基于宏基因组学与呼气末二氧化碳监测的重症肺炎患儿抗菌耐药预测模型构建  

Development of a multidimensional predictive model for antimicrobial resistance in severe pneumonia in mechanically ventilated children based on metagenomics and end-tidal carbon dioxide monitoring

作　　者：凌萍[1] 周麟永 陈烨 王莎 陈建丽[1] LING Ping;ZHOU Linyong;CHEN Ye;WANG Sha;CHEN Jianli(PICU Department of Guiyang Maternal Meternal and Child Health Care Hospital,Guiyang 550004,China)

机构地区：[1]贵阳市妇幼保健院儿童重症医学科,贵州贵阳550004

出　　处：《中国病原生物学杂志》2025年第1期46-51,共6页Journal of Pathogen Biology

基　　金：2022年度贵州省卫生健康委科学技术基金项目(No.gzwkj2022-401)。

摘　　要：目的评估宏基因组学测序(metagenomic next-generation sequencing technology,mNGS)技术在预测机械通气重症肺炎患儿抗菌药物耐药性中的表现,结合呼吸末二氧化碳(ETCO_(2))监测数据,探讨其临床应用价值。方法纳入2022年7月至2024年4月在贵阳市儿童医院重症医学科的112名重症肺炎患儿,年龄在1个月至18岁之间,均需机械通气治疗。收集患儿的临床数据和支气管肺泡灌洗液(BALF)样本,使用mNGS技术进行病原微生物和耐药基因的检测。结合ETCO_(2)监测数据,采用决策树算法构建耐药性预测模型,并通过混淆矩阵和受试者工作特征(ROC)曲线评估模型性能。结果使用mNGS技术识别出的主要病原微生物包括肺炎链球菌、流感嗜血杆菌、金黄色葡萄球菌和呼吸道合胞病毒;主要耐药基因包括bla_TEM、mecA、ermB和vanA。初始ETCO_(2)和最高ETCO_(2)水平与多种耐药基因呈显著正相关(P<0.05)。决策树模型的准确率为0.859,AUC值为0.896。结论mNGS技术能够快速、准确地识别重症肺炎患儿的病原微生物及其耐药基因,结合ETCO_(2)监测数据显著提高了耐药性预测的准确性。基于此构建的决策树模型性能良好,为重症肺炎的个体化治疗提供了新的思路和方法。Objective To evaluate the performance of metagenomic sequencing(mNGS)combined with end-tidal carbon dioxide(ETCO_(2))monitoring in predicting antibiotic resistance in mechanically ventilated children with severe pneumonia.Methods A total of 112 children with severe pneumonia who were enrolled from July 2022 to April 2024 at Guiyang Children's Hospital.Clinical data and bronchoalveolar lavage fluid(BALF)samples were collected.Pathogens and resistance genes were detected using mNGS.A decision tree algorithm,combined with ETCO_(2)data,was used to construct a resistance prediction model.Results The main pathogens were Streptococcus pneumoniae,Haemophilus influenzae,Staphylococcus aureus,and respiratory syncytial virus.Key resistance genes included bla_TEM,mecA,ermB,and vanA.Initial and highest ETCO_(2)levels were significantly correlated with several resistance genes(P<0.05).The model accuracy was 0.859,with an AUC of 0.896.Conclusion mNGS combined with ETCO_(2)monitoring significantly improves the accuracy of resistance prediction,providing a novel approach for individualized treatment of severe pneumonia.

关 键 词：宏基因组学测序 呼吸末二氧化碳 重症肺炎 抗菌药物耐药性 机械通气 

分 类 号：R725.6[医药卫生—儿科]