Zhu K, Zhang ZX, Zhang M. Application value of machine learning models in predicting intraoperative hypothermia in laparoscopic surgery for polytrauma patients.
World J Clin Cases 2024;
12:5513-5522. [PMID:
39188615 PMCID:
PMC11269995 DOI:
10.12998/wjcc.v12.i24.5513]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 05/30/2024] [Accepted: 06/20/2024] [Indexed: 07/11/2024] Open
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.
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