机器学习方法构建老年心房颤动合并冠心病患者远期死亡的预测模型研究  

Machine learning-based prediction of long-term mortality in patients with atrial fibrillation and coronary heart disease aged 60 years and over

作　　者：董敏[1] 邹彤[1] 彭炳锋 史继筠 徐蕾[3] 裴作为 曲祎玫 张美慧 汪芳[1] 杨杰孚[1] Dong Min;Zou Tong;Peng Bingfeng;Shi Jiyun;Xu Lei;Pei Zuowei;Qu Yimei;Zhang Meihui;Wang Fang;Yang Jiefu(Department of Cardiology,Beijing Hospital,National Center of Gerontology,Institute of Geriatric Medicine,Chinese Academy of Medical Sciences,Beijing 100730;School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081;Department of Neurology,Beijing Hospital,National Center of Gerontology,Institute of Geriatric Medicine,Chinese Academy of Medical Sciences,Beijing100730;Department of Cardiology,Central Hospital of Dalian University of Technology,Dalian 116089)

机构地区：[1]北京医院心内科,国家老年医学中心,中国医学科学院老年医学研究院,北京100730 [2]北京理工大学计算机学院,北京100081 [3]北京医院神经内科,国家老年医学中心,中国医学科学院老年医学研究院,北京100730 [4]大连理工大学附属中心医院心内科,大连116089

出　　处：《中华老年医学杂志》2022年第7期804-810,共7页Chinese Journal of Geriatrics

基　　金：国家重点研发计划(2020YFC2008106);北京医院院级课题(BJ-2021-188)。

摘　　要：目的利用机器学习方法建立老年心房颤动(房颤)合并冠心病患者的远期死亡预测模型,并确定相应的危险因素。方法回顾性队列研究,连续入组2013年1月至2015年3月北京医院收治的60岁及以上房颤合并冠心病患者329例,男性183例(55.6%)例,女性146例(44.4%),年龄(77.8±7.3)岁,80岁及以上142例(43.2%)。失访11例(3.3%),死亡151例(45.9%),最后纳入分析的患者共318例。根据患者生存结局,将318例患者分为死亡组(151例)和存活组(167例)。此外,另选取2015年4—7月入院的60岁及以上房颤合并冠心病患者60例为外部数据验证集。采集人口统计学参数、合并疾病、辅助检查和临床治疗情况。随访至少6年,记录包括死亡在内的主要不良心脑血管事件(MACCE)。最后将入组患者按9∶1的比例随机分为训练集和测试集,通过机器学习算法建立不同模型预测房颤合并冠心病患者远期死亡率,并通过外部数据(60例)验证比较确立最优模型,利用Shapley加法解释算法对变量的重要性进行排序,得出排名前20位的特征变量,以确定危险因素。结果329例患者中,总体中位随访时间77.0月(95%CI:54.0~84.0),失访11例(3.3%),死亡151例(45.9%)。通过分析得出支持向量机模型、k-近邻算法(KNN)模型、决策树模型、随机森林模型、ADABoost模型、XGBoost模型、Logistic回归模型预测远期死亡率的受试者工作特征曲线(ROC)下面积(AUC)分别为0.76、0.75、0.75、0.91、0.86、0.85和0.81。其中随机森林模型预测效能最高,其准确率达0.789,F1值高达0.806,且优于传统的Logistic回归模型(AUC:0.91比0.81,P<0.05)。D-二聚体、年龄、MACCE次数、左心室射血分数、人血白蛋白水平、贫血、纽约心脏病协会心功能分级、陈旧性心肌梗死病史、估测肾小球滤过率(eGFR)及静息心率是预测远期死亡率的重要危险因素。结论基于机器学习方法建立的随机森林模型可预测老年房颤合并�Objective To establish a long-term mortality rate prediction model for patients aged 60 years and over with atrial fibrillation and coronary heart disease using the machine learning method,and identify the corresponding risk factors of mortality.Methods In this retrospective cohort study,a total of 329(11 cases lost of follow-up)patients with 183 males(55.6%)and 146 females(44.4%),aged(77.8±7.3)years,and 142 patients aged 80 years or older(43.2%)were selected in our hospitals from January 2013 to March 2015.And their clinical data on atrial fibrillation and coronary heart disease were analyzed.They were divided into the death group(151 cases)and the survival group(167 cases)according to the survival outcome.In addition,60 patients aged 60 years and over admitted to our hospitals from April to July 2015 with atrial fibrillation and coronary heart disease were selected as external data validation set.The clinical data included age,gender,body mass index,diagnosis,co-morbidity,laboratory indicators,electrocardiogram,echocardiogram,treatment data.These patients were followed up for at least 6 years,and the main adverse cardiovascular and cerebrovascular events(MACCE),including death,were recorded.Finally,the data of the enrolled patients were randomly divided into the training set and the test set according to the ratio of 9∶1,Different models were established to predict the long-term mortality of patients with atrial fibrillation and coronary heart disease by machine learning algorithm.The optimal model was established by substituting external data(60 cases)into the model for verification and comparison.The top 20 risk factors for mortality were determined by Shapley additive explanation(SHAP)algorithm.Results A total of 329 hospitalized patients were included in this study,the overall median follow-up time was 77.0 months(95%CI:54.0~84.0),11 cases lost during follow-up(3.3%),and 151 cases died(45.9%).The analysis found that the areas under the ROC curve for a support vector machine(SVM)model,k-Nearest Neighbor(K

关 键 词：心房颤动 冠状动脉疾病 预测 

分 类 号：R541.75[医药卫生—心血管疾病] R541.4[医药卫生—内科学]