不同麻醉方式对老年患者术后早期认知功能障碍影响的评估

Evaluation of the Effects of Different Anesthesia Methods on Early Postoperative Cognitive Dysfunction in Elderly Patients

目的建立老年手术患者术后早期认知功能障碍(POCD)预测模型,并确定POCD低风险人群。方法选取2017年4月~2018年4月于遵化市人民医院接受下腹开腹手术老年患者406例,根据术后有无POCD将其分为POCD组20例,未发生POCD组386例。记录一般资料(年龄、BMI)、临床记录、术前辅助检测和麻醉方式等资料,建立POCD预测模型,通过ROC曲线分析计算ROC曲线下面积(AUC)反映POCD预测型区分度、灵敏度和特异度。结果两组患者在ASA分级、手术时间、术中出血、术中补液量、性别比和合并慢性疾病(高血压、糖尿病)上比较,差异均无统计学意义(P>0.05);年龄≥66岁、BMI≥25.0kg/m^2和全身麻醉为POCD发生独立危险因素,AUC=0.678,95%CI:0.721~0.798,灵敏度50.59%、特异度为78.01%;低危人群定义为年龄<66岁、BMI<25.0kg/m2且手术麻醉方式为腰麻联合硬膜外麻醉的下腹部手术老年患者。结论老年患者术后发生POCD来自于多个因素,与患者年龄、BMI、和麻醉方式相关,建立POCD的预测模型有助于确定低风险POCD患者,为减少临床POCD发生提供新思路。

Objective To establish a predictive model of early postoperative cognitive dysfunction(POCD)in elderly patients and to identify low-risk groups of POCD.Methods From April 2017 to April 2018,406 elderly patients underwent open abdominal surgery in Zunhua People's Hospital.According to the presence or absence of POCD,20 patients were divided into POCD group and 386 patients without POCD.Record general data(age,BMI),clinical records,preoperative assisted detection and anesthesia methods,establish a POCD prediction model,and calculate the area under the ROC curve(AUC)by ROC curve analysis to reflect the POCD prediction type discrimination,sensitivity and specificity.Results There were no significant differences in ASA classification,operative time,intraoperative bleeding,intraoperative fluid volume,sex ratio,and chronic disease(hypertension,diabetes)between the two groups(P>0.05);age≥66 years old,BMI≥25.0 kg/m2 and general anesthesia were independent risk factors for POCD,AUC=0.678,95%CI:0.721~0.798,sensitivity 50.59%,specificity 78.01%;low-risk group defined as age<66 years old,BMI<25.0 kg/m^2 and surgical anesthesia is an elderly patient with lower abdominal surgery for spinal anesthesia combined with epidural anesthesia.Conclusion Postoperative POCD in elderly patients comes from a number of factors,which are related to age,BMI,and anesthesia.Establishing a predictive model of POCD can help identify low-risk POCD patients and provide new ideas for reducing clinical POCD.