Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/23247
DC FieldValueLanguage
dc.contributor.authorRossello, Xavier-
dc.contributor.authorDorresteijn, Jannick An-
dc.contributor.authorJanssen, Arne-
dc.contributor.authorLambrinou, Ekaterini-
dc.contributor.authorScherrenberg, Martijn-
dc.contributor.authorBonnefoy-Cudraz, Eric-
dc.contributor.authorCobain, Mark-
dc.contributor.authorPiepoli, Massimo F.-
dc.contributor.authorVisseren, Frank Lj-
dc.contributor.authorDendale, Paul-
dc.date.accessioned2021-10-12T12:19:47Z-
dc.date.available2021-10-12T12:19:47Z-
dc.date.issued2020-08-
dc.identifier.citationEuropean heart journal: Acute cardiovascular care, 2020, vol. 9, no. 5, pp. 522–532en_US
dc.identifier.issn20488734-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/23247-
dc.description.abstractRisk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of - usually interactive and online available - tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofEuropean heart journal: Acute cardiovascular careen_US
dc.rights© The European Society of Cardiologyen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectRisk predictionen_US
dc.subjectCardiovascular diseaseen_US
dc.subjectPatienten_US
dc.subjectPreventionen_US
dc.subjectRisk assessmenten_US
dc.titleRisk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)en_US
dc.typeArticleen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationCentro Nacional de Investigaciones Cardiovasculares (CNIC)en_US
dc.collaborationCIBER - Centro de Investigacion Biomedica en Reden_US
dc.collaborationUtrecht Universityen_US
dc.collaborationJessa Hospitalen_US
dc.collaborationHasselt Universityen_US
dc.collaborationImperial College Londonen_US
dc.collaborationGuglielmo da Saliceto Hospitalen_US
dc.collaborationUniversity of Southern Californiaen_US
dc.subject.categoryHealth Sciencesen_US
dc.journalsSubscriptionen_US
dc.countrySpainen_US
dc.countryNetherlandsen_US
dc.countryBelgiumen_US
dc.countryCyprusen_US
dc.countryFranceen_US
dc.countryUnited Kingdomen_US
dc.countryUnited Statesen_US
dc.subject.fieldMedical and Health Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1177/2048872619858285en_US
dc.identifier.pmid31303009-
dc.relation.issue5en_US
dc.relation.volume9en_US
cut.common.academicyear2019-2020en_US
dc.identifier.spage522en_US
dc.identifier.epage532en_US
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextNo Fulltext-
item.openairetypearticle-
crisitem.author.deptDepartment of Nursing-
crisitem.author.facultyFaculty of Health Sciences-
crisitem.author.orcid0000-0002-2601-8861-
crisitem.author.parentorgFaculty of Health Sciences-
crisitem.journal.journalissn2048-8726-
crisitem.journal.publisherSage-
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