摘要
目的通过比较最终出现心血管终点事件的心血管疾病患者组与对照组的CT血流储备分数(computed tomography fractional flow reserve,CT-FFR)值,探讨CT-FFR值与心血管疾病预后的关系。方法收集2013年12月—2014年6月接受冠状动脉CTA(CCTA)检查,且结果显示为一支或多支冠状动脉中度病变(狭窄程度30%~70%)的76例患者的病史资料。应用人工智能CT-FFR软件对患者CCTA图像进行分析并计算。同时回顾性分析CCTA检查后5年内心血管终点事件的发生率,定义心血管终点事件为全因死亡、发生心肌梗死、接受紧急血运重建,以及随访CCTA检查提示冠状动脉病变明显进展者。将出现终点事件患者纳入试验组,未出现终点事件患者纳入对照组。采用多因素logistic回归分析变量CT-FFR值≤0.8、性别、年龄与患者心血管终点事件发生的相关性。结果多因素logistic回归分析结果显示,患者CT-FFR值≤0.8与心血管终点事件发生呈显著相关性(OR=3.903,P=0.021),为心血管终点事件发生的独立危险因素;年龄、性别与心血管终点事件发生无明显相关性。结论CT-FFR值≤0.8可能对稳定型冠状动脉性心脏病患者的心血管终点事件的预测起重要作用。
Objective To explore the relationship between computed tomography fractional flow reserve(CT-FFR)and cardiovascular disease prognosis by comparing the CT-FFR values in patients with and without cardiovascular end point events.Methods Seventy-six patients who underwent coronary CT angiography(CCTA)between December 2013 and June 2014 were enrolled in this study.CCTA showed one or more moderate coronary lesions(30%-70%stenosis)in each patient.These CCTA images were analyzed and calculated using artificial intelligence CT-FFR software.The incidence of cardiovascular endpoints was reviewed in five years.Cardiovascular endpoints were defined as all-cause death,myocardial infarction,emergency revascularization,and obvious progress of coronary lesions confirmed by CCTA during 5-year follow-up.The patients with end point events were assigned to experimental group,others as controls.Multivariate logistic regression analysis was used to analyze the correlation between CT-FFR≤0.8,gender,age,and cardiovascular end point events.Results CT-FFR≤0.8 was significantly correlated with cardiovascular end point events(OR=3.903,P=0.021)and was an independent risk factor for cardiovascular end point events.Age and gender were not correlated with cardiovascular end point events.Conclusion CT-FFR≤0.8 may play an important role in predicting cardiovascular endpoints in patients with stable coronary heart disease.
作者
翁婷雯
毛定飚
金倞
李铭
曲新凯
WENG Tingwen;MAO Dingbiao;JIN Liang;LI Ming;QU Xinkai(Department of Cardiology,Huadong Hospital,Fudan University,Shanghai 200040,China)
出处
《上海医学》
CAS
北大核心
2020年第1期14-17,共4页
Shanghai Medical Journal
基金
复旦大学附属华东医院院级课题(H1318).
关键词
CT血流储备分数
稳定型冠状动脉性心脏病
冠状动脉中度病变
预后分析
人工智能
深度学习
Fractional flow reserve from coronary CT angiography
Stable coronary heart disease
Moderate coronary stenosis
Prognostic analysis
Artificial intelligence
Deep learning approach
