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摘要
Background:A simple noninvasive model to predict obstructive coronary artery disease (OCAD) may promote risk stratification and reduce the burden of coronary artery disease (CAD). This study aimed to develop pre-procedural, noninvasive prediction models that better estimate the probability of OCAD among patients with suspected CAD undergoing elective coronary angiography (CAG). @@@ Methods: We included 1262 patients, who had reliable Framingham risk variable data, in a cohort without known CAD from a prospective registry of patients referred for elective CAG. We investigated pre-procedural OCAD (>= 50% stenosis in at least one major coronary vessel based on CAG) predictors. @@@ Results: A total of 945 (74.9%) participants had OCAD. The final modified Framingham scoring (MFS) model consisted of anemia, high-sensitivity C-reactive protein, left ventricular ejection fraction, and five Framingham factors (age, sex, total and high-density lipoprotein cholesterol, and hypertension). Bootstrap method (1000 times) revealed that the model demonstrated a good discriminative power (c statistic, 0.729 +/- 0.0225; 95% CI, 0.69-0.77). MFS provided adequate goodness of fit (P = 0.43) and showed better performance than Framingham score (c statistic, 0.703 vs. 0.521; P<0.001) in predicting OCAD, thereby identifying patients with high risks for OCAD (risk score >= 27) with >= 70% predictive value in 68.8% of subjects (range, 37.2-87.3% for low [<= 17] and very high [>= 41] risk scores). @@@ Conclusion: Our data suggested that the simple MFS risk stratification tool, which is available in most primary-level clinics, showed good performance in estimating the probability of OCAD in relatively stable patients with suspected CAD; nevertheless, further validation is needed.
- 出版日期2018-1-16
- 单位广东省人民医院; 广东省心血管病研究所
