVD.
- Assessed model discrimination (AUC) and calibration (P/O ratios).
- Compared PREVENT and WHO scores using Net Reclassification Index (NRI) and analyzed impact on preventive treatment recommendations.
Main Results:
- PREVENT and WHO scores showed similar discrimination (AUC 0.76 vs. 0.75).
- PREVENT demonstrated superior calibration (P/O 1.21 vs. 1.57) and improved risk classification (NRI 0.19).
- Improvements were notable in women and Black or mixed-race individuals, leading to better identification for preventive treatment.
Conclusions:
- The PREVENT score shows better alignment between predicted and observed ASCVD risk in a diverse LMIC cohort.
- Its superior calibration facilitates more accurate risk stratification and efficient resource allocation.
- PREVENT offers a valuable tool for improving cardiovascular disease prevention strategies in LMICs.
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.
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