2014年9月,美国心脏协会（American Heart Association,AHA）/美国心脏病学会（American College of Cardiology,ACC）结合最新循证医学证据更新了非ST段抬高型急性冠脉综合征（non-ST-segment elevation of acute coronary syndrome,NS...2014年9月,美国心脏协会（American Heart Association,AHA）/美国心脏病学会（American College of Cardiology,ACC）结合最新循证医学证据更新了非ST段抬高型急性冠脉综合征（non-ST-segment elevation of acute coronary syndrome,NSTE-ACS）管理指南。指南指出，抗血小板治疗仍是NSTE—ACS药物治疗的基石，对确诊或疑似NSTE—ACS患者，无论采用早期侵入性策略还是缺血指导策略，早期启用抗血小板治疗均可有效减少血栓事件，改善预后。展开更多
Objective We aimed to assess the feasibility and superiority of machine learning(ML)methods to predict the risk of Major Adverse Cardiovascular Events(MACEs)in chest pain patients with NSTE-ACS.Methods Enrolled chest ...Objective We aimed to assess the feasibility and superiority of machine learning(ML)methods to predict the risk of Major Adverse Cardiovascular Events(MACEs)in chest pain patients with NSTE-ACS.Methods Enrolled chest pain patients were from two centers,Beijing Anzhen Emergency Chest Pain Center Beijing Bo’ai Hospital,China Rehabilitation Research Center.Five classifiers were used to develop ML models.Accuracy,Precision,Recall,F-Measure and AUC were used to assess the model performance and prediction effect compared with HEART risk scoring system.Ultimately,ML model constructed by Naïve Bayes was employed to predict the occurrence of MACEs.Results According to learning metrics,ML models constructed by different classifiers were superior over HEART(History,ECG,Age,Risk factors,&Troponin)scoring system when predicting acute myocardial infarction(AMI)and all-cause death.However,according to ROC curves and AUC,ML model constructed by different classifiers performed better than HEART scoring system only in prediction for AMI.Among the five ML algorithms,Linear support vector machine(SVC),Naïve Bayes and Logistic regression classifiers stood out with all Accuracy,Precision,Recall and F-Measure from 0.8 to 1.0 for predicting any event,AMI,revascularization and all-cause death(vs.HEART≤0.78),with AUC from 0.88 to 0.98 for predicting any event,AMI and revascularization(vs.HEART≤0.85).ML model developed by Naïve Bayes predicted that suspected acute coronary syndrome(ACS),abnormal electrocardiogram(ECG),elevated hs-cTn I,sex and smoking were risk factors of MACEs.Conclusion Compared with HEART risk scoring system,the superiority of ML method was demonstrated when employing Linear SVC classifier,Naïve Bayes and Logistic.ML method could be a promising method to predict MACEs in chest pain patients with NSTE-ACS.展开更多
Key points： PRATO-ACS randomized NSTE-ACS patients to rosuvastatin or control prior to PCI Statin resulted in significant decrease in nephropathy, better short-term outcomes Data needed to elucidate class effects of ...Key points： PRATO-ACS randomized NSTE-ACS patients to rosuvastatin or control prior to PCI Statin resulted in significant decrease in nephropathy, better short-term outcomes Data needed to elucidate class effects of statins, causality展开更多
CTGF (结缔组织生长因子)和TGF-β1 (转化生长因子-β1)是两种具有广泛生物学活性的细胞信号分子,参与细胞增殖、迁移、分化及细胞外基质的合成与沉积,尤其在纤维化过程中起重要作用。TGF-β1是CTGF表达的最强诱导剂之一,两者在多种病...CTGF (结缔组织生长因子)和TGF-β1 (转化生长因子-β1)是两种具有广泛生物学活性的细胞信号分子,参与细胞增殖、迁移、分化及细胞外基质的合成与沉积,尤其在纤维化过程中起重要作用。TGF-β1是CTGF表达的最强诱导剂之一,两者在多种病理过程中表现出协同作用。目前,已有大量研究表明两者在心肌纤维化中起到关键作用。同时多项研究表明,血清CTGF和TGF-β1水平与冠状动脉粥样硬化的发展进程存在密切关系,可为临床提供更敏感的疾病评估指标。其与冠状动脉病变复杂及严重程度的相关性,一直作为近年来学者的研究热点。非ST段抬高急性冠状动脉(冠脉)综合征(non-ST segment elevation acute coronary syndrome, NSTE-ACS)的核心病理机制为冠状动脉粥样病变基础上继发血栓形成和/或痉挛。临床上NSTE-ACS患者在冠状动脉狭窄基础上,往往同时伴有弥漫性多支血管病变。本文旨在分析NSTE-ACS患者中CTGF和TGF-β1水平与SYNTAX评分之间的相关性并进行比较。CTGF (connective tissue growth factor) and TGF-β1 (transforming growth factor-β1) are two cell signaling molecules with extensive biological activities, participating in cell proliferation, migration, differentiation, and the synthesis and deposition of extracellular matrix, especially playing a significant role in the process of fibrosis. TGF-β1 is one of the strongest inducers of CTGF expression, and the two exhibit a synergistic effect in various pathological processes. Currently, numerous studies have demonstrated that they play a crucial role in myocardial fibrosis. Meanwhile, multiple studies have shown that the levels of serum CTGF and TGF-β1 are closely related to the development process of coronary atherosclerosis, providing more sensitive disease assessment indicators for clinical practice. The correlation between them and the complexity and severity of coronary artery lesions has been a research hotspot in recent years. The core pathological mechanism of non-ST segment elevation acute coronary syndrome (NSTE-ACS) lies in the secondary thrombosis and/or spasm on the basis of coronary atherosclerotic lesions. Clinically, patients with NSTE-ACS often have diffuse multi-vessel lesions in addition to coronary artery stenosis. This article aims to analyze and compare the correlation between the levels of CTGF and TGF-β1 and the SYNTAX score in patients with NSTE-ACS.展开更多
文摘2014年9月,美国心脏协会（American Heart Association,AHA）/美国心脏病学会（American College of Cardiology,ACC）结合最新循证医学证据更新了非ST段抬高型急性冠脉综合征（non-ST-segment elevation of acute coronary syndrome,NSTE-ACS）管理指南。指南指出，抗血小板治疗仍是NSTE—ACS药物治疗的基石，对确诊或疑似NSTE—ACS患者，无论采用早期侵入性策略还是缺血指导策略，早期启用抗血小板治疗均可有效减少血栓事件，改善预后。
基金supported by Beijing Nova Program[Z201100006820087]National Key R&D Program of China[2020YFC2004800]+2 种基金National Natural Science Foundation of China[81870322]The Capital Health Research and Development of Special Fund[2018-1-2061]The Natural Science Foundation of Beijing,China[7191002].
文摘Objective We aimed to assess the feasibility and superiority of machine learning(ML)methods to predict the risk of Major Adverse Cardiovascular Events(MACEs)in chest pain patients with NSTE-ACS.Methods Enrolled chest pain patients were from two centers,Beijing Anzhen Emergency Chest Pain Center Beijing Bo’ai Hospital,China Rehabilitation Research Center.Five classifiers were used to develop ML models.Accuracy,Precision,Recall,F-Measure and AUC were used to assess the model performance and prediction effect compared with HEART risk scoring system.Ultimately,ML model constructed by Naïve Bayes was employed to predict the occurrence of MACEs.Results According to learning metrics,ML models constructed by different classifiers were superior over HEART(History,ECG,Age,Risk factors,&Troponin)scoring system when predicting acute myocardial infarction(AMI)and all-cause death.However,according to ROC curves and AUC,ML model constructed by different classifiers performed better than HEART scoring system only in prediction for AMI.Among the five ML algorithms,Linear support vector machine(SVC),Naïve Bayes and Logistic regression classifiers stood out with all Accuracy,Precision,Recall and F-Measure from 0.8 to 1.0 for predicting any event,AMI,revascularization and all-cause death(vs.HEART≤0.78),with AUC from 0.88 to 0.98 for predicting any event,AMI and revascularization(vs.HEART≤0.85).ML model developed by Naïve Bayes predicted that suspected acute coronary syndrome(ACS),abnormal electrocardiogram(ECG),elevated hs-cTn I,sex and smoking were risk factors of MACEs.Conclusion Compared with HEART risk scoring system,the superiority of ML method was demonstrated when employing Linear SVC classifier,Naïve Bayes and Logistic.ML method could be a promising method to predict MACEs in chest pain patients with NSTE-ACS.
文摘Key points： PRATO-ACS randomized NSTE-ACS patients to rosuvastatin or control prior to PCI Statin resulted in significant decrease in nephropathy, better short-term outcomes Data needed to elucidate class effects of statins, causality
文摘CTGF (结缔组织生长因子)和TGF-β1 (转化生长因子-β1)是两种具有广泛生物学活性的细胞信号分子,参与细胞增殖、迁移、分化及细胞外基质的合成与沉积,尤其在纤维化过程中起重要作用。TGF-β1是CTGF表达的最强诱导剂之一,两者在多种病理过程中表现出协同作用。目前,已有大量研究表明两者在心肌纤维化中起到关键作用。同时多项研究表明,血清CTGF和TGF-β1水平与冠状动脉粥样硬化的发展进程存在密切关系,可为临床提供更敏感的疾病评估指标。其与冠状动脉病变复杂及严重程度的相关性,一直作为近年来学者的研究热点。非ST段抬高急性冠状动脉(冠脉)综合征(non-ST segment elevation acute coronary syndrome, NSTE-ACS)的核心病理机制为冠状动脉粥样病变基础上继发血栓形成和/或痉挛。临床上NSTE-ACS患者在冠状动脉狭窄基础上,往往同时伴有弥漫性多支血管病变。本文旨在分析NSTE-ACS患者中CTGF和TGF-β1水平与SYNTAX评分之间的相关性并进行比较。CTGF (connective tissue growth factor) and TGF-β1 (transforming growth factor-β1) are two cell signaling molecules with extensive biological activities, participating in cell proliferation, migration, differentiation, and the synthesis and deposition of extracellular matrix, especially playing a significant role in the process of fibrosis. TGF-β1 is one of the strongest inducers of CTGF expression, and the two exhibit a synergistic effect in various pathological processes. Currently, numerous studies have demonstrated that they play a crucial role in myocardial fibrosis. Meanwhile, multiple studies have shown that the levels of serum CTGF and TGF-β1 are closely related to the development process of coronary atherosclerosis, providing more sensitive disease assessment indicators for clinical practice. The correlation between them and the complexity and severity of coronary artery lesions has been a research hotspot in recent years. The core pathological mechanism of non-ST segment elevation acute coronary syndrome (NSTE-ACS) lies in the secondary thrombosis and/or spasm on the basis of coronary atherosclerotic lesions. Clinically, patients with NSTE-ACS often have diffuse multi-vessel lesions in addition to coronary artery stenosis. This article aims to analyze and compare the correlation between the levels of CTGF and TGF-β1 and the SYNTAX score in patients with NSTE-ACS.
