Medical history for prognostic risk assessment and diagnosis of stable patients with suspected coronary artery disease
Min JK, Dunning A, Gransar H, Achenbach S, Lin FY, Al-Mallah M, Budoff MJ, Callister TQ, Chang HJ, Cademartiri F, Maffei E, Chinnaiyan K, Chow BJ, 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 YJ, Leipsic J, Berman DS, Pencina M. Medical history for prognostic risk assessment and diagnosis of stable patients with suspected coronary artery disease. Am J Med. 2015 ;128(8):871-8.
The American journal of medicine
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.
Medical Subject Headings
Adolescent; Adult; Algorithms; Coronary Angiography; Coronary Artery Disease; Female; Humans; Male; Medical History Taking; Middle Aged; Myocardial Infarction; Prognosis; Proportional Hazards Models; Reproducibility of Results; Risk Assessment; Risk Factors; Tomography, X-Ray Computed; Young Adult