Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Aug 26, 2024; 12(24): 5513-5522
Published online Aug 26, 2024. doi: 10.12998/wjcc.v12.i24.5513
Application value of machine learning models in predicting intraoperative hypothermia in laparoscopic surgery for polytrauma patients
Kun Zhu, Zi-Xuan Zhang, Miao Zhang
Kun Zhu, The Second Department of Anesthesia, Tianjin Hospital, Tianjin 300211, China
Zi-Xuan Zhang, Department of War Rescue Training, Qingdao Special Servicemen Recuperation Center of PLA Navy, Qingdao 266001, Shandong Province, China
Miao Zhang, Department of Internal Medicine, Qingdao Fushan Elderly Apartments, Qingdao 266001, Shandong Province, China
Co-first authors: Kun Zhu and Zi-Xuan Zhang.
Author contributions: Zhu K, Zhang ZX and Zhang M designed the experiments and conducted clinical data collection, performed postoperative follow-up and recorded the data, conducted the collation and statistical analysis, and wrote the original manuscript and revised the paper; All authors read and approved the final manuscript. Zhu K and Zhang ZX are co-first authors and contributed equally to this work, including design of the study, acquiring and analyzing data from experiments, and writing of the manuscript.
Institutional review board statement: This study was approved by the Ethics Committee of Tianjin Hospital.
Informed consent statement: The Ethics Committee agreed to waive informed consent.
Conflict-of-interest statement: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data sharing statement: All data generated or analyzed during this study are included in this published article.
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: Miao Zhang, BSc, Department of Internal Medicine, Qingdao Fushan Elderly Apartments, No. 66-68 Jinsong 1st Road, Shibei District, Qingdao 266001, Shandong Province, China. zhangmiaoamiao@163.com
Received: April 29, 2024
Revised: May 30, 2024
Accepted: June 20, 2024
Published online: August 26, 2024
Processing time: 72 Days and 18.3 Hours
Abstract
BACKGROUND

Hypothermia during laparoscopic surgery in patients with multiple trauma is a significant concern owing to its potential complications. Machine learning models offer a promising approach to predict the occurrence of intraoperative hypothermia.

AIM

To investigate the value of machine learning model to predict hypothermia during laparoscopic surgery in patients with multiple trauma.

METHODS

This retrospective study enrolled 220 patients who were admitted with multiple injuries between June 2018 and December 2023. Of these, 154 patients were allocated to a training set and the remaining 66 were allocated to a validation set in a 7:3 ratio. In the training set, 53 cases experienced intraoperative hypothermia and 101 did not. Logistic regression analysis was used to construct a predictive model of intraoperative hypothermia in patients with polytrauma undergoing laparoscopic surgery. The area under the curve (AUC), sensitivity, and specificity were calculated.

RESULTS

Comparison of the hypothermia and non-hypothermia groups found significant differences in sex, age, baseline temperature, intraoperative temperature, duration of anesthesia, duration of surgery, intraoperative fluid infusion, crystalloid infusion, colloid infusion, and pneumoperitoneum volume (P < 0.05). Differences between other characteristics were not significant (P > 0.05). The results of the logistic regression analysis showed that age, baseline temperature, intraoperative temperature, duration of anesthesia, and duration of surgery were independent influencing factors for intraoperative hypothermia during laparoscopic surgery (P < 0.05). Calibration curve analysis showed good consistency between the predicted occurrence of intraoperative hypothermia and the actual occurrence (P > 0.05). The predictive model had AUCs of 0.850 and 0.829 for the training and validation sets, respectively.

CONCLUSION

Machine learning effectively predicted intraoperative hypothermia in polytrauma patients undergoing laparoscopic surgery, which improved surgical safety and patient recovery.

Keywords: Polytrauma; Laparoscopic surgery; Hypothermia; Related factor; Risk prediction

Core Tip: Intraoperative hypothermia is a significant concern during laparoscopic surgery in patients with multiple trauma. This study investigated the value of a machine learning model in predicting hypothermia in this patient population. The results showed that machine learning effectively predicted intraoperative hypothermia, providing a valuable tool to improve surgical safety and patient recovery. Age, baseline temperature, intraoperative temperature, duration of anesthesia, and duration of surgery were identified as independent factors influencing hypothermia. The predictive model had good accuracy and consistency in both the training and validation sets.