ArticleWorld journal of clinical cases2024
Application value of machine learning models in predicting intraoperative hypothermia in laparoscopic surgery for polytrauma patients.
Article in World journal of clinical cases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Risk prediction models for inadvertent intraoperative hypothermia in surgical patients: a systematic review and meta-analysis.International journal of nursing studies advances · 2026Review
- Explainable prediction of hypothermia risk in laparoscopic surgery: a retrospective cross-sectional study using machine learning.BMC surgery · 2025Article
- Risk factors and predictive modeling of intraoperative hypothermia in laparoscopic surgery patients.BMC surgery · 2025Article
- Development and validation of a machine learning-based prediction model for intraoperative hypothermia in Chinese patients undergoing gastrointestinal surgery.Perioperative medicine (London, England) · 2025Article
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3 authors.
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Abstract
backgroundHypothermia 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.
aimTo investigate the value of machine learning model to predict hypothermia during laparoscopic surgery in patients with multiple trauma.
methodsThis 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.
resultsComparison 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 (
conclusionMachine learning effectively predicted intraoperative hypothermia in polytrauma patients undergoing laparoscopic surgery, which improved surgical safety and patient recovery.
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