Development and Validation of a Personalized, Sex-Specific Prediction Algorithm of Severe Atheromatosis in Middle-Aged Asymptomatic Individuals: The ILERVAS Study - Bermúdez-López, Marcelino; Martí-Antonio, Manuel; Castro-Boqué, Eva; Bretones, María del Mar; Farràs, Cristina; Torres, Gerard; Pamplona, Reinald; Lecube, Albert; Mauricio, Dídac; Valdivielso, José Manuel; Fernández, Elvira (Frontiers in Cardiovascular Medicine, 2022)
Background: Although European guidelines recommend vascular ultrasound for the assessment of cardiovascular risk in low-to-moderate-risk individuals, no algorithm properly identifies patients who could benefit from it. The aim of this study is to develop a sex-specific algorithm to identify those patients, especially females who are usually underdiagnosed. Methods: Clinical, anthropometrical and biochemical data were combined with a twelve-territory vascular ultrasound to predict severe atheromatosis (SA: ≥ 3 territories with plaque). A Personalized Algorithm for Severe Atheromatosis Prediction (PASAP-ILERVAS) was obtained by machine learning. Models were trained in the ILERVAS cohort (n=8330; 51% females) and validated in the control subpopulation of the NEFRONA cohort (n=559; 47% females). Performance was compared to the Systematic COronary Risk Evaluation (SCORE) model. Results: The PASAP-ILERVAS is a sex-specific, easy-to-interpret predictive model that stratifies individuals according to their risk of SA in low, intermediate or high-risk. New clinical predictors beyond traditional factors were uncovered. In low- and high-risk (L&H-risk) males, the net reclassification index (NRI) was .044 (95% CI: .020-.068), and the integrated discrimination index (IDI) was .038 (95% CI: .029-.048) compared to the SCORE. In L&H-risk females, PASAP-ILERVAS showed a significant increase in the AUC (.074 (95% CI: .062-.087, p-value: <.001)), a NRI of .193 (95% CI: .162-.224), and an IDI of .119 (95% CI: .109-.129). Conclusions: The PASAP-ILERVAS improves SA prediction, especially in females. Thus, it could reduce the number of unnecessary complementary explorations selecting patients for a further imaging study within the intermediate risk group, increasing cost-effectiveness and optimizing health resources.