Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts
Wissenschaftlicher Artikel/Review - 12.06.2012
Laule Michael, Sinitsyn Valentin, van Driessche Luc, van Mieghem Carlos A G, Rowe Garrett W, Schoepf U Joseph, Davies L Ceri, Petersen Steffen E, Pugliese Francesca, Friedrich Guy, Plank Fabian, Auer Thomas, Gopalan Deepa, Nikolaou Konstantin, Zimmermann Elke, Dewey Marc, Hausleiter Jörg, Hadamitzky Martin, Becker Dávid, Merkely Bela, Bartykowszki Andrea, Maurovich-Horvat Pal, Battle Juan, Cury Ricardo C, Bamberg Fabian, De Zordo Tobias, Feuchtner Gudrun, Meijs Matthijs F L, Desbiolles Lotus, Leschka Sebastian, Alkadhi Hatem, Krestin Gabriel P, de Feyter Pim J, Mollet Nico R, Galema Tjebbe W, Nieman Koen, Hunink M G Myriam, Steyerberg Ewout W, Cramer Maarten J, Knuuti Juhani, Fornaro Jürgen, Stinn Björn, Wildermuth Simon, Aldrovandi Annachiara, Seitun Sara, Martini Chiara, Maffei Erica, Cademartiri Filippo, Goetschalckx Kaatje, Bogaert Jan, Kajander Sami, Genders Tessa S S
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.
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.
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.
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.