Development of a diagnosis model for coronary artery disease

Abstract

Background: The purpose of this study was to develop a coronary artery disease (CAD) prediction model that optimally estimates the pre-test probability of CAD for patients suspected of CAD.

Methods and results: This retrospective, multi-centre study included 7360 consecutive patients (4678 men, 57.87±11.42 years old; 2682 women, 61.60±9.58 years old) who underwent coronary angiography for evaluation of CAD. A prediction model was fitted for diagnosis of CAD with the help of eight significant risk factors including sex, age, smoking status, diabetes, hypertension, dyslipidaemia, serum creatinine and angina. All potential predictors were significantly associated with the presence of CAD. The prevalence of CAD was significantly higher in men than in women. The clinical model gives a relatively accurate prediction of CAD with an area under the curve (AUC) of 0.74 (95% CI, 0.88-0.96; P<0.001). Addition of angina to the prediction model improves the predictive precision of the model. The optimal cut-off for predicting CAD in this model was 0.79 with a sensitivity of 0.658 and a specificity of 0.709.

Conclusion: A prediction model including age, sex, and cardiovascular risk factors allow for an accurate estimation of the pre-test probability of coronary artery disease in Chinese populations. This algorithm may be useful in making decisions relating to the diagnosis of CAD.

Keywords: Coronary artery disease; Diagnosis; Logistic model; Risk factors.

Fig. 1

Receiver operating characteristic (ROC) curve of the CAD diagnosis model for the diagnosis of coronary atherosclerosis using coronary angiography. This ROC curve provides a pure measure of the diagnostic accuracy of the test. The data from all patients were used to generate these curves. The area under the ROC curve (AUC) was 0.737 (P < 0.01, B). Without angina, the AUC was 0.727 (P < 0.01, A). C and D show the discriminative curves for males and females, respectively.