Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts

Publication

Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts

Journal Paper/Review - Jun 12, 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

Units

Radiologie und Nuklearmedizin

PubMed

22692650

Citation

Laule M, Sinitsyn V, van Driessche L, van Mieghem C, Rowe G, Schoepf U, Davies L, Petersen S, Pugliese F, Friedrich G, Plank F, Auer T, Gopalan D, Nikolaou K, Zimmermann E, Dewey M, Hausleiter J, Hadamitzky M, Becker D, Merkely B, Bartykowszki A, Maurovich-Horvat P, Battle J, Cury R, Bamberg F, De Zordo T, Feuchtner G, Meijs M, Desbiolles L, Leschka S, Alkadhi H, Krestin G, de Feyter P, Mollet N, Galema T, Nieman K, Hunink M, Steyerberg E, Cramer M, Knuuti J, Fornaro J, Stinn B, Wildermuth S, Aldrovandi A, Seitun S, Martini C, Maffei E, Cademartiri F, Goetschalckx K, Bogaert J, Kajander S, Genders T. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts. BMJ 2012; 344:e3485.

Type

Journal Paper/Review (English)

Journal

BMJ 2012; 344

Publication Date

Jun 12, 2012

Issn Electronic

1756-1833

Pages

e3485

Brief description/objective

OBJECTIVES
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.