Development of a predictive model of cardiovascular risk in a male population from the peruvian Amazon

Abstract

Background: The coexistence of malnutrition due to over- and under-nutrition in the Peruvian Amazon increases chronic diseases and cardiovascular risk. Methods: A cross-sectional study of a male population where anthropometric, clinical, and demographic variables were obtained to create a binary logistic regression predictive model of cardiovascular risk. Results: We compared two methods with good predictive results, finally choosing Model 4 (r2 = 0.57, sensitivity 73.68%, specificity 95.35%, Youden index 0.69, and validity index 94.21), with non-invasive variables such as blood pressure (p < 0.001), hip circumference (p < 0.001), and FINDRISC test result (p < 0.05); Conclusions: We developed a cheap, fast, and non-invasive tool to determine cardiovascular risk in the population of this endemic area.