Wang JJ, Zhou Q, Huang ZH, Han Y, Qin CZ, Chen ZQ, Xiao XY, Deng Z. Establishment of a prediction model for prehospital return of spontaneous circulation in out-of-hospital patients with cardiac arrest. World J Cardiol 2023; 15(10): 508-517 [PMID: 37900904 DOI: 10.4330/wjc.v15.i10.508]
Corresponding Author of This Article
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
Research Domain of This Article
Emergency Medicine
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Share the Article
Wang JJ, Zhou Q, Huang ZH, Han Y, Qin CZ, Chen ZQ, Xiao XY, Deng Z. Establishment of a prediction model for prehospital return of spontaneous circulation in out-of-hospital patients with cardiac arrest. World J Cardiol 2023; 15(10): 508-517 [PMID: 37900904 DOI: 10.4330/wjc.v15.i10.508]
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 byShenzhen 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.
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.
