Validation of a Novel Clinical Prediction Score for Severe Coronary Artery Diseases before Elective Coronary Angiography

OBJECTIVES: Coronary artery disease (CAD) severity is associated with patient prognosis. However, few efficient scoring systems have been developed to screen severe CAD in patients with stable angina and suspected CAD before coronary angiography. Here, we present a novel scoring system for CAD severity before elective coronary angiography. METHODS: Five hundred fifty-one patients with stable angina who were admitted for coronary angiography were enrolled in this study. Patients were divided into training (n = 347) and validation (n = 204) cohorts. Severe CAD was defined as having a Gensini score of 20 or more. All patients underwent echocardiography (ECG) to detect ejection fraction and aortic valve calcification (AVC). Multivariable analysis was applied to determine independent risk factors and develop the scoring system. RESULTS: In the training cohort, age, male sex, AVC, abnormal ECG, diabetes, hyperlipidemia, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol were identified as independent factors for severe CAD by multivariable analysis, and the Severe Prediction Scoring (SPS) system was developed. C-indices of receiver operating characteristic (ROC) curves for severe CAD were 0.744 and 0.710 in the training and validation groups, respectively. The SPS system also performed well during calibration, as demonstrated by Hosmer-Lemeshow analysis in the validation group. Compared with the Diamond-Forrester score, the SPS system performed better for severe CAD prediction before elective coronary angiography. CONCLUSIONS: Severe CAD prediction was achieved by analyzing age, sex, AVC, ECG, diabetes status, and lipid levels. Angina patients who achieve high scores using this predicting system should undergo early coronary angiography.