Back to Search Results

Development and Validation of a Personalized, Sex-Specific Prediction Algorithm of Severe Atheromatosis in Middle-Aged Asymptomatic Individuals: The ILERVAS Study.

Grupo de Investigación Translacional Vascular y Renal, IRBLleida, Red de Investigación Renal (RedInRen-ISCIII), Lleida, Spain. Centre d'Atenció Primària Cappont, Gerència Territorial de Lleida, Institut Català de la Salut, Barcelona, Spain. Research Support Unit Lleida, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gorina (IDIAPJGol), Barcelona, Spain. Departament de Medicina Respiratòria, Hospital Universitari Arnau de Vilanova, Grup Recerca Translational Medicina Respiratòria, IRBLleida, Universitat de Lleida, Lleida, Spain. CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain. Departament de Medicina Experimental, IRBLleida, Universitat de Lleida, Lleida, Spain. Departament d'Endocrinologia i Nutrició, Hospital Universitari Arnau de Vilanova, Grup de Recerca Obesitat i Metabolisme (ODIM), IRBLleida, Universitat de Lleida, Lleida, Spain. Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain. Departament d'Endocrinologia i Nutrició, Hospital de la Santa Creu i Sant Pau, Institut de Recerca Biomèdica Sant Pau (IIB Sant Pau), Barcelona, Spain.

Frontiers in cardiovascular medicine. 2022;:895917
Full text from:

Other resources

Abstract

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 women who are usually underdiagnosed. METHODS Clinical, anthropometrical, and biochemical data were combined with a 12-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 = 8,330; 51% women) and validated in the control subpopulation of the NEFRONA cohort (n = 559; 47% women). 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) men, the net reclassification index (NRI) was 0.044 (95% CI: 0.020-0.068), and the integrated discrimination index (IDI) was 0.038 (95% CI: 0.029-0.048) compared to the SCORE. In L&H-risk women, PASAP-ILERVAS showed a significant increase in the area under the curve (AUC, 0.074 (95% CI: 0.062-0.087), p-value: < 0.001), an NRI of 0.193 (95% CI: 0.162-0.224), and an IDI of 0.119 (95% CI: 0.109-0.129). CONCLUSION The PASAP-ILERVAS improves SA prediction, especially in women. 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. CLINICAL TRIAL REGISTRATION [www.ClinicalTrials.gov], identifier [NCT03228459].