Li XF, Huang YZ, Tang JY, Li RC, Wang XQ. Development of a random forest model for hypotension prediction after anesthesia induction for cardiac surgery. World J Clin Cases 2021; 9(29): 8729-8739 [PMID: 34734051 DOI: 10.12998/wjcc.v9.i29.8729]
Corresponding Author of This Article
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
Research Domain of This Article
Anesthesiology
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/
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
Abstract
BACKGROUND
Hypotension after the induction of anesthesia is known to be associated with various adverse events. The involvement of a series of factors makes the prediction of hypotension during anesthesia quite challenging.
AIM
To explore the ability and effectiveness of a random forest (RF) model in the prediction of post-induction hypotension (PIH) in patients undergoing cardiac surgery.
METHODS
Patient information was obtained from the electronic health records of the Second Affiliated Hospital of Hainan Medical University. The study included patients, ≥ 18 years of age, who underwent cardiac surgery from December 2007 to January 2018. An RF algorithm, which is a supervised machine learning technique, was employed to predict PIH. Model performance was assessed by the area under the curve (AUC) of the receiver operating characteristic. Mean decrease in the Gini index was used to rank various features based on their importance.
RESULTS
Of the 3030 patients included in the study, 1578 (52.1%) experienced hypotension after the induction of anesthesia. The RF model performed effectively, with an AUC of 0.843 (0.808-0.877) and identified mean blood pressure as the most important predictor of PIH after anesthesia. Age and body mass index also had a significant impact.
CONCLUSION
The generated RF model had high discrimination ability for the identification of individuals at high risk for a hypotensive event during cardiac surgery. The study results highlighted that machine learning tools confer unique advantages for the prediction of adverse post-anesthesia events.
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.
