000162251 001__ 162251
000162251 005__ 20251017144654.0
000162251 0247_ $$2doi$$a10.3389/fcvm.2025.1579947
000162251 0248_ $$2sideral$$a144808
000162251 037__ $$aART-2025-144808
000162251 041__ $$aeng
000162251 100__ $$0(orcid)0000-0002-5064-3763$$aCastel-Feced, Sara$$uUniversidad de Zaragoza
000162251 245__ $$aPrediction of cardiovascular risk using machine-learning methods. Sex-specific differences
000162251 260__ $$c2025
000162251 5060_ $$aAccess copy available to the general public$$fUnrestricted
000162251 5203_ $$aMachine learning (ML) algorithms offer some advantages over traditional scoring systems to assess the influence of cardiovascular risk factors (CVRFs) on the risk of major cardiovascular event (MACE), and could be useful in clinical practice. These algorithms can also be trained using a growing body of real world data (RWD). The aim of the study was to evaluate the MACE risk applying the XGBoost and Random Forest ML algorithms to RWD, stratifying the study population by sex, comparing the outcomes of these two algorithms.MethodsThe follow-up period of the study was from 2018 to 2020. For each algorithm, 3 models were generated, including age and different combinations of three groups of variables: blood test and blood pressure measurements; CVRFs; and medication adherence.ResultsIn this study, 52,393 subjects were included, of whom 581 suffered a MACE. The incidence of MACE was 1% in women and 1.3% in men. The most prevalent CVRF was hypertension, followed by hypercholesterolaemia in both sexes. Adherence to treatment was highest for antihypertensives and lowest for antidiabetics. In all models age was the greatest relative contributor to the risk of MACE, followed by adherence to antidiabetics. Adherence to treatment proved to be an important variable in the risk of having a MACE. Moreover, similar performance was found for RF and XGBoost algorithms.ConclusionThese findings support the use of ML to assess cardiovascular risk and guide personalized prevention strategies in primary care settings.
000162251 536__ $$9info:eu-repo/grantAgreement/ES/DGA-GRISSA/B09-23R$$9info:eu-repo/grantAgreement/ES/DGA-IIU/796-2019$$9info:eu-repo/grantAgreement/ES/ISCIII/PI22-01193
000162251 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000162251 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000162251 700__ $$0(orcid)0000-0001-7293-701X$$aAguilar-Palacio, Isabel$$uUniversidad de Zaragoza
000162251 700__ $$0(orcid)0000-0002-7194-8275$$aMalo, Sara$$uUniversidad de Zaragoza
000162251 700__ $$aGonzález-García, Juan
000162251 700__ $$0(orcid)0000-0003-1647-3462$$aMaldonado, Lina$$uUniversidad de Zaragoza
000162251 700__ $$0(orcid)0000-0002-6671-5661$$aRabanaque-Hernández, María José$$uUniversidad de Zaragoza
000162251 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000162251 7102_ $$14014$$2623$$aUniversidad de Zaragoza$$bDpto. Economía Aplicada$$cÁrea Métodos Cuant.Econ.Empres
000162251 7102_ $$11011$$2615$$aUniversidad de Zaragoza$$bDpto. Microb.Ped.Radio.Sal.Pú.$$cÁrea Medic.Prevent.Salud Públ.
000162251 773__ $$g12 (2025), 1579947 [11 pp.]$$pFront. cardiovasc. med.$$tFrontiers in cardiovascular medicine$$x2297-055X
000162251 8564_ $$s1393081$$uhttps://zaguan.unizar.es/record/162251/files/texto_completo.pdf$$yVersión publicada
000162251 8564_ $$s2333505$$uhttps://zaguan.unizar.es/record/162251/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
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000162251 951__ $$a2025-10-17-14:37:46
000162251 980__ $$aARTICLE