Application value of machine learning models in predicting intraoperative hypothermia in laparoscopic surgery for polytrauma patients  

作　　者：Kun Zhu Zi-Xuan Zhang Miao Zhang 

机构地区：[1]The Second Department of Anesthesia,Tianjin Hospital,Tianjin 300211,China [2]Department of War Rescue Training,Qingdao Special Servicemen Recuperation Center of PLA Navy,Qingdao 266001,Shandong Province,China [3]Department of Internal Medicine,Qingdao Fushan Elderly Apartments,Qingdao 266001,Shandong Province,China

出　　处：《World Journal of Clinical Cases》2024年第24期5513-5522,共10页世界临床病例杂志

摘　　要：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.

关 键 词：POLYTRAUMA Laparoscopic surgery HYPOTHERMIA Related factor Risk prediction 

分 类 号：R657.3[医药卫生—外科学] R692[医药卫生—临床医学]