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

      research-article
      1 , 2 , 3 , 1 , 2 , 27 , , 2 , 4 , 4 , 2 , 4 , 2 , 4 , 2 , 5 , 5 , 12 , 5 , 12 , 6 , 7 , 6 , 8 , 8 , 9 , 9 , 2 , 10 , 11 , 10 , 11 , 10 , 11 , 10 , 10 , 12 , 12 , 12 , 13 , 13 , 13 , 13 , 14 , 15 , 15 , 15 , 16 , 16 , 17 , 18 , 19 , 20 , 21 , 21 , 22 , 22 , 23 , 23 , 23 , 23 , 24 , 24 , 25 , 25 , 26
      BMJ : British Medical Journal
      BMJ Publishing Group Ltd.

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          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.

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          Most cited references25

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          Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus

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            Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease.

            The diagnosis of coronary-artery disease has become increasingly complex. Many different results, obtained from tests with substantial imperfections, must be integrated into a diagnostic conclusion about the probability of disease in a given patient. To approach this problem in a practical manner, we reviewed the literature to estimate the pretest likelihood of disease (defined by age, sex and symptoms) and the sensitivity and specificity of four diagnostic tests: stress electrocardiography, cardiokymography, thallium scintigraphy and cardiac fluoroscopy. With this information, test results can be analyzed by use of Bayes' theorem of conditional probability. This approach has several advantages. It pools the diagnostic experience of many physicians ans integrates fundamental pretest clinical descriptors with many varying test results to summarize reproducibly and meaningfully the probability of angiographic coronary-artery disease. This approach also aids, but does not replace, the physician's judgment and may assit in decisions on cost effectiveness of tests.
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              Long-term prognosis associated with coronary calcification: observations from a registry of 25,253 patients.

              The purpose of this study was to develop risk-adjusted multivariable models that include risk factors and coronary artery calcium (CAC) scores measured with electron-beam tomography in asymptomatic patients for the prediction of all-cause mortality. Several smaller studies have documented the efficacy of CAC testing for assessment of cardiovascular risk. Larger studies with longer follow-up will lend strength to the hypothesis that CAC testing will improve outcomes, cost-effectiveness, and safety of primary prevention efforts. We used an observational outcome study of a cohort of 25,253 consecutive, asymptomatic individuals referred by their primary physician for CAC scanning to assess cardiovascular risk. Multivariable Cox proportional hazards models were developed to predict all-cause mortality. Risk-adjusted models incorporated traditional risk factors for coronary disease and CAC scores. The frequency of CAC scores was 44%, 14%, 20%, 13%, 6%, and 4% for scores of 0, 1 to 10, 11 to 100, 101 to 400, 401 to 1,000, and >1,000, respectively. During a mean follow-up of 6.8 +/- 3 years, the death rate was 2% (510 deaths). The CAC was an independent predictor of mortality in a multivariable model controlling for age, gender, ethnicity, and cardiac risk factors (model chi-square = 2,017, p 1,000, respectively (p 1,000 (p < 0.0001). This large observational data series shows that CAC provides independent incremental information in addition to traditional risk factors in the prediction of all-cause mortality.
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                Author and article information

                Contributors
                Role: clinical epidemiologist
                Role: professor of medical decision making
                Role: professor of radiology and clinical epidemiology (Erasmus) and adjunct professor of health decision sciences (Harvard)
                Role: cardiologist, medical coordinator of ICCU, and assistant professor
                Role: cardiologist
                Role: staff radiologist
                Role: professor of non-invasive cardiac imaging
                Role: professor and chairman of department of radiology
                Role: senior radiologist
                Role: associate professor, senior staff radiologist, section head of computed tomography, and section head of emergency radiology
                Role: staff cardiologist
                Role: resident in cardiology
                Role: associate professor of cardiology
                Role: professor, consultant, and director of centre
                Role: consultant radiologist
                Role: adjunct chair of department of radiology
                Role: cardiologist
                Role: associate professor and head of cardiovascular imaging unit
                Role: staff radiologist
                Role: staff radiologist
                Role: radiologist
                Role: staff cardiologist
                Role: professor of radiology and chairman of institute of radiology
                Role: staff radiologist
                Role: staff radiologist
                Role: associate professor of radiology
                Role: resident in radiology
                Role: resident in radiology
                Role: research fellow
                Role: professor of medicine
                Role: senior clinical lecturer and consultant
                Role: reader in advanced cardiovascular imaging and honorary consultant cardiologist
                Role: reader in advanced cardiovascular imaging and honorary consultant cardiologist
                Role: professor of radiology, medicine, and pediatrics and director of cardiovascular imaging
                Role: program coordinator
                Role: staff cardiologist
                Role: associate director of cardiology and consultant in interventional cardiology
                Role: head of radiology department
                Role: consultant cardiovascular radiologist
                Role: professor of radiology and vice chair of department of clinical radiology
                Role: fellow in radiology
                Role: chairman of department of radiology and director of cardiac imaging
                Role: radiologist
                Role: chairman of department of radiology and director of cardiac imaging
                Role: resident in cardiology
                Role: professor of cardiology and director of heart centre
                Role: associate professor and deputy director heart centre
                Role: director of cardiac magnetic resonance imaging
                Role: assistant medical director
                Role: chief consultant radiologist
                Role: specialist in radiology
                Role: senior physician and deputy director of cardiac catheterisation laboratory
                Journal
                BMJ
                BMJ
                bmj
                BMJ : British Medical Journal
                BMJ Publishing Group Ltd.
                0959-8138
                1756-1833
                2012
                2012
                12 June 2012
                : 344
                : e3485
                Affiliations
                [1 ]Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, Netherlands
                [2 ]Department of Radiology, Erasmus University Medical Centre
                [3 ]Department of Public Health, Erasmus University Medical Centre
                [4 ]Department of Cardiology, Erasmus University Medical Centre
                [5 ]Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
                [6 ]Department of Cardiology, University Medical Centre Utrecht, Utrecht, Netherlands
                [7 ]Department of Radiology, University Medical Centre Utrecht
                [8 ]Turku PET Centre, Turku University Hospital, Turku, Finland
                [9 ]Department of Cardiovascular Diseases, University Hospital Leuven, Leuven, Belgium
                [10 ]Department of Radiology and Cardiology, Azienda Ospedaliero-Universitaria, Parma, Italy
                [11 ]Department of Radiology, Giovanni XXIII Clinic, Monastier, Treviso, Italy
                [12 ]Institute of Radiology, Kantonsspital St Gallen, St Gallen, Switzerland
                [13 ]Department of Radiology, Innsbruck Medical University, Innsbruck, Austria
                [14 ]Department of Cardiology, Innsbruck Medical University
                [15 ]Centre for Advanced Cardiovascular Imaging, Barts and The London NIHR Cardiovascular Biomedical Research Unit, Barts and the London School of Medicine and Dentistry, Barts and the London NHS Trust, London, UK
                [16 ]Department of Radiology, Medical University of South Carolina, Charleston, SC, USA
                [17 ]Department of Cardiology, Onze-Lieve-Vrouwziekenhuis Hospital Aalst, Aalst, Belgium
                [18 ]Department of Cardiology, St Blasius Hospital Dendermonde, Belgium
                [19 ]Department of Radiology, Federal Centre of Medicine and Rehabilitation, Moscow, Russia
                [20 ]Department of Radiology, Papworth Hospital NHS Trust, Cambridge, UK
                [21 ]Department of Clinical Radiology, University Hospitals Munich, Munich, Germany
                [22 ]Department of Radiology, Baptist Hospital of Miami and Baptist Cardiac and Vascular Institute, Miami, FL, USA
                [23 ]Heart Centre, Semmelweis University, Budapest, Hungary
                [24 ]Department of Cardiology, German Heart Centre, Munich, Germany
                [25 ]Department of Radiology, Charité, Medical School, Humboldt University, Berlin, Germany
                [26 ]Department of Cardiology, Charité, Medical School
                [27 ]Department of Health Policy and Management, Harvard School of Public Health, Harvard University, Boston, MA, USA
                Author notes
                Correspondence to: M G M Hunink, Departments of Epidemiology and Radiology, Erasmus University Medical Centre, PO Box 2040, 3000 CA Rotterdam, Netherlands m.hunink@ 123456erasmusmc.nl
                Article
                gent001068
                10.1136/bmj.e3485
                3374026
                22692650
                13a9c96c-9bc2-4ae6-b281-388e06b99e96
                © Genders et al 2012

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.

                History
                : 14 April 2012
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