腹腔镜手术患者术中低体温风险预测模型的构建及验证

目的 构建腹腔镜手术患者术中低体温风险预测模型并验证模型的预测效果.方法 采用便利抽样法,选取2020年6月—10月在山东省某三级甲等医院接受腹腔镜手术并符合纳入标准的患者1043例,按照7:3的比例随机分配至建模组和验证组.将建模组发生术中低体温患者(407例)和未发生术中低体温患者(323例)的各项影响因素进行对比...

Establishment and validation of a risk prediction model for intraoperative hypothermia in patients undergoing laparoscopic surgery

Objective To construct a risk predictive model of intraoperative hypothermia for patients undergoing laparoscopic surgery and to verify the predictive effect of the model. Methods 1043 patients who underwent laparoscopic surgery and met the inclusion and exclusion criteria were selected in our hospital from June to October 2020,using the convenience sampling method. They were randomly assigned to a modeling group and a verification group at a ratio of 7 ∶ 3. The influencing factors of patients with intraoperative hypothermia（n=407） and patients without intraoperative hypothermia（n=323） in the modeling group were compared,which is conducive to the random forest algorithm to sort the influencing factors and build the prediction model. Results The incidence of intraoperative hypothermia was 55.75% in the modeling group and 54.95% in the validation group. In the importance score of random forest algorithm variables,basic body temperature,operating room temperature,BMI,operation time and other indicators have a high contribution to the model classification,with clinical significance. The area under the receiver operating characteristic curve of the predictive model is 0.797;the sensitivity is 78.74%;the specificity is 64.03%;the accuracy is 72.20%. Conclusion The prediction model based on random forest algorithm is effective,which is of great significance to identify the key factors of intraoperative hypothermia in patients undergoing laparoscopic surgery and intervene timely and effectively.