Dataset Information

Cardiac arrest: An interdisciplinary scoping review of the literature from 2019.

ABSTRACT:

Objectives

The Interdisciplinary Cardiac Arrest Research Review (ICARE) group was formed in 2018 to conduct a systematic annual search of peer-reviewed literature relevant to cardiac arrest. Now in its second year, the goals of the review are to illustrate best practices in research and help reduce compartmentalization of knowledge by disseminating clinically relevant advances in the field of cardiac arrest across disciplines.

Methods

An electronic search of PubMed using keywords related to cardiac arrest was conducted. Title and abstracts retrieved by these searches were screened for relevance, classified by article type (original research or review), and sorted into 7 categories. Screened manuscripts underwent standardized scoring of overall methodological quality and impact on the categorized fields of study by reviewer teams lead by a subject-matter expert editor. Articles scoring higher than 99 percentiles by category-type were selected for full critique. Systematic differences between editors' and reviewers' scores were assessed using Wilcoxon signed-rank test.

Results

A total of 3348 articles were identified on initial search; of these, 1364 were scored after screening for relevance and deduplication, and forty-five underwent full critique. Epidemiology & Public Health represented 24% of fully reviewed articles with Prehospital Resuscitation, Technology & Care, and In-Hospital Resuscitation & Post-Arrest Care Categories both representing 20% of fully reviewed articles. There were no significant differences between editor and reviewer scoring.

Conclusions

The sheer number of articles screened is a testament to the need for an accessible source calling attention to high-quality and impactful research and serving as a high-yield reference for clinicians and scientists seeking to follow the ever-growing body of cardiac arrest-related literature. This will promote further development of the unique and interdisciplinary field of cardiac arrest medicine.

SUBMITTER: Murphy TW

PROVIDER: S-EPMC8244427 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCardiac arrest is a life-threatening cessation of activity in the heart. Early prediction of cardiac arrest is important, as it allows for the necessary measures to be taken to prevent or intervene during the onset. Artificial intelligence (AI) technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk.ObjectiveThis study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature.MethodsA scoping review was conducted in line with the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. Scopus, ScienceDirect, Embase, the Institute of Electrical and Electronics Engineers, and Google Scholar were searched to identify relevant studies. Backward reference list checks of the included studies were also conducted. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively.ResultsOut of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. Of the 47 studies, we were able to classify the approaches taken by the studies into 3 different categories: 26 (55%) studies predicted cardiac arrest by analyzing specific parameters or variables of the patients, whereas 16 (34%) studies developed an AI-based warning system. The remaining 11% (5/47) of studies focused on distinguishing patients at high risk of cardiac arrest from patients who were not at risk. Two studies focused on the pediatric population, and the rest focused on adults (45/47, 96%). Most of the studies used data sets with a size of <10,000 samples (32/47, 68%). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (38/47, 81%), and the most used algorithm was the neural network (23/47, 49%). K-fold cross-validation was the most used algorithm evaluation tool reported in the studies (24/47, 51%).ConclusionsAI is extensively used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in improving cardiac medicine. There is a need for more reviews to learn the obstacles to the implementation of AI technologies in clinical settings. Moreover, research focusing on how to best provide clinicians with support to understand, adapt, and implement this technology in their practice is also necessary.

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