Angiography-Based Machine Learning for Predicting Fractional Flow Reserve in Intermediate Coronary Artery Lesions
Angiography-Based Machine Learning for Predicting Fractional Flow Reserve in Intermediate Coronary Artery Lesions
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DOI：
10.1161/jaha.118.011685
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发表时间：
2019-02-19
影响因子：
5.4
通讯作者：
Park, Seung-Jung
中科院分区：
文献类型：
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作者：
Cho, Hyungjoo;Lee, June-Goo;Park, Seung-Jung
背景-基于血管造影术的监督机器学习（ML）算法被开发用于将血流储备分数为0.80的病变分类。方法和结果-以4：1的比例，将1501例患者（1501个中间病变）随机分为训练集和测试集。在开口和靶病变远端10 mm之间，沿中心线绘制一系列血管造影管腔直径测量值沿着。基于直径图的24个计算的血管造影特征和4个临床特征（年龄、性别、体表面积和累及节段）用于XGBoost的ML。该模型通过2000次自助迭代进行独立训练和测试。对79名患者进行了外部验证。包括所有28个特征，在1204个训练样本中具有5倍交叉验证的ML模型预测血流储备分数
Background-An angiography-based supervised machine learning (ML) algorithm was developed to classify lesions as having fractional flow reserve 0.80.Methods and Results-With a 4:1 ratio, 1501 patients with 1501 intermediate lesions were randomized into training versus test sets. Between the ostium and 10mmdistal to the target lesion, a series of angiographic lumen diameter measurements along the centerline was plotted. The 24 computed angiographic features based on the diameter plot and 4 clinical features (age, sex, body surface area, and involve segment) were used for ML by XGBoost. The model was independently trained and tested by 2000 bootstrap iterations. External validation with 79 patients was conducted. Including all 28 features, the ML model with 5-fold cross-validation in the 1204 training samples predicted fractional flow reserve