Shi YC, Li J, Li SJ, Li ZP, Zhang HJ, Wu ZY, Wu ZY. Flap failure prediction in microvascular tissue reconstruction using machine learning algorithms. World J Clin Cases 2022; 10(12): 3729-3738 [PMID: 35647170 DOI: 10.12998/wjcc.v10.i12.3729]
Corresponding Author of This Article
Zhi-Yuan Wu, MD, PhD, Professor, Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, No. 57 South of Renmin Avenue, Zhanjiang 524001, Guangdong Province, China. 1608700812@qq.com
Research Domain of This Article
Surgery
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/
Yu-Cang Shi, Jie Li, Shao-Jie Li, Zhan-Peng Li, Hui-Jun Zhang, Ze-Yong Wu, Zhi-Yuan Wu, Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
Author contributions: Shi YC and Li J contributed equally to this work; Shi YC and Li J were responsible for conceptualization, data curation, and methodology and wrote the original draft; Li SJ, Li ZP and Zhang HJ analyzed the data and edited the manuscript; Wu ZY was responsible for validation and supervision and reviewed the manuscript; All authors approved the final submission.
Institutional review board statement: This study was approved by the Ethics Committee of the Affiliated Hospital of Guangdong Medical University.
Informed consent statement: The data used in this study were not involved in the patients’ privacy information, so the informed consent was waived by the Ethics Committee of the Affiliated Hospital of Guangdong Medical University.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Zhi-Yuan Wu, MD, PhD, Professor, Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, No. 57 South of Renmin Avenue, Zhanjiang 524001, Guangdong Province, China. 1608700812@qq.com
Received: December 14, 2021 Peer-review started: December 14, 2021 First decision: January 26, 2022 Revised: February 11, 2022 Accepted: March 6, 2022 Article in press: March 6, 2022 Published online: April 26, 2022 Processing time: 128 Days and 3.1 Hours
ARTICLE HIGHLIGHTS
Research background
Microvascular tissue reconstruction is a well-established technique used for the wide variety of tissue defects. However, still a risk of experiencing flap failure exist that eventually results in additional hospital stays, financial burden, and mental stress of the patients.
Research motivation
The application of the machine learning technique in flap failure events remains an underestimated area.
Research objectives
The objective of the current study was to develop machine learning-based predictive models for the flap failure to identify potential factors and screening the high-risk patients.
Research methods
To establish machine learning classifiers, we used a data set with 945 consecutive patients who underwent microvascular tissue reconstruction. Model performances were evaluated by the indicators including area under the receiver operating characteristic curve, accuracy, precision, recall, and F1 score. A multivariable regression analysis was also performed for the essential variables in the random forest model.
Research results
The flap failure event occurred in 152 patients (1.9%) after the operation. The random forest classifier based on various preoperative and intraoperative variables performed the best, with an area under the curve score of 0.770 in the test set. The top variables in the random forest were age, body mass index, ischemia time, smoking, diabetes, experience, prior chemotherapy, hypertension, insulin, and obesity.
Research conclusions
Machine learning models were successfully developed for identifying the potential factors and screening out the high-risk patients for the interesting outcome of flap failure.
Research perspectives
In our study, the machine learning technique correctly predicted flap failure in the patients who followed microvascular tissue reconstruction. Results from our research will help the clinician in decision-making by better understanding the underlying pathologic mechanisms of the disease and improving the long-term outcome of patients.
