MALDI-TOF质谱不同算法快速鉴定甲氧西林耐药金黄色葡萄球菌的可行性  

Feasibility study on rapid identification of methicillin-resistant Staphylococcus aureus by different algorithms of MALDI-TOF mass spectrometry

作　　者：马红英 李雪[2] 李亚楠 鲁辛辛[1] Ma Hongying;Li Xue;Li Yanan;Lu Xinxin(Department of Clinical Laboratory,Beijing Tongren Hospital,Capital Medical University,Beijing 100730;Department of Clinical Laboratory,Beijing Anzhen Hospital,Capital Medical University,Beijing 100029,China;Department of Clinical Laboratory,Bethune Hospital,Taiyuan 030032,China)

机构地区：[1]首都医科大学附属北京同仁医院检验科,北京100730 [2]首都医科大学附属北京安贞医院检验科,北京100029 [3]山西白求恩医院检验科,太原030032

出　　处：《中华检验医学杂志》2023年第2期176-182,共7页Chinese Journal of Laboratory Medicine

摘　　要：目的探索基质辅助激光解吸电离飞行时间(MALDI-TOF)质谱仪不同算法快速识别甲氧西林耐药金黄色葡萄球菌可行性。方法从北京同仁医院检验科2017年1月至2019年6月的细菌库中选取314株金黄色葡萄球菌临床分离株用MALDI-TOF MS鉴定,经头孢西丁纸片法(抑菌环直径≤21 mm)及聚合酶链反应(PCR)mecA基因初筛,将菌株分为甲氧西林耐药的金黄色葡萄球菌(MRSA)组(130株)和甲氧西林敏感的金黄色葡萄球菌(MSSA)组(184株);甲酸提取法采集谱图,将MRSA组和MSSA组再各分成3个亚组,即MRSA-1亚组(43株)、MRSA-2亚组(42株)及MRSA-3亚组(45株)和MSSA-1亚组(60株)、MSSA-2亚组(61株)及MSSA-3亚组(63株);使用Bruker MALDI-TOF质谱仪的ClinProTools软件中的遗传算法、快速分类算法、监督式神经网络算法以及中元汇吉质谱仪EX-Smartspec软件的卷积神经网络算法进行试验研究,重复3轮(第1轮MRSA-1和MRSA-2、MSSA-1和MSSA-2为建模组,MRSA-3和MSSA-3为验证组,依此类推进行3轮)。以4种算法的受试者工作特征(ROC)曲线下面积展开性能确认。从北京同仁医院检验科2019年7—12月的细菌库中选38株MRSA和40株MSSA临床株,使用甲酸提取法采集谱图,对卷积神经网络算法建立的模型进行独立测试。结果在3轮建模和验证后,3个子组Bruker ClinProTools遗传算法的ROC曲线下面积分别为0.89、0.74和0.64,快速分类算法的ROC曲线下面积分别为0.77、0.95和0.94,监督式神经网络算法的ROC曲线下面积分别为0.90、0.98和0.98,中元汇吉质谱仪EX-Smartspec软件的卷积神经网络算法的ROC曲线下面积分别为0.95、0.99和0.99。卷积神经网络算法的独立测试结果显示其敏感度88.82%(810/912)、特异度81.15%(779/960)、准确性84.88%(1589/1872)、ROC曲线下面积0.92。结论Bruker ClinProTools软件中的监督式神经网络算法与中元汇吉质谱仪EX-Smartspec软件所用的卷积神经网络算法在快速识别MRSA时的性能指标Objective To explore the feasibility of rapid identification of methicillin-resistant Staphylococcus aureus using different algorithms of the matrix-assisted laser desorption/ionization time of flight(MALDI-TOF)mass spectrometer.Methods Totally 314 clinical isolates of Staphylococcus aureus were selected from the bacterial bank at Beijing Tongren Hospital from January 2017 to June 2019.The samples were identified by MALDI-TOF MS,and screened by cefoxitin disk method(inhibition ring diameter£21 mm)and PCR mecA gene.The strains were divided into a methicillin-resistant Staphylococcus aureus(MRSA)group(130 strains)and a methicillin-susceptible Staphylococcus aureus(MSSA)group(184 strains).Then,after collecting the spectrograms of these samples using formic acid extraction,the MRSA group and MSSA group were divided into three subgroups each,namely MRSA-1(43 strains),MRSA-2(42 strains),MRSA-3(45 strains)and MSSA-1(60 strains),MSSA-2(61 strains)and MSSA-3(63 strains).The groups were studied using genetic algorithm(GA),fast classification algorithm(QC)and supervised neural network algorithm(SNN)in the ClinProTools software on the Bruker MALDI-TOF mass spectrometer,and the convolutional neural network algorithm(CNN)in the Ex-SmartSpec software on the Zhongyuan Hui-Ji mass spectrometer.These studies were repeated for 3 rounds.The first round with MRSA-1 and MRSA-2,MSSA-1 and MSSA-2 being model groups,MRSA-3 and MSSA-3 being validation groups.The validation groups were rotated for each round.The areas under the receiver operating characteristic(ROC)curve expansions of the four algorithms were used to confirm each program′s performance.Then,38 MRSA strains and 40 MSSA clinical strains were selected from the bacterial bank of the Laboratory of Beijing Tongren Hospital from July 2019 to December 2019,and were put through the formic acid extraction method to collect their spectra.These samples were tested independently with their convolutional neural network models.Results After three rounds of modeling and verification,the

关 键 词：质谱分析法 耐甲氧西林金黄色葡萄球菌 算法 

分 类 号：R446.5[医药卫生—诊断学]