Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Dec 28;19(12):970-980.
doi: 10.11909/j.issn.1671-5411.2022.12.002.

Electrocardiogram-based artificial intelligence for the diagnosis of heart failure: a systematic review and meta-analysis

Xin-Mu Li  1 , Xin-Yi Gao  1 , Gary Tse  1   2 , Shen-Da Hong  3   4 , Kang-Yin Chen  1 , Guang-Ping Li  1 , Tong Liu  1
Affiliations

Electrocardiogram-based artificial intelligence for the diagnosis of heart failure: a systematic review and meta-analysis

Xin-Mu Li et al. J Geriatr Cardiol. .

Abstract

Background: The electrocardiogram (ECG) is an inexpensive and easily accessible investigation for the diagnosis of cardiovascular diseases including heart failure (HF). The application of artificial intelligence (AI) has contributed to clinical practice in terms of aiding diagnosis, prognosis, risk stratification and guiding clinical management. The aim of this study is to systematically review and perform a meta-analysis of published studies on the application of AI for HF detection based on the ECG.

Methods: We searched Embase, PubMed and Web of Science databases to identify literature using AI for HF detection based on ECG data. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria. Random-effects models were used for calculating the effect estimates and hierarchical receiver operating characteristic curves were plotted. Subgroup analysis was performed. Heterogeneity and the risk of bias were also assessed.

Results: A total of 11 studies including 104,737 subjects were included. The area under the curve for HF diagnosis was 0.986, with a corresponding pooled sensitivity of 0.95 (95% CI: 0.86-0.98), specificity of 0.98 (95% CI: 0.95-0.99) and diagnostic odds ratio of 831.51 (95% CI: 127.85-5407.74). In the patient selection domain of QUADAS-2, eight studies were designated as high risk.

Conclusions: According to the available evidence, the incorporation of AI can aid the diagnosis of HF. However, there is heterogeneity among machine learning algorithms and improvements are required in terms of quality and study design.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Flow diagram of the study selection process.
Figure 2
Figure 2
Forest plot of sensitivity.
Figure 4
Figure 4
Forest plot of diagnostic odds ratio.
Figure 5
Figure 5
SROC curve of artificial intelligence based on electrocardiogram for heart failure diagnosis.
Figure 3
Figure 3
Forest plot of specificity.
Figure 6
Figure 6
Risk of bias according to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria.
See this image and copyright information in PMC

References

    1. Lakhani I, Leung KSK, Tse G, et al Novel mechanisms in heart failure with preserved, midrange, and reduced ejection fraction. Front Physiol. 2019;10:874. doi: 10.3389/fphys.2019.00874. - DOI - PMC - PubMed
    1. Murphy SP, Kakkar R, McCarthy CP, et al Inflammation in heart failure: JACC state-of-the-art review. J Am Coll Cardiol. 2020;75:1324–1340. doi: 10.1016/j.jacc.2020.01.014. - DOI - PubMed
    1. Groenewegen A, Rutten FH, Mosterd A, et al Epidemiology of heart failure. Eur J Heart Fail. 2020;22:1342–1356. doi: 10.1002/ejhf.1858. - DOI - PMC - PubMed
    1. Tse G, Gong M, Wong SH, et al Frailty and clinical outcomes in advanced heart failure patients undergoing left ventricular assist device implantation: a systematic review and meta-analysis. J Am Med Dir Assoc. 2018;19:255–261.e1. doi: 10.1016/j.jamda.2017.09.022. - DOI - PubMed
    1. Zhang Y, Yuan M, Gong M, et al Associations between prefrailty or frailty components and clinical outcomes in heart failure: a follow-up meta-analysis. J Am Med Dir Assoc. 2019;20:509–510. doi: 10.1016/j.jamda.2018.10.029. - DOI - PubMed

LinkOut - more resources