IJCS | Volume 31, Nº5, September / October 2018

497 Azevedo et al. ASCVD risk estimator to estimate CVD risk Int J Cardiovasc Sci. 2018;31(5)492-498 Original Article between 60 and 79 years was classified as a risk index, whereas age < 60 years as a protective index for CVDs for the development of CVD. The other factors (male and FamH of CVD) showed no statistically relevant correlations or confidence interval that indicate them as risk index for CVDs. Our sample showed similar sex and age distribution between men and women, and a high prevalence of HBP in women and dyslipidemia in men. In the high risk group, age ≥ 60 years was the most prevalent risk factor, which is in accordance with the higher incidence of cardiovascular events at this age range in the Brazilian population. The present study had some limitations in the collection of other data that would enable a better comparison of risk factors that were not analyzed in the ASCVD Risk Estimator, but were included in other risk score estimators, such as obesity and FamH of CVD. A critical point of the ASCVD Risk Estimator is the ethnic definition for the 10-year risk calculation. This instrument considers two possible ethnicities for a reliable calculation – white and Afro-American. In case the answer “others” is chosen, a warning pops-up on the app screen saying that the estimated risks may be underestimated in American-Indian populations, and over- or underestimated in Asian- and Hispanic- and Americans. 8 Considering miscegenation in Brazil, and the difficulty in stratifying the population in white and non-white only, estimation of cardiovascular risk by the ASCVD Risk Estimator may be over- or underestimated. Although this does not nullify the usefulness of the instrument in the Brazilian population, caution is needed in interpreting these results and in individualized assessment of patients, since the Framingham score has not been validated in Brazil. In addition to the 10-year risk and lifetime risk, the ASCVD risk estimator app also estimates these risks in patients with optimal risk factors, that is, patients with optimal values and conditions of the variables analyzed. This additional tool is of great value for the analysis of modifiable risk factors and their impact on final risk score. The present study showed that 52.2% of patients with a 10-year risk ≥ 20% had a risk < 10% with optimal risk factors, i.e., those classified as high cardiovascular risk by the 10-year risk estimation would have a low risk if they had well-controlled comorbidities including HBP, DM, dyslipidemia, reinforcing the importance of prevention and control of modifiable risk factors. Investments on programs of control of chronic diseases, promotion of physical activity and balanced diet, and anti-smoking campaigns highlighting the risks for developing CVDs associated with smoking could contribute to reduce the number of individuals classified as high cardiovascular risk and prevent cardiovascular events. Conclusions We concluded that patients with high cardiovascular risk represented approximately one third of the study population; age greater than or equal to 60 years was the main non-modifiable risk factor, and HBP and dyslipidemia were the most prevalent modifiable risk factor in the high risk group. Also, this study evaluated the risk in patients with optimal risk factors and found that more than half of these patients would be classified as low risk, reinforcing the importance of the control of modifiable risk factors for the prevention of CVDs. We still don’t have a single protocol or score able to estimate the cardiovascular risk of all individuals in the same way, or that encompasses all risk factors involved in the pathophysiology of CVDs. Therefore, the physician must perform and individualized evaluation of patients and be updated on the best methods of disease prevention to improve current approaches. Author contributions Conception and design of the research: Azevedo TA, Nucera APCS, Moreira MLV. Acquisition of data: Azevedo TA. Analysis and interpretation of the data: Azevedo TA, Nucera APCS, Moreira MLV. Statistical analysis: Azevedo TA, Nucera APCS, Moreira MLV. Writing of the manuscript: Azevedo TA, Nucera APCS, Moreira MLV. Critical revision of the manuscript for intellectual content: Azevedo TA, Nucera APCS. Potential Conflict of Interest No potential conflict of interest relevant to this article was reported. Sources of Funding There were no external funding sources for this study.