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心电动力学图对急性冠脉综合征患者早期诊断的价值

Value of cardiodynamicsgram in early diagnosis of patients with acute coronary syndrome
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摘要 目的探讨由心电图(ECG)数据通过径向基函数(RBF)神经网络学习获得的心电动力学图(CDG)对急性冠脉综合征(ACS)患者早期诊断的价值。方法采用回顾性分析方法,选择2021年10月至2022年9月于深圳市宝安区人民医院急诊科就诊的以胸痛为主要首发症状的患者,根据出院诊断结果分为ACS组和非ACS组。收集患者的基线资料,包括性别、年龄、吸烟史、冠心病家族史及高血压、糖尿病、高脂血症、血管粥样硬化病史;记录患者入急诊科后首份12导联ECG,对其进行心电动力学分析生成CDG。绘制受试者工作特征曲线(ROC曲线),分析CDG及ECG对ACS和非ST段抬高急性冠脉综合征(NSTE-ACS)的早期诊断价值,计算敏感度、特异度、ROC曲线下面积(AUC)及其95%可信区间(95%CI)。观察并分析3例ECG正常的ACS患者的CDG和冠脉造影结果;对ECG正常但CDG阳性的非ACS患者进行30 d心血管不良事件随访。结果共纳入384例胸痛患者,其中ACS患者169例,非ACS患者215例。ACS组男性比例(87.0%比53.0%)、吸烟史比例(37.9%比12.1%)及高血压(46.2%比22.3%)、糖尿病(24.3%比7.9%)、高脂血症(55.0%比14.0%)、血管粥样硬化病史比例(22.5%比2.3%)均明显高于非ACS组(均P<0.05)。ROC曲线显示,CDG诊断ACS的AUC高于ECG〔AUC(95%CI):0.88(0.66~0.76)比0.71(0.84~0.92)〕,敏感度分别为92.8%、78.6%,特异度分别为83.3%、64.2%;CDG诊断NSTE-ACS的AUC高于ECG〔AUC(95%CI):0.85(0.80~0.90)比0.63(0.56~0.69)〕,敏感度分别为87.1%、61.3%、特异度分别为83.3%、64.2%。3例ECG正常的ACS患者的CDG均呈散乱无序状态,冠脉造影检查均提示冠脉主要分支存在≥70%的狭窄。215例非ACS患者中,有20例ECG正常但CDG表现为阳性的患者,随访结果显示,其中有3例在30 d内发生ST段抬高心肌梗死(STEMI),2例在30 d内发生不稳定型心绞痛(UA)。结论CDG在早期诊断ACS患者方面具有较高的价值,有望成为急诊科早期诊断ACS的重要手段。 Objective To explore the value of cardiodynamicsgram(CDG)obtained from electrocardiogram(ECG)data by radial basis functionradial basis function(RBF)neural network in early diagnosis of patients with acute coronary syndrome(ACS).Methods Retrospective analysis method was used.Patients with chest pain as the main initial symptom in the emergency department of Baoan District People's Hospital of Shenzhen from October 2021 to September 2022 were enrolled.Baseline data were collected,including gender,age,smoking history,family history of coronary heart disease and history of hypertension,diabetes,hyperlipidemia,and atherosclerosis.The first 12-lead ECG was recorded after admission to the emergency department,and electrocardiodynamics analysis was performed to generate CDG.Receiver operator characteristic curve(ROC curve)was plotted to analyze the value of CDG and ECG in the early diagnosis of ACS and non-ST segment elevation ACS(NSTE-ACS).Sensitivity,specificity,area under the ROC curve(AUC),and 95%confidence interval(95%CI)were calculated.CDG and coronary angiography results of 3 patients with ACS with normal ECG were observed and analyzed.Non-ACS patients with normal ECG but positive CDG were followed for 30 days for adverse cardiovascular events.Results A total of 384 patients with chest pain were included,including 169 patients with ACS and 215 patients without ACS.The proportion of male(87.0%vs.53.0%),smoking history(37.9%vs.12.1%),hypertension(46.2%vs.22.3%),diabetes(24.3%vs.7.9%),hyperlipidemia(55.0%vs.14.0%)and history of atherosclerosis(22.5%vs.2.3%)in ACS group were significantly higher than those in non-ACS group(all P<0.05).The ROC curve showed that the AUC of CDG diagnosis of ACS was higher than that of ECG[AUC(95%CI):0.88(0.66-0.76)vs.0.71(0.84-0.92)],the sensitivity was 92.8%,78.6%,and the specificity was 83.3%,64.2%,respectively.The AUC of CDG diagnosis of NSTE-ACS was higher than that of ECG[AUC(95%CI):0.85(0.80-0.90)vs.0.63(0.56-0.69)],the sensitivity was 87.1%,61.3%,and the specificity was 83.3%,64.2%,respectively.CDG of 3 patients with ACS with normal ECG showed disordered state,and coronary angiography showed≥70%stenosis of major coronary branches.Of 215 non-ACS patients,20 had a normal ECG but positive CDG,and 3 developed ST segment elevation myocardial infarction(STEMI)within 30 days,and 2 developed unstable angina(UA)within 30 days.Conclusion CDG has high value in early diagnosis of ACS patients and is expected to become an important means of early diagnosis of ACS in emergency.
作者 龚秀冰 顾亚楠 蒋湘粤 窦清理 Gong Xiubing;Gu Yanan;Jiang Xiangyue;Dou Qingli(Department of Emergency,Baoan District People's Hospital,Shenzhen 518000,Guangdong,China;Department of Critical Care Medicine,Qingyuan People's Hospital,Qingyuan 511500,Guangdong,China)
出处 《中华危重病急救医学》 CAS CSCD 北大核心 2024年第3期266-272,共7页 Chinese Critical Care Medicine
基金 深圳市医学重点专科项目(SZXK047)。
关键词 急性冠脉综合征 心电动力学图 神经网络学习 人工智能 心电图 Acute coronary syndrome Cardiodynamicsgram Neural network learning Artificial intelligence Electrocardiogram
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