Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/14995
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.accessioned2019-08-27T10:20:55Z-
dc.date.available2019-08-27T10:20:55Z-
dc.date.issued2019-10-01-
dc.identifier.citationEuropean Journal of Cardiovascular Nursing, 2019, vol. 18, no. 7, pp. 534-544en_US
dc.identifier.issn14745151-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/14995-
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.language.isoenen_US
dc.relation.ispartofEuropean Journal of Cardiovascular Nursingen_US
dc.rights© The European Society of Cardiologyen_US
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.collaborationCentro Nacional de Investigaciones Cardiovasculares (CNIC)en_US
dc.collaborationCentro de Investigación Biomédica en Red en Enfermedades Cardiovasculares (CIBERCV)en_US
dc.collaborationUniversity Medical Center Utrechten_US
dc.collaborationJessa Hospitalen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationHasselt Universityen_US
dc.collaborationHôpital cardiologique de Lyonen_US
dc.collaborationImperial College Londonen_US
dc.collaborationG da Saliceto Hospitalen_US
dc.collaborationUniversity of Southern Californiaen_US
dc.subject.categoryMEDICAL AND HEALTH 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.countryItalyen_US
dc.countryUnited Statesen_US
dc.subject.fieldMedical and Health Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1177/1474515119856207en_US
dc.identifier.pmid31234638-
dc.identifier.scopus2-s2.0-85068308549-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85068308549-
dc.relation.issue7en_US
dc.relation.volume18en_US
cut.common.academicyear2019-2020en_US
dc.identifier.spage534en_US
dc.identifier.epage544en_US
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.languageiso639-1en-
crisitem.journal.journalissn1873-1953-
crisitem.journal.publisherSage-
crisitem.author.deptDepartment of Nursing-
crisitem.author.facultyFaculty of Health Sciences-
crisitem.author.orcid0000-0002-2601-8861-
crisitem.author.parentorgFaculty of Health Sciences-
Appears in Collections:Άρθρα/Articles
CORE Recommender
Show simple item record

SCOPUSTM   
Citations

75
checked on Feb 2, 2024

WEB OF SCIENCETM
Citations

20
Last Week
0
Last month
0
checked on Oct 29, 2023

Page view(s) 50

333
Last Week
4
Last month
13
checked on May 11, 2024

Google ScholarTM

Check

Altmetric


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.