Use of an artificial intelligence-enabled electrocardiogram for screening people without coronary heart disease

doi:10.3877/cma.j.issn.2095-6568.2023.01.004

Abstract

Abstract:

Objective

To explore the application of artificial intelligence-assisted electrocardiograms (ECG) to identify patients without coronary heart disease (coronary artery stenosis < 50%).

Methods

Patients with suspected coronary heart disease and who underwent coronary angiography during hospitalization were enrolled. The ECG data set was established based on standard 12-lead ECG. The ECG was labeled as a group without coronary disease and a control group according to whether the main coronary artery or its main branches were narrowed by less than 50% in diameter. A deep neural network model was established by training ECG to identify patients without coronary heart disease.

Results

A total of 4 489 ECG medical records were included, of which 4 187 were used for model construction and 302 were used for external verification of the model. The area under curve (AUC) value, sensitivity, and specificity of the model were 0.70, 0.701, 0.630, and 0.469 respectively. The AUC value of external validation was 0.55, sensitivity 0.359, specificity 0.784, and F1 score 0.373.

Conclusion

The artificial intelligence model based on ECG can identify patients without coronary heart disease in patients with suspected coronary heart disease, which has certain clinical application value.

Key words: Artificial intelligence, Electrocardiogram, Without coronary heart disease, Coronary heart disease, Deep learning

Shaohua Guo, Shijia Geng, Shenda Hong, Guanyu Mu, Yizhi Zhang, Lei Yang, Tong Liu, Kangyin Chen. Use of an artificial intelligence-enabled electrocardiogram for screening people without coronary heart disease[J]. Chinese Journal of Heart and Heart Rhythm(Electronic Edition), 2023, 11(01): 18-23.

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URL: https://zhxzyxldzzz.cma-cmc.com.cn/EN/10.3877/cma.j.issn.2095-6568.2023.01.004

https://zhxzyxldzzz.cma-cmc.com.cn/EN/Y2023/V11/I01/18

Figures/Tables 5

组别	例数	无冠心病组	阳性对照组	男（%）	年龄（岁， $x ¯ ± s$ ）
训练集	3328	835	2493	49.3	64.4±10.4
验证集	416	109	307	45.6	63.8±10.3
测试集	443	97	346	45.8	64.0±10.1
外部验证	302	89	213	71.5	61.0±11.4

组别	例数	无冠心病组	阳性对照组	男（%）	年龄（岁， $x ¯ ± s$ ）
训练集	3328	835	2493	49.3	64.4±10.4
验证集	416	109	307	45.6	63.8±10.3
测试集	443	97	346	45.8	64.0±10.1
外部验证	302	89	213	71.5	61.0±11.4

指标	内部验证	外部验证
AUC	0.703	0.550
敏感性	0.701	0.359
特异性	0.630	0.784
F1评分	0.469	0.373

References 13

[1]	Zhou M, Wang H, Zhu J, et al. Cause-specific mortality for 240 causes in China during 1990-2013: a systematic subnational analysis for the Global Burden of Disease Study 2013[J]. Lancet, 2016, 387(10015):251-272.
[2]	West R, Ellis G, Brooks N. Complications of diagnostic cardiac catheterisation: results from a confidential inquiry into cardiac catheter complications[J]. Heart, 2006, 92(6):810-814.
[3]	Scanlon PJ, Faxon DP, Audet AM, et al. ACC/AHA guidelines for coronary angiography. A report of the American College of Cardiology/American Heart Association Task Force on practice guidelines (Committee on Coronary Angiography). Developed in collaboration with the Society for Cardiac Angiography and Interventions[J]. J Am Coll Cardiol, 1999,33(6):1756-1824.
[4]	Patel MR, Peterson ED, Dai D, et al. Low diagnostic yield of elective coronary angiography[J]. N Engl J Med, 2010, 362(10):886-895.
[5]	杨晓帆, 宫剑滨, 江时森, 等. 临床拟诊冠心病冠状动脉造影阴性180例分析 [J] . 中国综合临床,2005,21( 02 ): 103-104..
[6]	Attia ZI, Friedman PA, Noseworthy PA, et al. Age and sex estimation using artificial intelligence from standard 12-lead ECGs[J]. Circ Arrhythm Electrophysiol, 2019, 12(9):e007284.
[7]	Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction[J]. Lancet, 2019, 394(10201):861-867.
[8]	Attia ZI, Kapa S, Lopez-Jimenez F, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram[J]. Nat Med, 2019, 25(1):70-74.
[9]	McCarthy CP, Neumann JT, Michelhaugh SA, et al. Derivation and external validation of a high-sensitivity cardiac troponin-based proteomic model to predict the presence of obstructive coronary artery disease[J]. J Am Heart Assoc, 2020, 9(16):e017221.
[10]	Upton R, Mumith A, Beqiri A, et al. Automated echocardiographic detection of severe coronary artery disease using artificial intelligence[J]. JACC Cardiovasc Imaging, 2022, 15(5):715-727.
[11]	Leasure M, Jain U, Butchy A, et al. Deep learning algorithm Predicts angiographic coronary artery disease in stable patients using only a standard 12-lead electrocardiogram[J]. Can J Cardiol, 2021, 37(11):1715-1724.
[12]	Huang PS, Tseng YH, Tsai CF, et al. An artificial intelligence-enabled ECG algorithm for the prediction and localization of angiography-proven coronary artery disease[J]. Biomedicines, 2022, 10(2).
[13]	Lawton JS, Tamis-Holland JE, Bangalore S, et al. 2021 ACC/AHA/SCAI Guideline for coronary artery revascularization: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines[J]. J Am Coll Cardiol, 2022, 79(2):e21-e129.

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