Medical History for Risk Assessment in Coronary Artery Disease

Study Questions:

What is the utility of medical history-taking alone in predicting risk of myocardial infarction (MI) or death and obstructive coronary artery disease (CAD) in symptomatic stable individuals with suspected CAD?


Between 2004 and 2011, 14,004 adults with suspected CAD referred for cardiac imaging were followed: 1) 9,093 patients for coronary computed tomography angiography (CCTA) followed for 2.0 years (CCTA-1); 2) 2,132 patients for CCTA followed for 1.6 years (CCTA-2); and 3) 2,779 patients for exercise myocardial perfusion scintigraphy (MPS) followed for 5.0 years. A best-fit model from CCTA-1 for prediction of death or MI 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 CAD, defined as ≥50% stenosis, as compared with the method of Diamond and Forrester. Primary outcomes included all-cause mortality and nonfatal MI. Secondary outcomes included prevalent angiographically obstructive CAD.


In CCTA-1, best-fit model discriminated individuals at risk of death or MI (C-statistic 0.76). The integer model ranged from 3 to 13, corresponding to 3-year death risk or MI 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 CAD.


The authors concluded that for stable symptomatic patients with suspected CAD, they have developed a history-based method for prediction of death and obstructive CAD.


This study reports on a simple medical history-based method that predicts risk of death and MI in symptomatic stable patients with suspected CAD. Furthermore, this method exhibits superior performance to traditional methods for identifying individuals with obstructive CAD. While this score may effectively guide the use of noninvasive cardiac imaging and is certainly a step in the right direction, additional prospective studies are indicated to validate this model.

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