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dc.contributor.authorGenders, Tessa S S
dc.contributor.authorSteyerberg, Ewout W
dc.contributor.authorNieman, Koen
dc.contributor.authorGalema, Tjebbe W
dc.contributor.authorMollet, Nico R
dc.contributor.authorde Feyter, Pim J
dc.contributor.authorKrestin, Gabriel P
dc.contributor.authorAlkadhi, Hatem
dc.contributor.authorLeschka, Sebastian
dc.contributor.authorDesbiolles, Lotus
dc.contributor.authorMeijs, Matthijs F L
dc.contributor.authorCramer, Maarten J
dc.contributor.authorKnuuti, Juhani
dc.contributor.authorKajander, Sami
dc.contributor.authorBogaert, Jan
dc.contributor.authorGoetschalckx, Kaatje
dc.contributor.authorCademartiri, Filippo
dc.contributor.authorMaffei, Erica
dc.contributor.authorMartini, Chiara
dc.contributor.authorSeitun, Sara
dc.contributor.authorAldrovandi, Annachiara
dc.contributor.authorWildermuth, Simon
dc.contributor.authorStinn, Björn
dc.contributor.authorFornaro, Jürgen
dc.contributor.authorFeuchtner, Gudrun
dc.contributor.authorDe Zordo, Tobias
dc.contributor.authorAuer, Thomas
dc.contributor.authorPlank, Fabian
dc.contributor.authorFriedrich, Guy
dc.contributor.authorPugliese, Francesca
dc.contributor.authorSchoepf, U Joseph
dc.contributor.authorRowe, Garrett W
dc.contributor.authorvan Mieghem, Carlos A G
dc.contributor.authorvan Driessche, Luc
dc.contributor.authorSinitsyn, Valentin
dc.contributor.authorGopalan, Deepa
dc.contributor.authorNikolaou, Konstantin
dc.contributor.authorBamberg, Fabian
dc.contributor.authorCury, Ricardo C
dc.contributor.authorBattle, Juan
dc.contributor.authorMaurovich-Horvat, Pál
dc.contributor.authorBartykowszki, Andrea
dc.contributor.authorMerkely, Bela
dc.contributor.authorBecker, Dávid
dc.contributor.authorHadamitzky, Martin
dc.contributor.authorHausleiter, Jörg
dc.contributor.authorDewey, Marc
dc.contributor.authorZimmermann, Elke
dc.contributor.authorLaule, Michael
dc.contributor.authorHunink, Maria G M
dc.contributor.authorPetersen, Steffen Erhard
dc.contributor.authorDavies, L. Ceri
dc.date.accessioned2013-04-03T19:24:08Z
dc.date.issued2012
dc.identifier.citationGenders, Tessa S S, Ewout W Steyerberg, M G Myriam Hunink, Koen Nieman, Tjebbe W Galema, Nico R Mollet, Pim J de Feyter, et al. 2012. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts. BMJ : British Medical Journal 344:e3485.en_US
dc.identifier.issn0959-8138en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:10497278
dc.description.abstractObjectives: To develop prediction models that better estimate the pretest probability of coronary artery disease in low prevalence populations. Design Retrospective pooled analysis of individual patient data. Setting 18 hospitals in Europe and the United States. Participants Patients with stable chest pain without evidence for previous coronary artery disease, if they were referred for computed tomography (CT) based coronary angiography or catheter based coronary angiography (indicated as low and high prevalence settings, respectively). Main outcome measures Obstructive coronary artery disease (≥50% diameter stenosis in at least one vessel found on catheter based coronary angiography). Multiple imputation accounted for missing predictors and outcomes, exploiting strong correlation between the two angiography procedures. Predictive models included a basic model (age, sex, symptoms, and setting), clinical model (basic model factors and diabetes, hypertension, dyslipidaemia, and smoking), and extended model (clinical model factors and use of the CT based coronary calcium score). We assessed discrimination (c statistic), calibration, and continuous net reclassification improvement by cross validation for the four largest low prevalence datasets separately and the smaller remaining low prevalence datasets combined. Results: We included 5677 patients (3283 men, 2394 women), of whom 1634 had obstructive coronary artery disease found on catheter based coronary angiography. All potential predictors were significantly associated with the presence of disease in univariable and multivariable analyses. The clinical model improved the prediction, compared with the basic model (cross validated c statistic improvement from 0.77 to 0.79, net reclassification improvement 35%); the coronary calcium score in the extended model was a major predictor (0.79 to 0.88, 102%). Calibration for low prevalence datasets was satisfactory. Conclusions: Updated prediction models including age, sex, symptoms, and cardiovascular risk factors allow for accurate estimation of the pretest probability of coronary artery disease in low prevalence populations. Addition of coronary calcium scores to the prediction models improves the estimates.en_US
dc.language.isoen_USen_US
dc.publisherBMJ Publishing Group Ltd.en_US
dc.relation.isversionofdoi:10.1136/bmj.e3485en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3374026/pdf/en_US
dash.licenseLAA
dc.subjectSmoking and Tobaccoen_US
dc.subjectUKen_US
dc.subjectUSen_US
dc.subjectDrugs: Cardiovascular Systemen_US
dc.subjectPain (Neurology)en_US
dc.subjectHypertensionen_US
dc.subjectIschaemic Heart Diseaseen_US
dc.subjectRadiologyen_US
dc.subjectClinical Diagnostic Testsen_US
dc.subjectRadiology (Diagnostics)en_US
dc.subjectHealth Educationen_US
dc.subjectHealth Promotionen_US
dc.subjectSmokingen_US
dc.titlePrediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohortsen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalBMJ : British Medical Journalen_US
dash.depositing.authorHunink, Maria G M
dc.date.available2013-04-03T19:24:08Z
dc.identifier.doi10.1136/bmj.e3485*
dash.authorsorderedfalse
dash.contributor.affiliatedHunink, Maria
dash.contributor.affiliatedPetersen, Steffen Erhard


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