背景:多发伤患者腹腔镜手术期间的低体温是一个重要问题,因为其潜在的并发症。机器学习模型提供了一种有前途的方法来预测术中低温的发生。
目的:探讨机器学习模型对多发伤患者腹腔镜手术中低体温的预测价值。
方法:这项回顾性研究纳入了2018年6月至2023年12月期间收治的220例多发伤患者。其中,154名患者被分配到一个训练组,其余66名以7:3的比例被分配到一个验证组。在训练集中,53例出现术中低体温,101例未出现。采用Logistic回归分析构建腹腔镜手术多发伤患者术中低体温的预测模型。曲线下面积(AUC),灵敏度,并计算特异性。
结果:低温组和非低温组的比较发现性别差异显著,年龄,基线温度,术中温度,麻醉持续时间,手术持续时间,术中输液,晶体输注,胶体输液,气腹容积(P<0.05)。其他特征间差异均不显著(P>0.05)。Logistic回归分析结果显示,年龄,基线温度,术中温度,麻醉持续时间,术中低体温的独立影响因素(P<0.05)。校准曲线分析显示术中低体温的预测发生与实际发生具有较好的一致性(P>0.05)。预测模型的训练集和验证集的AUC分别为0.850和0.829,分别。
结论:机器学习可有效预测腹腔镜手术多发伤患者术中低体温,提高了手术安全性和患者康复。
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.
OBJECTIVE: 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.
CONCLUSIONS: Machine learning effectively predicted intraoperative hypothermia in polytrauma patients undergoing laparoscopic surgery, which improved surgical safety and patient recovery.