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
Abstract
BACKGROUND

Out-of-hospital cardiac arrest (OHCA) is a leading cause of death worldwide.

AIM

To explore factors influencing prehospital return of spontaneous circulation (P-ROSC) in patients with OHCA and develop a nomogram prediction model.

METHODS

Clinical data of patients with OHCA in Shenzhen, China, from January 2012 to December 2019 were retrospectively analyzed. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were applied to select the optimal factors predicting P-ROSC in patients with OHCA. A nomogram prediction model was established based on these influencing factors. Discrimination and calibration were assessed using receiver operating characteristic (ROC) and calibration curves. Decision curve analysis (DCA) was used to evaluate the model’s clinical utility.

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 DCA.

CONCLUSION

The nomogram prediction model could effectively predict the probability of P-ROSC in patients with OHCA.

Keywords: Cardiac arrest; Cardiopulmonary resuscitation; Recovery spontaneous circulation; Logistic regression analysis; Predictive model

Core Tip: A large gap in the rate of prehospital return of spontaneous circulation remains between China and other countries and that the relative contributions of aid measures of the factors to prehospital return of spontaneous circulation vary across countries. There is still not such model, including pre-emergency medical service intervention factors and Prehospital emergency measures, developing for prehospital return of spontaneous circulation in China. Compared to similar models from other countries, the model proposed in the present study 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.