微创医学

目的探讨全身麻醉患者在麻醉复苏室（PACU）内发生低体温的影响因素，并构建列线图预测模型。方法回顾性分析261例全身麻醉患者的临床资料，按照约7∶3的比例随机分为训练集（186例）与验证集（75例）。根据患者在PACU内是否发生低体温将训练集分为低体温组（n=69）与非低体温组（n=117），采用多因素Logistic回归分析患者发生低体温的影响因素，并基于影响因素构建列线图预测模型，采用受试者操作特征（ROC）曲线、校准曲线分析，评价模型的区分度及校准度。结果 261例全身麻醉手术患者中118例在PACU内发生低体温，发生率为45.2%。训练集中低体温组与非低体温组患者的年龄、美国麻醉医师协会（ASA）分级、体重指数（BMI）、手术时长、术中出血量、术中输液量和术中输血、术中低体温、术中冲洗情况比较，差异具有统计学意义（P<0.05）。多因素Logistic回归分析显示，年龄、术中低体温、BMI是全身麻醉患者PACU内发生低体温的影响因素（P<0.05）。基于上述影响因素构建的列线图预测模型，训练集中ROC曲线下面积为 0.840（95%CI：0.780，0.900），灵敏度、特异度分别为 78.32%、80.34%；验证集中ROC曲线下面积为 0.727（95%CI：0.608，0.847），灵敏度、特异度分别为 72.14%、71.87%。校准曲线分析显示，训练集中Brier评分为0.153，验证集中Brier评分为0.201，Hosmer-Lemeshow检验显示模型预测发生概率与实际发生概率差异无统计学意义（P>0.05）。结论年龄、术中低体温、BMI是全身麻醉患者PACU内发生低体温的影响因素。基于上述影响因素构建的列线图预测模型具有一定的预测效能，可为临床早期识别高危患者、制订针对性体温保护策略提供参考。

Objective To investigate the influencing factors of hypothermia in patients under general anesthesia in the post-anesthesia care unit (PACU) and construct a nomogram prediction model. Methods A retrospective analysis was performed on the clinical data of 261 patients undergoing general anesthesia. The patients were randomly divided into a training set (n=186) and a validation set (n=75) according to the ratio of approximately 7∶3. According to the occurrence of hypothermia in the PACU, the training set was further categorized into a hypothermia group (n=69) and a non-hypothermia group (n=117). Multivariate Logistic regression analysis was used to identify the influencing factors for hypothermia, and a nomogram prediction model was constructed based on these factors. The discrimination and calibration of the model were evaluated using receiver operating characteristic (ROC) curves and calibration curves. Results Of 261 patients undergoing general anesthesia surgery, 118 developed hypothermia in the PACU, with an incidence rate of 45.2%. In the training set, statistically significant differences were observed between the hypothermia group and the non-hypothermia group in age, American Society of Anesthesiologists (ASA) classification, body mass index (BMI), operation duration, intraoperative blood loss, intraoperative fluid infusion volume, intraoperative blood transfusion, intraoperative hypothermia, and intraoperative irrigation (P<0.05). Multivariate Logistic regression analysis showed that age, intraoperative hypothermia, and BMI were influencing factors for hypothermia in the PACU among patients undergoing general anesthesia (P<0.05). The nomogram prediction model constructed based on the above influencing factors achieved an area under the ROC curve of 0.840 (95%CI: 0.780, 0.900) in the training set, with a sensitivity of 78.32% and a specificity of 80.34%, respectively. In the validation set, the area under the ROC curve was 0.727 (95%CI: 0.608, 0.847), with a sensitivity of 72.14% and a specificity of 71.87%, respectively. Calibration curve analysis showed a Brier score of 0.153 in the training set and 0.201 in the validation set. The Hosmer-Lemeshow test indicated no statistically significant difference between the predicted probability and the observed probability of hypothermia (P>0.05). Conclusion Age, intraoperative hypothermia, and BMI are influencing factors for hypothermia occurring in the PACU in patients undergoing general anesthesia. The nomogram prediction model constructed based on these factors shows favorable predictive performance, which can provide a reference for the early identification of high-risk patients and the formulation of targeted temperature protection strategies in clinical practice.