{"title":"Application value of machine learning models in predicting intraoperative hypothermia in laparoscopic surgery for polytrauma patients.","authors":"Kun Zhu, Zi-Xuan Zhang, Miao Zhang","doi":"10.12998/wjcc.v12.i24.5513","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Aim: </strong>To investigate the value of machine learning model to predict hypothermia during laparoscopic surgery in patients with multiple trauma.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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 (<i>P</i> < 0.05). Differences between other characteristics were not significant (<i>P</i> > 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 (<i>P</i> < 0.05). Calibration curve analysis showed good consistency between the predicted occurrence of intraoperative hypothermia and the actual occurrence (<i>P</i> > 0.05). The predictive model had AUCs of 0.850 and 0.829 for the training and validation sets, respectively.</p><p><strong>Conclusion: </strong>Machine learning effectively predicted intraoperative hypothermia in polytrauma patients undergoing laparoscopic surgery, which improved surgical safety and patient recovery.</p>","PeriodicalId":23912,"journal":{"name":"World Journal of Clinical Cases","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11269995/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Journal of Clinical Cases","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.12998/wjcc.v12.i24.5513","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
The World Journal of Clinical Cases (WJCC) is a high-quality, peer reviewed, open-access journal. The primary task of WJCC is to rapidly publish high-quality original articles, reviews, editorials, and case reports in the field of clinical cases. In order to promote productive academic communication, the peer review process for the WJCC is transparent; to this end, all published manuscripts are accompanied by the anonymized reviewers’ comments as well as the authors’ responses. The primary aims of the WJCC are to improve diagnostic, therapeutic and preventive modalities and the skills of clinicians and to guide clinical practice in clinical cases.