Medical History for Prognostic Risk Assessment and Diagnosis of Stable Patients with Suspected Coronary Artery Disease

James K. Min, Allison Dunning, Heidi Gransar, Stephan Achenbach, Fay Y. Lin, Mouaz Al-Mallah, Matthew J. Budoff, Tracy Q. Callister, Hyuk Jae Chang, Filippo Cademartiri, Erica Maffei, Kavitha Chinnaiyan, Benjamin J.W. Chow, Ralph D'Agostino, Augustin Delago, John Friedman, Martin Hadamitzky, Joerg Hausleiter, Sean W. Hayes, Philipp KaufmannGilbert L. Raff, Leslee J. Shaw, Louise Thomson, Todd Villines, Ricardo C. Cury, Gudrun Feuchtner, Yong Jin Kim, Jonathon Leipsic, Hugo Marques, Daniel S. Berman, Michael Pencina

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

Objective To develop a clinical cardiac risk algorithm for stable patients with suspected coronary artery disease based upon angina typicality and coronary artery disease risk factors. Methods Between 2004 and 2011, 14,004 adults with suspected coronary artery disease referred for cardiac imaging were followed: 1) 9093 patients for coronary computed tomography angiography (CCTA) followed for 2.0 years (CCTA-1); 2) 2132 patients for CCTA followed for 1.6 years (CCTA-2); and 3) 2779 patients for exercise myocardial perfusion scintigraphy (MPS) followed for 5.0 years. A best-fit model from CCTA-1 for prediction of death or myocardial infarction was developed, with integer values proportional to regression coefficients. Discrimination was assessed using C-statistic. The validated model was tested for estimation of the likelihood of obstructive coronary artery disease, defined as ≥50% stenosis, as compared with the method of Diamond and Forrester. Primary outcomes included all-cause mortality and nonfatal myocardial infarction. Secondary outcomes included prevalent angiographically obstructive coronary artery disease. Results In CCTA-1, best-fit model discriminated individuals at risk of death or myocardial infarction (C-statistic 0.76). The integer model ranged from 3 to 13, corresponding to 3-year death risk or myocardial infarction of 0.25% to 53.8%. When applied to CCTA-2 and MPS cohorts, the model demonstrated C-statistics of 0.71 and 0.77, respectively. Both best-fit (C = 0.76; 95% confidence interval [CI], 0.746-0.771) and integer models (C = 0.71; 95% CI, 0.693-0.719) performed better than Diamond and Forrester (C = 0.64; 95% CI, 0.628-0.659) for estimating obstructive coronary artery disease. Conclusions For stable symptomatic patients with suspected coronary artery disease, we developed a history-based method for prediction of death and obstructive coronary artery disease.

Original languageEnglish
Pages (from-to)871-878
Number of pages8
JournalAmerican Journal of Medicine
Volume128
Issue number8
DOIs
StatePublished - 1 Aug 2015

Keywords

  • Coronary artery disease
  • Diagnosis
  • Prognosis

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