Medical history for prognostic risk assessment and diagnosis of stable patients with suspected coronary artery disease.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Min, JK; Dunning, A; Gransar, H; Achenbach, S; Lin, FY; Al-Mallah, M; Budoff, MJ; Callister, TQ; Chang, H-J; Cademartiri, F; Maffei, E; Chinnaiyan, K; Chow, BJW; D'Agostino, R; DeLago, A; Friedman, J; Hadamitzky, M; Hausleiter, J; Hayes, SW; Kaufmann, P; Raff, GL; Shaw, LJ; Thomson, L; Villines, T; Cury, RC; Feuchtner, G; Kim, Y-J; Leipsic, J; Marques, H; Berman, DS; Pencina, M

Published Date

  • August 2015

Published In

Volume / Issue

  • 128 / 8

Start / End Page

  • 871 - 878

PubMed ID

  • 25865923

Pubmed Central ID

  • 25865923

Electronic International Standard Serial Number (EISSN)

  • 1555-7162

Digital Object Identifier (DOI)

  • 10.1016/j.amjmed.2014.10.031

Language

  • eng

Conference Location

  • United States