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ORIGINAL RESEARCH article
Front. Cardiovasc. Med.
Sec. Intensive Care Cardiovascular Medicine
Volume 11 - 2024 |
doi: 10.3389/fcvm.2024.1469801
Development and Validation of Prediction Model for Death within Six Months after Cardiac Arrest
Provisionally accepted- Fujian Medical University Union Hospital, Fuzhou, China
Backgr ound: Even in patients with successful return of spontaneous circulation (ROSC), outcome after cardiac arrest (CA) remains poor and some even toward to death at last after several months of treatment. There is a need for an early assessment of outcome in patients with ROSC after CA. Therefore, we developed three models for prediction of death within six months after CA using early post-arrest factors, performed external validation and compared their efficiency. Methods: In this retrospective cohort study, 199 patients aged 18-80 years who experienced intra-hospital cardiac arrest (IHCA) patients or out-of-hospital cardiac nomogram was 0.991, 0.893 and 0.905 respectively. Conclusions: The prediction model established by early post-arrest factors performed well, which can provide suggestion for evaluating prognosis within six months after cardiac arrest. Predictive model constructed by Random Forest methods had better predictive efficacy.
Keywords: Cardiac arrest, prognosis, Prediction model, machine learning, Return of spontaneous circulation
Received: 08 Aug 2024; Accepted: 11 Nov 2024.
Copyright: © 2024 Lu, Zeng, Lin and Ye. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yuqi Zeng, Fujian Medical University Union Hospital, Fuzhou, China
Qinyong Ye, Fujian Medical University Union Hospital, Fuzhou, China
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