Value of cardiodynamicsgram in early diagnosis of patients with acute coronary syndrome
10.3760/cma.j.cn121430-20231009-00851
- VernacularTitle:心电动力学图对急性冠脉综合征患者早期诊断的价值
- Author:
Xiubing GONG
1
;
Yanan GU
;
Xiangyue JIANG
;
Qingli DOU
Author Information
1. 深圳市宝安区人民医院急诊科，广东深圳 518000
- Keywords:
Acute coronary syndrome;
Cardiodynamicsgram;
Neural network learning;
Artificial intelligence;
Electrocardiogram
- From:
Chinese Critical Care Medicine
2024;36(3):266-272
- CountryChina
- Language:Chinese
-
Abstract:
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.
