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dc.contributor.authorHenriksson, Martin
dc.contributor.authorPalmer, Stephen
dc.contributor.authorChen, Ruoling
dc.contributor.authorDamant, Jacqueline
dc.contributor.authorFitzpatrick, Natalie K
dc.contributor.authorAbrams, Keith
dc.contributor.authorHingorani, Aroon D
dc.contributor.authorStenestrand, Ulf
dc.contributor.authorJanzon, Magnus
dc.contributor.authorFeder, Gene
dc.contributor.authorKeogh, Bruce
dc.contributor.authorShipley, Martin J
dc.contributor.authorKaski, Juan-Carlos
dc.contributor.authorTimmis, Adam
dc.contributor.authorSculpher, Mark
dc.contributor.authorHemingway, Harry
dc.date.accessioned2018-08-30T09:53:45Z
dc.date.available2018-08-30T09:53:45Z
dc.date.issued2010-01-20
dc.identifier.issn0959-8138
dc.identifier.pmid20085988
dc.identifier.doi10.1136/bmj.b5606
dc.identifier.urihttp://hdl.handle.net/2436/621656
dc.description.abstractTo determine the effectiveness and cost effectiveness of using information from circulating biomarkers to inform the prioritisation process of patients with stable angina awaiting coronary artery bypass graft surgery. Decision analytical model comparing four prioritisation strategies without biomarkers (no formal prioritisation, two urgency scores, and a risk score) and three strategies based on a risk score using biomarkers: a routinely assessed biomarker (estimated glomerular filtration rate), a novel biomarker (C reactive protein), or both. The order in which to perform coronary artery bypass grafting in a cohort of patients was determined by each prioritisation strategy, and mean lifetime costs and quality adjusted life years (QALYs) were compared. Swedish Coronary Angiography and Angioplasty Registry (9935 patients with stable angina awaiting coronary artery bypass grafting and then followed up for cardiovascular events after the procedure for 3.8 years), and meta-analyses of prognostic effects (relative risks) of biomarkers. The observed risk of cardiovascular events while on the waiting list for coronary artery bypass grafting was 3 per 10,000 patients per day within the first 90 days (184 events in 9935 patients). Using a cost effectiveness threshold of pound20,000- pound30,000 (euro22,000-euro33,000; $32,000-$48,000) per additional QALY, a prioritisation strategy using a risk score with estimated glomerular filtration rate was the most cost effective strategy (cost per additional QALY was < pound410 compared with the Ontario urgency score). The impact on population health of implementing this strategy was 800 QALYs per 100,000 patients at an additional cost of pound 245,000 to the National Health Service. The prioritisation strategy using a risk score with C reactive protein was associated with lower QALYs and higher costs compared with a risk score using estimated glomerular filtration rate.
dc.formatapplication/PDF
dc.language.isoen
dc.publisherBMJ
dc.relation.urlhttps://www.bmj.com/content/340/bmj.b5606
dc.titleAssessing the cost effectiveness of using prognostic biomarkers with decision models: case study in prioritising patients waiting for coronary artery surgery.
dc.typeJournal article
dc.identifier.journalBMJ
dc.date.accepted2009-10-27
rioxxterms.funderUniversity of Wolverhampton
rioxxterms.identifier.projectUOW300818RC
rioxxterms.versionAM
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
rioxxterms.licenseref.startdate2010-01-20
dc.source.journaltitleBMJ (Clinical research ed.)
refterms.dateFCD2018-08-30T09:53:46Z
refterms.versionFCDAM
refterms.dateFOA2018-08-30T09:53:46Z


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