BACKGROUND: Framingham risk score (FRS) is an important cardiovascular risk assessment tool, based on objective measurements of blood pressure and lipid profile, among other factors. However, in large population surveys, these measures are not always available, which limits their use. OBJECTIVES: Evaluate the performance of the FRS predictive results using subjective measures. METHODOLOGY: Cross-sectional study of 1,414 male rotating shift workers in an iron ore extraction company. The original FRS was calculated using objective systolic and diastolic blood pressure measurements, total cholesterol (TC), and HDL cholesterol. The modified FRS was calculated using subjective measurements of blood pressure and lipid profile, based on self-reported medical diagnosis and use of medications for these conditions. Three adaptations were proposed: (1) FRS-SAH, which considers only self-reported hypertension
(2) FRS-DLP, based solely on self-reported dyslipidemia
and (3) FRS-SAH and DLP, which integrates both self-reported factors. Agreement between the two scores was assessed using the kappa coefficient and the Bland-Altman analysis. The accuracy of the scores in predicting cardiovascular risk was compared using the ROC curve and the area under the curve (AUC). RESULTS: The scatter plot showed a strong correlation (r = 0.9036, p <
0.001) between adapted FRS-SAH and original FRS. The ROC curve showed an AUC with results above 0.85 for all models, confirming the effectiveness of the adapted scale. Bland-Altman indicated good precision between the measurements. Binary logistic regression analysis showed that all the factors associated with CVD-risk by the original FRS were similar to those associated with the adapted FRS. Among the adaptations, the FRS-SAH demonstrated the highest correlation and predictive accuracy. CONCLUSION: The adapted FRS proved to be effective in estimating CVD-risk, showing high correlation, sensitivity, specificity, and accuracy compared to the original FRS. Adaptive FRS based on self-reported hypertension, showed the best performance, making it a reliable alternative for contexts where direct measurements are not feasible.