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

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, Michael Laule, 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, Michael Laule

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.

Conflict of interest statement

Competing interests: All authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: support from the Erasmus University Medical Centre; FP, SEP, and LCD’s work contributes to the translational research portfolio of Barts and the London Cardiovascular Biomedical Research Unit, which is supported and funded by the National Institute for Health Research; UJS is a consultant for Bayer and Siemens and provides research support to Bayer, Bracco, General Electric, and Siemens; MD holds research grants (European Regional Development Fund, German Heart Foundation/German Foundation of Heart Research, GE Healthcare (Amersham), Bracco, Guerbet, and Toshiba Medical Systems), receives speaker fees (Toshiba Medical Systems, Guerbet, and Bayer-Schering), runs cardiac CT workshops at Charité (www.ct-kurs.de), has written the book Coronary CT Angiography (Springer, 2008), and is involved in institutional research collaborations (Siemens Medical Solutions, Philips Medical Systems, and Toshiba Medical Systems); FC is a consultant for Servier, received speaker fees (Bracco Imaging), and holds a research grant with HE Healthcare; EM is a consultant for Servier and holds a research grants with HE Healthcare; FB has received speakers fees from Siemens Healthcare; all other authors have no financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years and no other relationships or activities that could appear to have influenced the submitted work.

Figures

https://www.ncbi.nlm.nih.gov/pmc/articles/instance/4789856/bin/gent001068.f1_default.jpg
Fig 1 Calibration plot of the Duke clinical score, in low prevalence datasets (n=4426). Distribution of predicted probabilities shown separately for patients with and without severe coronary artery disease. Triangles indicate observed proportions of severe disease, by tenths of predicted probability; 95% CI=confidence interval
https://www.ncbi.nlm.nih.gov/pmc/articles/instance/4789856/bin/gent001068.f2_default.jpg
Fig 2 Validity of clinical model using the four largest low prevalence datasets and the smaller remaining low prevalence databases combined. Distribution of predicted probabilities shown separately for patients with and without obstructive coronary artery disease. Triangles indicate observed proportion of disease, by tenths of the predicted probability; 95% CI=95% confidence interval

References

    1. American Heart Association. Heart disease and stroke statistics—2010 update. 2010.
    1. DeFrances CJ, Lucas CA, Buie VC, Golosinskiy A. 2006 national hospital discharge survey. Natl Health Stat Report 2008:1-20.
    1. Patel MR, Peterson ED, Dai D, Brennan JM, Redberg RF, Anderson HV, et al. Low diagnostic yield of elective coronary angiography. N Engl J Med 2010;362:886-95.
    1. Genders TS, Meijboom WB, Meijs MF, Schuijf JD, Mollet NR, Weustink AC, et al. CT coronary angiography in patients suspected of having coronary artery disease: decision making from various perspectives in the face of uncertainty. Radiology 2009;253:734-44.
    1. Ladapo JA, Jaffer FA, Hoffmann U, Thomson CC, Bamberg F, Dec W, et al. Clinical outcomes and cost-effectiveness of coronary computed tomography angiography in the evaluation of patients with chest pain. J Am Coll Cardiol 2009;54:2409-22.
    1. Min JK, Gilmore A, Budoff MJ, Berman DS, O’Day K. Cost-effectiveness of coronary CT angiography versus myocardial perfusion SPECT for evaluation of patients with chest pain and no known coronary artery disease. Radiology 2010;254:801-8.
    1. Gibbons RJ, Balady GJ, Bricker JT, Chaitman BR, Fletcher GF, Froelicher VF, et al. ACC/AHA 2002 guideline update for exercise testing: summary article: a report of the American College of Cardiology/American Heart Association task force on practice guidelines (committee to update the 1997 exercise testing guidelines). Circulation 2002;106:1883-92.
    1. Fraker TD Jr, Fihn SD, Chronic Stable Angina writing committee, American College of Cardiology, American Heart Association, Gibbons RJ, et al. 2007 chronic angina focused update of the ACC/AHA 2002 guidelines for the management of patients with chronic stable angina: a report of the American College of Cardiology/American Heart Association task force on practice guidelines writing group to develop the focused update of the 2002 guidelines for the management of patients with chronic stable angina. J Am Coll Cardiol 2007;50:2264-74.
    1. Fox K, Garcia MA, Ardissino D, Buszman P, Camici PG, Crea F, et al. Guidelines on the management of stable angina pectoris: executive summary—the task force on the management of stable angina pectoris of the European Society of Cardiology. Eur Heart J 2006;27:1341-81.
    1. Cooper A, Calvert N, Skinner J, Sawyer L, Sparrow K, Timmis A, et al. Chest pain of recent onset: assessment and diagnosis of recent onset chest pain or discomfort of suspected cardiac origin. National Clinical Guideline Centre for Acute and Chronic Conditions, 2010.
    1. Diamond GA, Forrester JS. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. N Engl J Med 1979;300:1350-8.
    1. Pryor DB, Harrell FE Jr, Lee KL, Califf RM, Rosati RA. Estimating the likelihood of significant coronary artery disease. Am J Med 1983;75:771-80.
    1. Pryor DB, Shaw L, McCants CB, Lee KL, Mark DB, Harrell FE Jr, et al. Value of the history and physical in identifying patients at increased risk for coronary artery disease. Ann Intern Med 1993;118:81-90.
    1. Genders TS, Steyerberg EW, Alkadhi H, Leschka S, Desbiolles L, Nieman K, et al. A clinical prediction rule for the diagnosis of coronary artery disease: validation, updating, and extension. Eur Heart J 2011;32:1316-30.
    1. European Institute for Biomedical Imaging Research. Homepage. 2011. .
    1. Schuetz GM, Zacharopoulou NM, Schlattmann P, Dewey M. Meta-analysis: noninvasive coronary angiography using computed tomography versus magnetic resonance imaging. Ann Intern Med 2010;152:167-77.
    1. Mowatt G, Cook JA, Hillis GS, Walker S, Fraser C, Jia X, et al. 64-slice computed tomography angiography in the diagnosis and assessment of coronary artery disease: systematic review and meta-analysis. Heart 2008;94:1386-93.
    1. von Ballmoos MW, Haring B, Juillerat P, Alkadhi H. Meta-analysis: diagnostic performance of low-radiation-dose coronary computed tomography angiography. Ann Intern Med 2011;154:413-20.
    1. Expert committee on the diagnosis and classification of diabetes mellitus. Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care 2003;26(suppl):S5-20.
    1. European Society of Hypertension-European Society of Cardiology Guidelines Committee. 2003 European Society of Hypertension-European Society of Cardiology guidelines for the management of arterial hypertension. J Hypertens 2003;21:1011-53.
    1. Expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). Executive summary of the third report of the National Cholesterol Education Program (NCEP). JAMA 2001;285:2486-97.
    1. Diamond GA. A clinically relevant classification of chest discomfort. J Am Coll Cardiol 1983;1:574-5.
    1. Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M Jr, Detrano R. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol 1990;15:827-32.
    1. van Buuren S, Boshuizen HC, Knook DL. Multiple imputation of missing blood pressure covariates in survival analysis. Stat Med 1999;18:681-94.
    1. Rubin DB, Schenker N. Multiple imputation in health-care databases: an overview and some applications. Stat Med 1991;10:585-98.
    1. Steyerberg EW, Borsboom GJ, van Houwelingen HC, Eijkemans MJ, Habbema JD. Validation and updating of predictive logistic regression models: a study on sample size and shrinkage. Stat Med 2004;23:2567-86.
    1. Begg CB, Greenes RA. Assessment of diagnostic tests when disease verification is subject to selection bias. Biometrics 1983;39:207-15.
    1. de Groot JA, Bossuyt PM, Reitsma JB, Rutjes AW, Dendukuri N, Janssen KJ, et al. Verification problems in diagnostic accuracy studies: consequences and solutions. BMJ 2011;343:d4770.
    1. Yamada H, Do D, Morise A, Atwood JE, Froelicher V. Review of studies using multivariable analysis of clinical and exercise test data to predict angiographic coronary artery disease. Prog Cardiovasc Dis 1997;39:457-81.
    1. Harrell FE. Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Springer, 2001.
    1. Steyerberg EW. Clinical prediction models: a practical approach to development, validation and updating. Springer, 2008.
    1. Pencina MJ, D’Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 2011;30:11-21.
    1. Vergouwe Y, Steyerberg EW, Eijkemans MJ, Habbema JD. Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol 2005;58:475-83.
    1. Hunink MGM, Glasziou PP, Siegel JE, Weeks J, Pliskin J, Elstein A, et al. Decision making in health and medicine: Integrating evidence and values. Cambridge University Press, 2001.
    1. Greenland P, Bonow RO, Brundage BH, Budoff MJ, Eisenberg MJ, Grundy SM, et al. ACCF/AHA 2007 clinical expert consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain: a report of the American College of Cardiology Foundation clinical expert consensus task force (ACCF/AHA writing committee to update the 2000 expert consensus document on electron beam computed tomography) developed in collaboration with the Society of Atherosclerosis Imaging and Prevention and the Society of Cardiovascular Computed Tomography. J Am Coll Cardiol 2007;49:378-402.
    1. Kwok Y, Kim C, Grady D, Segal M, Redberg R. Meta-analysis of exercise testing to detect coronary artery disease in women. Am J Cardiol 1999;83:660-6.
    1. Budoff MJ, Diamond GA, Raggi P, Arad Y, Guerci AD, Callister TQ, et al. Continuous probabilistic prediction of angiographically significant coronary artery disease using electron beam tomography. Circulation 2002;105:1791-6.
    1. Haberl R, Becker A, Leber A, Knez A, Becker C, Lang C, et al. Correlation of coronary calcification and angiographically documented stenoses in patients with suspected coronary artery disease: results of 1,764 patients. J Am Coll Cardiol 2001;37:451-7.
    1. Knez A, Becker A, Leber A, White C, Becker CR, Reiser MF, et al. Relation of coronary calcium scores by electron beam tomography to obstructive disease in 2,115 symptomatic patients. Am J Cardiol 2004;93:1150-2.
    1. Budoff MJ, Shaw LJ, Liu ST, Weinstein SR, Mosler TP, Tseng PH, et al. Long-term prognosis associated with coronary calcification: observations from a registry of 25,253 patients. J Am Coll Cardiol 2007;49:1860-70.
    1. Dedic A, Genders TS, Ferket BS, Galema TW, Mollet NR, Moelker A, et al. Stable angina pectoris: head-to-head comparison of prognostic value of cardiac CT and exercise testing. Radiology 2011. 261:428-36.
    1. Elias-Smale SE, Proenca RV, Koller MT, Kavousi M, van Rooij FJ, Hunink MG, et al. Coronary calcium score improves classification of coronary heart disease risk in the elderly: the Rotterdam study. J Am Coll Cardiol 2010;56:1407-14.
    1. Hadamitzky M, Distler R, Meyer T, Hein F, Kastrati A, Martinoff S, et al. Prognostic value of coronary computed tomographic angiography in comparison with calcium scoring and clinical risk scores. Circ Cardiovasc Imaging 2011;4:16-23.

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