• Tessa S S Genders, Ewout W Steyerberg, M G Myriam Hunink, Koen Nieman, Tjebbe W Galema, Nico R Mollet, Pim J de Feyter, Gabriel P Krestin, Hatem Alkadhi, Sebastian Leschka, Lotus Desbiolles, Matthijs F L Meijs, Maarten J Cramer, Juhani Knuuti, Sami Kajander, Jan Bogaert, Kaatje Goetschalckx, Filippo Cademartiri, Erica Maffei, Chiara Martini, Sara Seitun, Annachiara Aldrovandi, Simon Wildermuth, Björn Stinn, Jürgen Fornaro, Gudrun Feuchtner, Tobias De Zordo, Thomas Auer, Fabian Plank, Guy Friedrich, Francesca Pugliese, Steffen E Petersen, L Ceri Davies, U Joseph Schoepf, Garrett W Rowe, Carlos A G van Mieghem, Luc van Driessche, Valentin Sinitsyn, Deepa Gopalan, Konstantin Nikolaou, Fabian Bamberg, Ricardo C Cury, Juan Battle, Pál Maurovich-Horvat, Andrea Bartykowszki, Bela Merkely, Dávid Becker, Martin Hadamitzky, Jörg Hausleiter, Marc Dewey, Elke Zimmermann, and Michael Laule.
• Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, Netherlands.
• BMJ. 2012 Jan 1;344:e3485.

ObjectivesTo develop prediction models that better estimate the pretest probability of coronary artery disease in low prevalence populations.DesignRetrospective pooled analysis of individual patient data.Setting18 hospitals in Europe and the United States.ParticipantsPatients 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 MeasuresObstructive 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.ResultsWe 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.ConclusionsUpdated 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.

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