ArticleBMC surgery2025
Explainable prediction of hypothermia risk in laparoscopic surgery: a retrospective cross-sectional study using machine learning.
Article in BMC surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 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
- Hypothermia and recovery outcomes before and after implementation of a multimodal perioperative temperature-management bundle in minimally invasive radical rectal cancer surgery.Frontiers in oncology · 2026Article
- Artificial intelligence and machine learning approaches for patient safety in complex surgery: a review.Patient safety in surgery · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectiveThis study aims to develop multiple machine learning models for predicting hypothermia risk in laparoscopic surgery and to perform interpretability analysis of the best-performing model. Our goal is to provide robust decision support for clinicians and ensure safe and effective patient care during surgical procedures.
methodsThis study included 1,030 patients who underwent laparoscopic surgery at Shandong Provincial Cancer Hospital, affiliated with Shandong First Medical University, between July 2023 and June 2024. We employed the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm for feature selection. We explored the performance of five machine learning algorithms-logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost)-to predict hypothermia risk during laparoscopic surgery. Finally, we conducted an interpretability analysis of the top-performing model using Shapley Additive Explanations (SHAP).
resultsLASSO identified nine risk factors: BMI, ASA classification, total volume of intravenous fluids, irrigating fluids during the operation, volume of CO
conclusionsThis study developed an efficient and interpretable predictive model for the risk of hypothermia in patients undergoing laparoscopic surgery. This model supports clinical decision-making and contributes to the overall goal of providing personalized care in the surgical environment.
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Registered trials
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