Chen Shiqun, Liu Yong, Islam Sheikh Mohammed Shariful, Yao Hua, Zhou Yingling, Chen Ji-Yan, Li Qiang
Department of Cardiology, Provincial Key Laboratory of Coronary Heart Disease, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510100, China.
Guangdong General Hospital Zhuhai Hospital (Zhuhai Golden Bay Center Hospital), Zhuhai, 519000, China.
BMC Cardiovasc Disord. 2018 Jan 16;18(1):7. doi: 10.1186/s12872-018-0745-0.
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).
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.
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).
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.
一种简单的无创模型来预测阻塞性冠状动脉疾病（OCAD）可能会促进风险分层并减轻冠状动脉疾病（CAD）的负担。本研究旨在开发术前无创预测模型，以更好地估计接受选择性冠状动脉造影（CAG）的疑似CAD患者中OCAD的概率。
我们纳入了1262例具有可靠的弗雷明汉风险变量数据的患者，这些患者来自一个前瞻性登记队列，该队列中的患者均无已知CAD且被转诊进行选择性CAG。我们研究了术前OCAD（基于CAG，至少一支主要冠状动脉血管狭窄≥50%）的预测因素。
共有945例（74.9%）参与者患有OCAD。最终的改良弗雷明汉评分（MFS）模型包括贫血、高敏C反应蛋白、左心室射血分数以及五个弗雷明汉因素（年龄、性别、总胆固醇和高密度脂蛋白胆固醇以及高血压）。自助法（1000次）显示该模型具有良好的辨别力（c统计量，0.729±0.0225；95%置信区间，0.69 - 0.77）。MFS提供了足够的拟合优度（P = 0.43），并且在预测OCAD方面表现优于弗雷明汉评分（c统计量，0.703对0.521；P < 0.001），从而识别出OCAD高风险患者（风险评分≥27），在68.8%的受试者中预测值≥70%（低风险[≤17]和极高风险[≥41]评分的范围为37.2 - 87.3%）。
我们的数据表明，大多数基层诊所都可使用的简单MFS风险分层工具，在估计相对稳定的疑似CAD患者中OCAD的概率方面表现良好；然而，仍需要进一步验证。
