A new risk stratification score for patients with suspected cardiac chest pain in emergency departments,based on machine learning

作     者：Hai-Feng Mao Xiao-Hui Chen Yun-Mei Li Si-Yuan Zhang Jun-Rong Mo Min Li Pei-Yi Lin Timothy HRainer Colin AGraham Hui-Lin Jiang Mao Hai-Feng;Chen Xiao-Hui;Li Yun-Mei;Zhang Si-Yuan;Mo Jun-Rong;Li Min;Lin Pei-Yi;Rainer Timothy H.;Graham Colin A.;Jiang Hui-Lin

作者机构：Department of EmergencyThe Second Affiliated Hospital of Guangzhou Medical UniversityGuangzhouGuangdong 510260China The Sixth Clinical Medical Institute of Guangzhou Medical UniversityGuangzhouGuangdong 511436China Accident and Emergency Medicine Academic UnitChinese University of Hong KongPrince of Wales HospitalHong Kong999077China 

出 版 物：《Chinese Medical Journal》 (中华医学杂志（英文版）)

年 卷 期：2020年第133卷第7期

页      面：879-880页

核心收录：

学科分类：100218[医学-急诊医学] 1002[医学-临床医学] 1010[医学-医学技术（可授医学、理学学位）] 10[医学] 

基　　金：supported by grants from the Scientific Research Project of the Guangzhou Education Bureau(No.1201610645) the Key Medical Disciplines and Specialties Program of Guangzhou 

主　　题：patients pain cardiac 

摘      要：To the Editor:Chest pain is one of the most common complaints for patients attending emergency departments(EDs)*** is important to accurately stratify risk of possible acute coronary syndrome(ACS)for these patients.[1]Several risk stratification scores such as thrombolysis in myocardial infarction(TIMI),global registry for acute coronary events(GRACE),Banach and HEART are helpful.[2]Previous research in our setting compared these four scores and found that the HEART score,with a C-statistic of 0.731,was the best for predicting 7-day major adverse cardiac events(MACE)The purpose of this study was to develop risk stratification prediction models for 7-day MACE in patients with chest pain,utilizing machine learning algorithms such as eXtreme Gradient Boosting(XGBoost),Support Vector Machine(SVM).