Retrospective Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Cardiol. Oct 26, 2023; 15(10): 508-517
Published online Oct 26, 2023. doi: 10.4330/wjc.v15.i10.508
Establishment of a prediction model for prehospital return of spontaneous circulation in out-of-hospital patients with cardiac arrest
Jing-Jing Wang, Qiang Zhou, Zhen-Hua Huang, Yong Han, Chong-Zhen Qin, Zhong-Qing Chen, Xiao-Yong Xiao, Zhe Deng
Jing-Jing Wang, Qiang Zhou, Zhen-Hua Huang, Yong Han, Chong-Zhen Qin, Zhong-Qing Chen, Xiao-Yong Xiao, Zhe Deng, Department of Emergency Medicine, Shenzhen Second People’s Hospital/The First Affiliated Hospital of Shenzhen University Health Science Center , shenzhen 518035, Guangdong Province, China
Co-first authors: Jing-Jing Wang and Qiang Zhou.
Author contributions: Wang JJ, Zhou Q contributed equally to this work and share first authorship; Deng Z, Wang JJ, and Zhou Q designed the research study; Wang JJ and Zhou Q analyzed the data and wrote the manuscript; Deng Z were responsible for revising the manuscript for important intellectual content; Wang JJ, Zhou Q, Huang ZH, Han Y, Qin CZ, Qin CZ, and Xiao XY performed the primary literature and data extraction; All authors read and approved the final version.
Supported by Shenzhen Science and Technology Program, No. JCYJ20180228163014668; Shenzhen Second People’s Hospital Clinical Research Fund of Guangdong Province High-level Hospital Construction Project; No. 20223357005; and No. 2023xgyj3357002.
Institutional review board statement: The study was reviewed and approved by the Shenzhen Center for Prehospital Care Institutional Review Board [(No. 2023-071-02PJ)].
Informed consent statement: This study meets the conditions for applying for exemption from informed consent in China, and the exemption from informed consent has been approved.
Conflict-of-interest statement: All the authors declare no conflicts of interest for this article.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author at dengzhe202209@163.com.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Zhe Deng, Doctor, MD, PhD, Chief Doctor, Chief Physician, Doctor, Occupational Physician, Professor, Teacher, Department of Emergency Medicine, Shenzhen Second People’s Hospital/The First Affiliated Hospital of Shenzhen University Health Science Center, Sungang Road, Futian District, Shenzhen 518035, China. dengzhe202209@163.com
Received: July 23, 2023
Peer-review started: July 23, 2023
First decision: September 4, 2023
Revised: September 17, 2023
Accepted: September 22, 2023
Article in press: September 22, 2023
Published online: October 26, 2023
Processing time: 92 Days and 23.4 Hours
ARTICLE HIGHLIGHTS
Research background

Out-of-hospital cardiac arrest (OHCA) is a leading cause of death worldwide. In China, 550000 people develop OHCA annually with a survival rate of only 1.3% after discharge, making OHCA a major public health issue.

Research motivation

A large gap of prehospital return of spontaneous circulation (P-ROSC) rate remains between China and other countries and that the relative contributions of aid measures for each of these factors to P-ROSC vary across countries. There are still not such model, including pre-EMS intervention factors and Prehospital emergency measures, have currently been developed for P-ROSC in China.

Research objectives

To develop a nomogram prediction model which is interpretable, convenient to implement, easy to comprehend in busy prehospital processing, and comprehensive, including prehospital drug administration. Therefore, it could serve as a potentially assistive tool for clinical aid decision-making.

Research methods

Clinical data of patients with OHCA were retrospectively analyzed A nomogram prediction model for P-ROSC in patients with OHCA was developed and validate.

Research results

Among the included 2685 patients with OHCA, the P-ROSC incidence was 5.8%. LASSO and multivariate logistic regression analyses showed that age, bystander cardiopulmonary resuscitation (CPR), initial rhythm, CPR duration, ventilation mode, and pathogenesis were independent factors influencing P-ROSC in these patients. The area under the ROC was 0.963. The calibration plot demonstrated that the predicted P-ROSC model was concordant with the actual P-ROSC. The good clinical usability of the prediction model was confirmed using decision curve analysis.

Research conclusions

We developed a simple and accessible model to predict the probability of achieving P-ROSC in China. The P-ROSC, with just six factors, is interpretable, convenient to implement, and comprehensive in busy prehospital processing; thus, it could serve as a possible assistive tool for clinical-aid decision-making.

Research perspectives

If we go one step further, we start to conduct prospective studies to identify the specific causalities and to improve the accuracy of data collection.