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
Review
. 2022 Apr;38(4):465-478.
doi: 10.1016/j.cjca.2022.01.007. Epub 2022 Jan 15.

Prediction of Sudden Cardiac Arrest in the General Population: Review of Traditional and Emerging Risk Factors

Andrew C T Ha  1 , Barbara S Doumouras  2 , Chang Nancy Wang  3 , Joan Tranmer  4 , Douglas S Lee  5
Affiliations
Free article
Review

Prediction of Sudden Cardiac Arrest in the General Population: Review of Traditional and Emerging Risk Factors

Andrew C T Ha et al. Can J Cardiol. 2022 Apr.
Free article

Abstract

Sudden cardiac death (SCD) is the most common and devastating outcome of sudden cardiac arrest (SCA), defined as an abrupt and unexpected cessation of cardiovascular function leading to circulatory collapse. The incidence of SCD is relatively infrequent for individuals in the general population, in the range of 0.03%-0.10% per year. Yet, the absolute number of cases around the world is high because of the sheer size of the population at risk, making SCA/SCD a major global health issue. On the basis of conservative estimates, there are at least 2 million cases of SCA occurring worldwide on a yearly basis. As such, identification of risk factors associated with SCA in the general population is an important objective from a clinical and public health standpoint. This review will provide an in-depth discussion of established and emerging factors predictive of SCA/SCD in the general population beyond coronary artery disease and impaired left ventricular ejection fraction. Contemporary studies on the association of age, sex, race, socioeconomic status, and the emerging contribution of diabetes and obesity to SCD risk beyond their role as atherosclerotic risk factors are reviewed. In addition, the role of biomarkers, particularly electrocardiographic ones, on SCA/SCD risk prediction in the general population are discussed. Finally, the use of machine learning as a tool to facilitate SCA/SCD risk prediction is examined.

PubMed Disclaimer

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

Publication types

Grants and funding

LinkOut - more resources