老年胃癌根治术后并发耐药菌感染的影响因素及耐药菌分布特点研究

Drug-resistant bacterial infection after radical resection of gastric cancer in elderly patients:Influencing factors and distribution characteristics of pathogens

背景老年患者身体机能、抵抗能力较差,术后极易发生感染,且随着药细菌不断增多,耐药菌感染发生率较高,经验性用药已不能适应耐药菌的变化.基于此,本研究全面系统分析老年胃癌根治术后并发耐药菌感染情况.目的分析老年胃癌根治术后并发耐药菌感染的影响因素及耐药菌分布特点,为胃癌根治术后有效防治感染提供参考依据.方法选取2019-04/2022-09我院收治的359例老年胃癌根治术患者,其中46例术后发生感染,包含耐药菌感染21例(耐药菌组)、非耐药菌感染25例(非耐药菌组)及未感染患者313例(对照组),对老年胃癌根治术后并发耐药菌感染进行单因素与多因素Logistic回归分析,采用R语言绘制预测并发耐药菌感染的列线图,采用受试者工作特征曲线(receiver operating characteristic,ROC)评价列线图模型的预测效能,采用决策分析曲线评价列线图的临床效用.结果感染部位以肺部(42.86%)为主,病原菌类型以革兰阴性菌为主,常见病原菌类型有鲍曼不动杆菌(28.57%)、铜绿假单胞菌(23.81%)等;耐药菌组年龄、糖尿病、术后机械通气、术后留置尿管>3 d、重症加强护理病房(intensive care unit,ICU)住院时间高于非耐药组、对照组,白蛋白、辅助性T淋巴细胞CD4^(+)/记忆性T细胞CD8^(+)低于非耐药组、对照组(P<0.05);Logistic回归分析显示,年龄、糖尿病、术后机械通气、术后留置尿管>3 d、ICU住院时间是发生耐药菌感染的相关独立危险因素,白蛋白、CD4^(+)/CD8^(+)是发生耐药菌感染的相关保护因素(P<0.05);ROC分析发现,所构建的列线图预测模型ROC曲线下面积为0.980(95%CI:0.857-0.994),校正曲线与理想曲线拟合良好.结论老年胃癌根治术后并发耐药菌感染部位以肺部为主,病原菌类型以革兰阴性菌为主,年龄、糖尿病、术后机械通气、术后留置尿管、白蛋白、ICU住院时间、CD4^(+)/CD8^(+)是耐药菌感染的影响因素,临床可据此制定针对性防护措施,以进一步改善预后.

BACKGROUND Elderly patients have poor physical function and resistance,and are prone to infection after surgery.With the increasing number of drug-resistant bacteria,the incidence of drug-resistant bacterial infection is high,and empirical medication cannot adapt to the changes in drug-resistant bacteria.This study comprehensively and systematically analyzed the incidence of drug-resistant bacterial infection after radical resection of gastric cancer in elderly patients.AIM To analyze the influencing factors of drug-resistant bacteria infections and distribution characteristics of pathogens in elderly people after radical gastric cancer surgery.METHODS A total of 359 elderly patients admitted to our hospital for radical gastric cancer surgery from April 2019 to September 2022 were selected,among which 46 had postoperative infections,including 21 cases of drug-resistant bacterial infections(drug-resistant bacterial group)and 25 cases of non-drug-resistant bacterial infections(non-drug-resistant bacterial group),and 313 had no infection(control group).Univariate and multivariate Logistic regression analyses were performed to identify the factors influencing drug-resistant bacterial infections,and based on the influencing factors identified,R was used to draw a nomogram for predicting concurrent drug-resistant bacterial infections.Receiver operating characteristic(ROC)curve analysis was performed to evaluate the predictive efficacy of the nomogram model,and decision curve analysis was used to evaluate the clinical utility of the nomogram.RESULTS Infection occurred mainly in the lung(42.86%),and pathogens were mainly Gram-negative bacteria,with Acinetobacter baumannii(28.57%)and Pseudomonas aeruginosa(23.81%)being the predominant bacteria.Age,diabetes,postoperative mechanical ventilation,postoperative indwelling catheter>3 d,and length of stay in intensive care unit(ICU)in the drug-resistant bacteria group were significantly higher than those of the non-drug-resistant group and the control group,while albumin and CD4^(+)/CD8^(+)ratio were significantly lower than those of the non-drug-resistant group and the control group(P<0.05).Logistic regression analysis showed that age,diabetes,postoperative mechanical ventilation,postoperative indwelling urinary catheter>3 d,and ICU length of stay were independent risk factors associated with the development of drug-resistant bacterial infections,and albumin and CD4^(+)/CD8^(+)ratio were protective factors(P<0.05).ROC curve analysis demonstrated that the area under the ROC curve of the nomogram model was 0.980(95%confidence interval:0.857-0.994),and the calibration curve fitted well with the ideal curve.CONCLUSION Drug-resistant bacterial infections occur mainly in the lung of elderly patients after radical surgery for gastric cancer,and pathogens are mainly Gram-negative bacteria.Age,diabetes,postoperative mechanical ventilation,postoperative indwelling urinary catheter,albumin,length of ICU stay,and CD4^(+)/CD8^(+)ratio are the influencing factors of drug-resistant bacterial infections,and clinicians should formulate targeted protective measures based on these factors to further improve the prognosis of such patients.