Retrospective Study
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Oct 16, 2021; 9(29): 8729-8739
Published online Oct 16, 2021. doi: 10.12998/wjcc.v9.i29.8729
Development of a random forest model for hypotension prediction after anesthesia induction for cardiac surgery
Xuan-Fa Li, Yong-Zhen Huang, Jing-Ying Tang, Rui-Chen Li, Xiao-Qi Wang
Xuan-Fa Li, Rui-Chen Li, Xiao-Qi Wang, Department of Anesthesiology, The Second Affiliated Hospital of Hainan Medical University, Haikou 570311, Hainan Province, China
Yong-Zhen Huang, Department of Anesthesiology, Hainan Hospital of Traditional Chinese Medicine, Haikou 570203, Hainan Province, China
Jing-Ying Tang, Department of Anesthesiology, Hainan Provincial People’s Hospital, Haikou 570000, Hainan Province, China
Author contributions: Li XF and Huang YZ contributed equally to this work; Li XF and Huang YZ were responsible for conceptualization, data curation, methodology, and wrote the original draft; Tang JY and Li RC were responsible for visualization and software; Wang XQ was responsible for validation, supervision, reviewed and edited the manuscript; All authors approved the final submission.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the Second Affiliated Hospital of Hainan Medical University.
Informed consent statement: This research did not involve any human or animal experiments, and the data used was downloaded from a public database. Therefore, the study did not require any informed consent.
Conflict-of-interest statement: The authors declare that they have no conflicting interests.
Data sharing statement: No additional data are available.
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: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Xiao-Qi Wang, MD, PhD, Associate Professor, Department of Anesthesiology, the Second Affiliated Hospital of Hainan Medical University, No. 368 Yehai Avenue, Longhua District, Haikou 570311, Hainan Province, China. wxq201904@163.com
Received: June 6, 2021
Peer-review started: June 6, 2021
First decision: June 25, 2021
Revised: July 7, 2021
Accepted: July 22, 2021
Article in press: July 22, 2021
Published online: October 16, 2021
Processing time: 130 Days and 21.9 Hours
Core Tip

Core Tip: This was a retrospective study intended to develop a prediction model for hypotensive events after anesthesia during cardiac surgery. A random forest machine learning technique was used to establish a predictive algorithm using preoperative data. “Features ranked by importance” were also identified in this study. This novel prediction model can be used to predict hypotension events and help to avoid the occurrence of any potential adverse events.