Development and Validation of a Personalized, Sex-Specific Prediction Algorithm of Severe Atheromatosis in Middle-Aged Asymptomatic Individuals : The ILERVAS Study
Bermúdez-López, Marcelino (Grupo de Investigación Translacional Vascular y Renal. IRBLleida. Red de Investigación Renal)
Martí-Antonio, Manuel (Grupo de Investigación Translacional Vascular y Renal. IRBLleida. Red de Investigación Renal)
Castro-Boqué, Eva (Institut de Recerca Biomèdica de Lleida)
Bretones, María del Mar (Grupo de Investigación Translacional Vascular y Renal. IRBLleida. Red de Investigación Renal)
Farràs, Cristina (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)
Torres, Gerard (Centro de Investigación Biomédica en Red de Enfermedades Respiratorias)
Pamplona, Reinald (Institut de Recerca Biomèdica de Lleida)
Lecube, Albert (Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas)
Mauricio Puente, Dídac (Institut d'Investigació Biomèdica Sant Pau)
Valdivielso, José Manuel (Grupo de Investigación Translacional Vascular y Renal. IRBLleida. Red de Investigación Renal)
Fernández, Elvira (Grupo de Investigación Translacional Vascular y Renal. IRBLleida. Red de Investigación Renal)

Date:	2022
Abstract:	IIB.
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].
Grants:	Instituto de Salud Carlos III RETIC RD16/0009/0011 Ministerio de Ciencia, Innovación y Universidades IJC2018-037792-I
Note:	Altres ajuts: Diputació de Lleida.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Cardiovascular disease ; Atherosclerosis ; Cardiovascular risk assessment ; Machine learning ; Recursive partitioning classification trees ; Vascular ultrasound
Published in:	Frontiers in Cardiovascular Medicine, Vol. 9 (July 2022) , p. 895917, ISSN 2297-055X

DOI: 10.3389/fcvm.2022.895917
PMID: 35928938

14 p, 2.8 MB

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Research literature > UAB research groups literature > Research Centres and Groups (research output) > Health sciences and biosciences > Institut de Recerca Sant Pau
Articles > Research articles
Articles > Published articles