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
ARTICLE HIGHLIGHTS
Research background
Hypotension, which most often occurs on the induction of anesthesia, is known to lead to the development of adverse events and poor outcomes in patients following surgery. However, risk scores based on conventional logistic regression analysis have a low ability to discriminate characteristics that influence the development of post-induction hypotension (PIH) events.
Research motivation
Recently, a model based on machine learning techniques was reported to effectively predict or actively monitor events of interest by using variables in medical records datasets.
Research objectives
We attempted to construct a random forest (RF) model for the prediction of PIH events by using electronic information in a patient records dataset.
Research methods
Data were acquired from the electronic dataset of the Second Affiliated Hospital of Hainan Medical University. A RF model based on an up-to-date machine learning algorithm was used to predict post-anesthesia hypotension in patients during cardiac surgery.
Research results
Of the 3030 patients analyzed, 1578 (52.1%) experienced hypotensive events after anesthesia. The RF model had a high predictive performance, with an AUC of 0.843 (0.808-0.877). The most important variable attributing to the accuracy of hypotension prediction after anesthesia in the RF model was the mean blood pressure, followed by age and body mass index.
Research conclusions
RF technology can accurately predict PIH in patients following cardiac surgery.
Research perspectives
In the era of individualized medicine, precise machine learning modeling based on accessible patient information may offer anesthesiologists an opportunity for early intervention in PIH events.
