微流控芯片技术在神经外科术后细菌感染快速诊断中的应用

Application of a microfluidic chip platform in rapid diagnosis of post-neurosurgical bacterial infection

目的建立并评估诊断神经外科术后细菌性感染的微流控芯片方法。方法回顾性分析首都医科大学附属北京天坛医院2007—2016年神经外科术后细菌性感染的病原菌与全国耐药监测网CHINET流行病学数据,结合文献报道的耐药菌分子流行病学信息选取靶病原体与耐药基因参数,共选取10项微生物鉴定参数(肺炎克雷伯菌,鲍曼不动杆菌,表皮葡萄球菌,阴沟肠杆菌,金黄色葡萄球菌,大肠埃希菌,屎肠球菌,粪肠球菌,嗜麦芽窄食单胞菌与铜绿假单胞菌)与15项耐药基因参数(mecA,vanA,vanB,aacC1,aadA1,bla_(CTX-M-1),bla_(CTX-M-9),bla_(GES-1),bla_(OXA-23),bla_(OXA-24),bla_(OXA-58),bla_(OXA-66),bla_(KPC-2),bla_(IMP-4),bla_(VIM-2))纳入微流控芯片设计。以MALDI-TOF MS鉴定、PCR检测耐药基因、微量肉汤稀释法检测耐药表型、ESBLs筛选试验为参比方法,用建立的微流控芯片技术检测13株已知菌种的菌液进行初步验证性能,检测108例患者的脑脊液细菌培养阳性标本进行临床应用价值评价。结果 13株已知菌株标本的鉴定率与耐药基因的一致率均为100%。108例患者的脑脊液细菌培养阳性标本检测中,微流控芯片的鉴定结果与MALDI-TOF MS分析的一致率高达94.44%;微流控芯片的耐药基因分析与微量肉汤稀释法/ESBLs筛选试验的一致性评价显示,对于碳青霉烯类抗菌药物、苯唑西林、万古霉素与ESBLs的表型与基因型的一致率均大于90%。结论微流控芯片快速准确,在微生物鉴定及耐药性预判方面具有应用价值,可以作为神经外科术后细菌感染诊断的重要补充方法。

Objective To establish and evaluate a microfluidic chip platform for the rapid diagnosis of post-neurosurgical bacterial infection. Methods The pathogens isolated from patients with post-neurosurgical bacterial infection in Beijing Tiantan Hospital Affiliated to Capital Medical University during 2007 and 2016 and the epidemiological data from China drug resistance monitoring network CHINET were analyzed retrospectively. Based on the retrospective data and the molecular epidemiological information of drug-resistant bacteria reported in the literature, target pathogens and drug resistance gene parameters were selected. The microbial identification parameters from 10 different bacteria, including Klebsiella pneumoniae, Acinetobacter baumannii, Staphylococcus epidermidis, Enterobacter cloacae, Staphylococcus aureus, Escherichia coli, Enterococcus faecium, Enterococcus faecalis, Stenotrophomonas maltophilia and Pseudomonas aeruginosa , and the parameters of 15 drug resistance genes, including mecA, vanA, vanB, aacC1, aadA1, bla CTX-M-1 , bla CTX-M-9 , bla GES-1 , bla OXA-23 , bla OXA-24 , bla OXA-58 , bla OXA-66 , bla KPC-2 , bla IMP-4 and bla VIM-2 , were selected for designing a microfluidic chip platform. Using MAIDI-TOF MS for bacterial identification, multiplex PCR for the detection of drug resistance genes, micro-broth dilution method for the detection of drug resistance phenotypes and ESBLs screening test as reference methods, 13 known bacteria were used to evaluate the preliminary performance of the established microfluidic chip platform, and 108 cerebrospinal fluid bacterial culture positive specimens were used to evaluate the clinical application value of the microfluidic chip platform. Results The identification rates of 13 known strains and the coincidence rate of drug resistance genes were 100%. The coincidence rate of identification results for 108 cerebrospinal fluid bacterial culture positive specimens between the microfluidic chip platform and the MALDI-TOF MS method was as high as 94.44%. The coincidence rates of drug resistance phenotype of carbapenems, oxacillin, vancomycin, ESBLs and genotype between the microfluidic chip platform and the micro-broth dilution method or ESBLs screening test were above 90%. Conclusion The established microfluidic chip platform is fast and accurate, and has application value in microbial identification and the prediction of drug resistance, which may be used as an important supplementary method in the diagnosis of post-neurosurgical bacterial infection.