Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Genders TS

PROVIDER: S-EPMC3374026 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Publications

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

Genders Tessa S S TS Steyerberg Ewout W EW Hunink M G Myriam MG Nieman Koen K Galema Tjebbe W TW Mollet Nico R NR de Feyter Pim J PJ Krestin Gabriel P GP Alkadhi Hatem H Leschka Sebastian S Desbiolles Lotus L Meijs Matthijs F L MF Cramer Maarten J MJ Knuuti Juhani J Kajander Sami S Bogaert Jan J Goetschalckx Kaatje K Cademartiri Filippo F Maffei Erica E Martini Chiara C Seitun Sara S Aldrovandi Annachiara A Wildermuth Simon S Stinn Björn B Fornaro Jürgen J Feuchtner Gudrun G De Zordo Tobias T Auer Thomas T Plank Fabian F Friedrich Guy G Pugliese Francesca F Petersen Steffen E SE Davies L Ceri LC Schoepf U Joseph UJ Rowe Garrett W GW van Mieghem Carlos A G CA van Driessche Luc L Sinitsyn Valentin V Gopalan Deepa D Nikolaou Konstantin K Bamberg Fabian F Cury Ricardo C RC Battle Juan J Maurovich-Horvat Pál P Bartykowszki Andrea A Merkely Bela B Becker Dávid D Hadamitzky Martin M Hausleiter Jörg J Dewey Marc M Zimmermann Elke E Laule Michael M

BMJ (Clinical research ed.) 20120612

<h4>Objectives</h4>To develop prediction models that better estimate the pretest probability of coronary artery disease in low prevalence populations.<h4>Design</h4>Retrospective pooled analysis of individual patient data.<h4>Setting</h4>18 hospitals in Europe and the United States.<h4>Participants</h4>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 angi ...[more]

PMID: 22692650

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Dataset Information

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

Objectives

Design

Setting

Participants

Main outcome measures

Results

Conclusions

Publications

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

Similar Datasets

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