本研究旨在开发一种简单有效的急性冠状动脉综合征(ACS)筛查模型,以便对表现为动脉硬化的患者进行早期干预和重点预防。
病例对照研究.
该研究使用横断面调查收集了2243名完成匿名电子病历(EMR)数据的患者的数据,并在2013年12月至2016年4月期间在山东省的一家医院收集了冠状动脉造影。
根据医院EMR数据库中的记录,18岁及以上的成年人被诊断为ACS或非ACS,和完整的基本信息(年龄和性别)。
54个实验室生物标志物和人口统计学因素(年龄和性别)。
平衡后,将所有患者实验室指标和人口统计学因素的数据集分为训练集和验证集。训练集平衡后,随机森林曲线下面积(AUCRF)和最小绝对收缩和选择算子(LASSO)回归用于特征提取。然后利用不同的特征集建立了两个集合随机森林模型,并进行了比较和分析,以评估模型的最佳模型,包括灵敏度,准确度和AUC接收器工作特性曲线与内部验证集。
建立ACS筛选模型。
由LASSO选择的具有31个特征的RF模型,AUC为0.616(95%CI0.650至0.772),验证集中的灵敏度为0.832,准确度为0.714。AUCRF选择的另一个具有27个特征的RF模型的AUC为0.621(95%CI0.664至0.785),验证集中的灵敏度为0.849,准确度为0.728。
建立的具有27个临床特征的ACS筛选模型为预测ACS的实际解决方案提供了更好的性能。
This research aimed to develop a simple and effective acute coronary syndrome (ACS) screening model in order to intervene early and focus on prevention in patients presenting with
arteriosclerosis.
A
case-control study.
The study used a cross-sectional survey to collect data from 2243 patients who completed anonymous electronic medical record (EMR) data and coronary angiography was gathered at a hospital in Shandong Province between December 2013 and April 2016.
Adults 18 years old and above diagnosed as ACS or non-ACS according to the records in hospital EMR database, and with completed basic information (age and sex).
54 laboratory biomarkers and demographic factors (age and sex).
A dataset without missing data of all patients\' laboratory indicators and demographic factors was divided into training set and validation set after being balanced. After the training set balanced, area under the curve of random forest (AUCRF) and least absolute shrinkage and selection operator (LASSO) regression were used for feature extraction. Then two set random forest models were established with the different feature sets, and the process of comparison and analysis was made to evaluate models for the optimal model including sensitivity, accuracy and AUC receiver operating characteristic curves with the internal validation set.
To establish an ACS screening model.
An RF model with 31 features selected by LASSO with an AUC of 0.616 (95% CI 0.650 to 0.772), a sensitivity of 0.832 and an accuracy of 0.714 in the validation set. The other RF model with 27 features selected by AUCRF with an AUC of 0.621 (95% CI 0.664 to 0.785), a sensitivity of 0.849 and an accuracy of 0.728 in the validation set.
The established ACS screening model with 27 clinical features provides a better performance for practical solution in predicting ACS.