基于XGBoost算法结合光电容积脉搏波与临床特征变量对冠心病患者出血风险的预测研究
Prediction study of bleeding risk in coronary artery disease patients based on XGBoost algorithm combined with photoplethysmography and clinical characteristic variables
张丽月 1董士勇 2石俊山 3米合拉衣·阿地勒 3王嵘2
作者信息
- 1. 100853 北京市,解放军医学院研究生院
- 2. 中国人民解放军总医院第一医学中心心脏大血管外科
- 3. 北京合众思壮时空物联科技有限公司
- 折叠
摘要
目的 基于机器学习(machine learning,ML)结合光电容积脉搏波(photoplethysmography,PPG)与临床特征变量构建冠状动脉粥样硬化性心脏病(coronary artery disease,CAD)患者抗栓治疗期间发生出血事件的预测模型.方法 选取冠心病患者抗栓治疗云端数据库中2018年1月至2019年10月在中国人民解放军总医院经冠状动脉造影诊断的冠心病并至少上报1例出血事件的冠心病患者,收集PPG及临床特征数据.将PPG-临床特征数据集按8∶2随机划分为训练集与验证集,训练集分别以随机森林、支持向量机及XGBoost算法构建CAD-出血事件预测模型;通过SHAP(SHapley Additive exPlanations)可解释模型对最佳ML预测模型纳入的临床变量进行筛选;最后用验证集数据从敏感性、特异性、受试者工作特征曲线的曲线下面积(AUC)三个方面对筛选后的预测模型进行评价.结果 共纳入155例CAD患者临床资料及PPG数据,XGBoost模型在训练集中表现出最佳预测性能(AUC=0.927).对临床特征变量筛选后发现,导致CAD患者抗栓治疗期间发生出血事件的预测因子有收缩压、糖尿病史、降糖药物使用等12个.最后使用验证集数据对纳入PPG-临床数据集特征与单用PPG特征变量构建CAD-出血事件预测模型进行比较,PPG-临床数据集构建的预测模型预测性能较好(AUC=0.892),敏感度及特异度均高于单用PPG特征的预测模型.结论 基于XGBoost算法结合PPG与临床特征变量的冠心病患者出血预测模型表现出较好的预测性能.在此基础上,应用便携式可穿戴PPG设备有望进一步实现对冠心病抗栓治疗患者出血风险的准确的居家动态监测,从而改善这些患者的长期临床结局.
Abstract
Objective To develop a predictive model for bleeding events during antithrombotic therapy in patients with coronary artery disease(CAD)based on machine learning combining photoplethysmography(PPG)and clinical characteristic variables.Methods PPG and clinical characteristic data from the online database of antithrombotic therapy for patients with coronary artery disease(CAD)at the General Hospital of the People's Liberation Army in China,diagnosed by coronary angiography from January 2018 to October 2019,with at least 1 reported bleeding event,were collected.The PPG-clinical characteristic dataset were randomly divide into the training and validation sets in an 80∶20 ratio.The training set was used to construct CAD-bleeding event prediction models by random forest,support vector machine and XGBoost algorithms.SHAP(SHapley Additive exPlanations)was utilized to explain the models and select the clinical variables included in the best machine learning predictive model.Finally,the selected predictive model on the validation set in terms of sensitivity,specificity and area under the receiver operating characteristic curve(AUC)was evaluated.Results A total of 155 CAD patients'clinical data and PPG data were included in the study.The XGBoost model demonstrated the best predictive performance in the training set(AUC=0.927).Following the screening of clinical feature variables,12 predictive factors for bleeding events during antiplatelet therapy in CAD patients were identified,including systolic blood pressure,history of diabetes and use of glucose-lowering medications.Subsequently,the predictive models constructed using the PPG-clinical data set features and PPG features alone were compared using validation set data.The model constructed with PPG-clinical data set features exhibited superior predictive performance(AUC= 0.892)compared to the model utilizing PPG features alone.Additionally,it demonstrated high sensitivity and specificity.Conclusion The XGBoost algorithm combined with PPG and clinical feature variables demonstrated superior predictive performance in forecasting bleeding events in patients with coronary artery disease(CAD).Building upon this,the application of portable wearable PPG devices holds promise for further enabling accurate and dynamic at-home monitoring of bleeding risk in CAD patients undergoing antiplatelet therapy,thereby improving their long-term clinical outcomes.
关键词
冠心病/抗栓治疗/出血/预测模型Key words
Coronary artery disease/Antithrombotic therapy/Hemorrhage/Prediction model引用本文复制引用
基金项目
国家重点研发计划(2016YFC1301400)
出版年
2024
NETL
NSTL
维普
万方数据