Concordance evaluation of coronary risk scores: implications for cardiovascular risk screening.
Objective: To assess the similarities and differences in predicted high-risk individuals identified by different cardiovascular risk calculation algorithms Research design and methods: A representative population of 10000 individuals was modelled in a computer using baseline data from the National H...
| Publicado en: | Current Medical Research & Opinion Vol. 20; no. 6; pp. 811 - 819 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=106682234&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 106682234 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03007995 DFY jtl: Current Medical Research & Opinion issn: 03007995 maglogo: N pubinfo: vid: 20 iid: 6 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 106682234 106682234 NLM15200737 2004141222 NLM15200737 106682234 ppf: 811 ppct: 8 formats: tig: atl: Concordance evaluation of coronary risk scores: implications for cardiovascular risk screening. aug: au: Reynolds TM Twomey PJ Wierzbicki AS Reynolds, Timothy M Twomey, Patrick J Wierzbicki, Anthony S affil: Consultant Chemical Pathologist, Queen's Hospital, Burton-on-Trent and Division of Clinical Sciences, Wolverhampton University, UK sug: subj: Cardiovascular Risk Factors Evaluation Cardiovascular Risk Factors Prevention and Control Computer Simulation England Female Funding Source Male Risk Assessment Human Female Male ab: Objective: To assess the similarities and differences in predicted high-risk individuals identified by different cardiovascular risk calculation algorithms Research design and methods: A representative population of 10000 individuals was modelled in a computer using baseline data from the National Health Survey for England. The effects of biological groups identified by each calculator depend on the variation in each major model parameters were then applied to each hypothetical individual. The predictive capacities of 3 different risk identification systems based on computer calculation (the Framingham algorithm), or on tabular methods (the Sheffield tables and the General Rule to Enable Atheroma Treatment) were evaluated. Results: All three models predict that similar numbers would receive treatment with 2.9 and 10% receiving treatment at 30 and 15% 10 year risk thresholds, respectively. However, concordance is limited as 0.3 or 6.8% are positive on all three systems; 1.6 or 9.7% on any two calculators at the 30 and 15% thresholds, respectively. The risk baseline assumptions in each model. Conclusion: Care needs to be taken with applying risk calculators to populations different from which they were derived. Any cardiovascular risk scoring system needs to be thoroughly evaluated against epidemiological data before it is introduced and also needs to be updated in line with changing trends in risk factors. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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