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Xpert MTB/RIF在涂阳患者中对NTM肺病的诊断价值及NTM菌种分布特征分析

刘文彬 唐磊明 王敏 蔡成松 潘峰

刘文彬, 唐磊明, 王敏, 蔡成松, 潘峰. Xpert MTB/RIF在涂阳患者中对NTM肺病的诊断价值及NTM菌种分布特征分析[J]. 中华全科医学, 2026, 24(2): 275-279. doi: 10.16766/j.cnki.issn.1674-4152.004378
引用本文: 刘文彬, 唐磊明, 王敏, 蔡成松, 潘峰. Xpert MTB/RIF在涂阳患者中对NTM肺病的诊断价值及NTM菌种分布特征分析[J]. 中华全科医学, 2026, 24(2): 275-279. doi: 10.16766/j.cnki.issn.1674-4152.004378
LIU Wenbin, TANG Leiming, WANG Min, CAI Chengsong, PAN Feng. The diagnostic value of Xpert MTB/RIF for NTM pulmonary disease in smear-positive patients and analysis of the species distribution characteristics of NTM[J]. Chinese Journal of General Practice, 2026, 24(2): 275-279. doi: 10.16766/j.cnki.issn.1674-4152.004378
Citation: LIU Wenbin, TANG Leiming, WANG Min, CAI Chengsong, PAN Feng. The diagnostic value of Xpert MTB/RIF for NTM pulmonary disease in smear-positive patients and analysis of the species distribution characteristics of NTM[J]. Chinese Journal of General Practice, 2026, 24(2): 275-279. doi: 10.16766/j.cnki.issn.1674-4152.004378

Xpert MTB/RIF在涂阳患者中对NTM肺病的诊断价值及NTM菌种分布特征分析

doi: 10.16766/j.cnki.issn.1674-4152.004378
基金项目: 

浙江省医药卫生科技计划项目 2021KY888

详细信息
    通讯作者:

    潘峰,E-mail: 2359506302@qq.com

  • 中图分类号: R446.5 R378.91

The diagnostic value of Xpert MTB/RIF for NTM pulmonary disease in smear-positive patients and analysis of the species distribution characteristics of NTM

  • 摘要:   目的  评估Xpert MTB/RIF在涂阳患者中对非结核分枝杆菌(NTM)肺病的诊断价值以及分析NTM的菌种分布,以提高临床对涂阳NTM肺病患者的早期诊断能力。  方法  回顾性选取2020年4月—2024年9月杭州师范大学附属医院收治的122例住院患者(NTM肺病73例、肺结核49例)为研究对象,以涂阳患者为分析亚组,通过Kappa分析验证Xpert与基质辅助激光解析电离飞行时间质谱(MALDI-TOF MS)对NTM肺病和肺结核的鉴别诊断一致性,同时分析NTM的菌种分布特征。  结果  Xpert在涂阳NTM肺病、涂阴NTM肺病、涂阳肺结核和涂阴肺结核患者中阳性率分别为1.92%(1/52)、0%(0/21)、100.00%(38/38)、72.73%(8/11),1例脓肿分枝杆菌脓肿亚种被Xpert误判为结核分枝杆菌(MTB)。73例NTM肺病患者中单一菌株感染79.45%(58/73),2种及以上NTM菌株混合感染20.55%(15/73),检测出的89株NTM菌型中较常见的分别为胞内分枝杆菌62.92%(56/89)和脓肿分枝杆菌17.98%(16/89)。Xpert与MALDI-TOF MS检测在涂阳患者中对NTM肺病和肺结核鉴别诊断的一致性极好(Kappa=0.938,P < 0.001)。  结论  胞内分枝杆菌是NTM肺病患者中最常见的菌种类型,其次为脓肿分枝杆菌。Xpert在涂阳患者中能有效区分NTM肺病与肺结核,对涂阳NTM肺病的诊断具有显著的临床价值。

     

  • 表  1  MALDI-TOF MS对89株NTM菌株的菌种鉴定结果及检出率

    Table  1.   The species identification results and detection rate of 89 NTM bacterial strains by MALDI-TOF MS

    菌种类型 菌株数 检出率(%)
    M.intracellulare 56 62.92
    M.abscessus 16 17.98
      M.abscessus subsp.abscessus 12 13.48
      M.abscessus subsp.bolletii 2 2.25
      M.abscessus subsp.massiliense 2 2.25
    M.avium 4 4.49
    M.kansasii 4 4.49
      M.nonchromogenicum 2 2.25
      M.triviale 2 2.25
    M.xenopi 1 1.12
    M.chelonae 1 1.12
    M.simiae 1 1.12
    M.gordonae 1 1.12
    M.immunogenum 1 1.12
    注:M.abscessus包括M.abscessus subsp.abscessusM.abscessus subsp.bolletiiM.abscessus subsp.massiliense三种亚型。
    下载: 导出CSV

    表  2  MALDI-TOF MS对73例NTM肺病患者的菌种鉴定结果

    Table  2.   The bacterial species identification results of 73 patients with NTM lung disease by MALDI-TOF MS

    菌种类型 患者例数 检出率(%)
    单一菌株感染 58 79.45
      M.intracellulare 45 61.64
      M.abscessus subsp.abscessus 4 5.48
      M.kansasii 3 4.11
      M.abscessus subsp.massiliense 2 2.74
      M.triviale 2 2.74
      M.xenopi 1 1.37
      M.nonchromogenicum 1 1.37
    混合感染 15 20.55
      M.intracellulare+M.avium 3 4.11
      M.intracellulare+M.abscessus subsp.abscessus 4 5.48
      M.intracellulare+M.abscessus subsp.bolletii 1 1.37
      M.intracellulare+M.gordonae 1 1.37
      M.intracellulare+M.chelonae 1 1.37
      M.abscessus subsp.abscessus+M.abscessus subsp.bolletii 1 1.37
      M.abscessus subsp.abscessus+M.immunogenum 1 1.37
      M.abscessus subsp.abscessus+M.nonchromogenicum 1 1.37
      M.kansasii+M.simiae 1 1.37
      M.abscessus subsp.abscessus+M.intracellulare+M.avium 1 1.37
    下载: 导出CSV

    表  3  Xpert与MALDI-TOF MS对NTM肺病和肺结核诊断的一致性评价

    Table  3.   Evaluation of the consistency between Xpert and MALDI-TOF MS in the diagnosis of NTM pulmonary disease and pulmonary tuberculosis

    检测方法 例数(n=122) MALDI-TOF MS Kappa检验
    NTM肺病组 肺结核组 Kappa P
    AFB+Xpert- 51 51 0 0.938a < 0.001a
    AFB+Xpert+ 39 1 38
    AFB-Xpert- 24 21 3 0.568b < 0.001b
    AFB-Xpert+ 8 0 8
    注:AFB+为AFB涂片阳性;AFB-为AFB涂片阴性;Xpert+为Xpert阳性;Xpert-为Xpert阴性;a为AFB+Xpert-与AFB+Xpert+比较;b为AFB-Xpert-与AFB-Xpert+比较。
    下载: 导出CSV
  • [1] DONOHUE M J. Increasing nontuberculous mycobacteria reporting rates and species diversity identified in clinical laboratory reports[J]. BMC Infect Dis, 2018, 18(1): 163. DOI: 10.1186/s12879-018-3043-7.
    [2] FURUUCHI K, MORIMOTO K, YOSHIYAMA T, et al. Interrelational changes in the epidemiology and clinical features of nontuberculous mycobacterial pulmonary disease and tuberculosis in a referral hospital in Japan[J]. Respir Med, 2019, 152: 74-80. doi: 10.1016/j.rmed.2019.05.001
    [3] NGUYEN M H, HAAS M K, KASPERBAUER S H, et al. Nontuberculous mycobacterial pulmonary disease: patients, principles, and prospects[J]. Clin Infect Dis, 2024, 79(4): e27-e47. doi: 10.1093/cid/ciae421
    [4] TAN Y J, DENG Y F, YAN X F, et al. Nontuberculous mycobacterial pulmonary disease and associated risk factors in China: a prospective surveillance study[J]. J Infect, 2021, 83(1): 46-53. doi: 10.1016/j.jinf.2021.05.019
    [5] 马小军. 国内外非结核分枝杆菌诊治指南比较及临床适用性思考[J]. 中华内科杂志, 2016, 55(4): 264-266.

    MA X J. Comparison of domestic and international diagnostic and treatment guidelines for nontuberculous mycobacteria and considerations on clinical applicability[J]. Chin J Intern Med, 2016, 55(4): 264-266.
    [6] YU G C, ZHONG F M, SHEN Y Q, et al. Diagnostic accuracy of the Xpert MTB/RIF assay for tuberculous pericarditis: a systematic review and meta-analysis[J]. PLoS One, 2021, 16(9): e0257220. DOI: 10.1371/journal.pone.0257220.
    [7] BERHANU R H, DAVID A, DA SILVA P, et al. Performance of Xpert MTB/RIF, Xpert ultra, and Abbott RealTime MTB for diagnosis of pulmonary tuberculosis in a high-HIV-burden setting[J]. J Clin Microbiol, 2018, 56(12): e00560-e00518.
    [8] WANG J, ZHANG X X, HUO F M, et al. Analysis of Xpert MTB/RIF results in retested patients with very low initial bacterial loads: a retrospective study in China[J]. J Infect Public Health, 2023, 16(6): 911-916. doi: 10.1016/j.jiph.2023.04.004
    [9] SHEN Y Q, FANG L K, XU X D, et al. CapitalBio Mycobacterium real-time polymerase chain reaction detection test: rapid diagnosis of Mycobacterium tuberculosis and nontuberculous mycobacterial infection[J]. Int J Infect Dis, 2020, 98: 1-5.
    [10] CHU Y, WANG X M, DOU M, et al. Clinical characteristics, species distribution, and drug resistance of non-tuberculous mycobacteria lung disease in Qingdao, China[J]. Infect Drug Resist, 2024, 17: 4807-4814. doi: 10.2147/IDR.S475015
    [11] 无曹彬, 陈虹. 肺结核基层诊疗指南(2018年)[J]. 中华全科医师杂志, 2019, 18(8): 709-717.

    CAO B, CHEN H. Guideline for primary care of pulmonary tuberculosis (2018)[J]. Chin J Gen Pract, 2019, 18(8): 709-717.
    [12] 中华医学会结核病学分会. 非结核分枝杆菌病诊断与治疗指南(2020年版)[J]. 中华结核和呼吸杂志, 2020, 43(11): 918-946.

    Society of Tuberculosis, Chinese Medical Association. Guideline on diagnosis and treatment of non-tuberculous mycobacteria diseases (2020)[J]. Chin J Tuberc Respir Dis, 2020, 43(11): 918-946.
    [13] 王黎霞, 成诗明, 周林, 等. 肺结核诊断WS 288-2017[J]. 中国感染控制杂志, 2018, 17(7): 642-652.

    WANG L X, CHENG S M, ZHOU L, et al. Diagnosis of pulmonary tuberculosis WS 288-2017[J]. Chin J Infect Control, 2018, 17(7): 642-652.
    [14] CHIN K L, SARMIENTO M E, ALVAREZ-CABRERA N, et al. Pulmonary non-tuberculous mycobacterial infections: current state and future management[J]. Eur J Clin Microbiol Infect Dis, 2020, 39(5): 799-826. doi: 10.1007/s10096-019-03771-0
    [15] ZHANG Y Y, SUN R Y, YU C L, et al. Spatial heterogeneity of nontuberculous mycobacterial pulmonary disease in Shanghai: insights from a ten-year population-based study[J]. Int J Infect Dis, 2024, 143: 107001. DOI: 10.1016/j.ijid.2024.107001.
    [16] TAN Y J, DENG Y F, YAN X F, et al. Nontuberculous mycobacterial pulmonary disease and associated risk factors in China: a prospective surveillance study[J]. J Infect, 2021, 83(1): 46-53. doi: 10.1016/j.jinf.2021.05.019
    [17] WU M L, AZIZ D B, DARTOIS V, et al. NTM drug discovery: status, gaps and the way forward[J]. Drug Discov Today, 2018, 23(8): 1502-1519. doi: 10.1016/j.drudis.2018.04.001
    [18] SHEN Y Q, FANG L K, XU X D, et al. CapitalBio Mycobacterium real-time polymerase chain reaction detection test: rapid diagnosis of Mycobacterium tuberculosis and nontuberculous mycobacterial infection[J]. Int J Infect Dis, 2020, 98: 1-5.
    [19] SINSHAW W, KEBEDE A, BITEW A, et al. Effect of sputum quality and role of Xpert(®) MTB/RIF assay for detection of smear-negative pulmonary tuberculosis in same-day diagnosis strategy in Addis Ababa, Ethiopia[J]. Afr J Lab Med, 2022, 11(1): 1671. DOI: 10.4102/ajlm.v11i1.1671.
    [20] GONG X, HE Y R, ZHOU K Y, et al. Efficacy of Xpert in tuberculosis diagnosis based on various specimens: a systematic review and meta-analysis[J]. Front Cell Infect Microbiol, 2023, 13: 1149741. DOI: 10.3389/fcimb.2023.1149741.
    [21] 余旭良, 胡昌弟, 金菊仙, 等. Xpert MTB/RIF联合GenoType MTBDRplus在衢州地区耐多药结核病快速诊断中的应用研究[J]. 中华全科医学, 2019, 17(8): 1375-1378.

    YU X L, HU C D, JIN J X, et al. The application of Xpert MTB/RIF combined with GenoType MTBDRplus in the rapid diagnosis of MDR-TB in Quzhou[J]. Chinese Journal of General Practice, 2019, 17(8): 1375-1378.
    [22] WANG Z, ZOU Y W, WEI Z H, et al. Analytical and clinical validation of a novel MeltPlus TB-NTM/RIF platform for simultaneous detection of Mycobacterium tuberculosis complex, non-Tuberculous mycobacteria and rifampicin resistance[J]. Front Cell Infect Microbiol, 2025, 15: 1534268. DOI: 10.3389/fcimb.2025.1534268.
    [23] LI X M, SUN D Z, LIANG C S, et al. Characterization of non-tuberculous mycobacterial pulmonary disease and pulmonary tuberculosis in patients with AFB smear-positive sputum: a retrospective comparative study[J]. Heliyon, 2024, 10(17): e37434. DOI: 10.1016/j.heliyon.2024.e37434.
    [24] WANG J Y, CHEN Z L, XU Y N, et al. Screening and drug resistance analysis of non-tuberculous mycobacteria in patients with suspected pulmonary tuberculosis on the Hainan Island, China[J]. Infect Drug Resist, 2023, 16: 463-476. doi: 10.2147/IDR.S396050
    [25] SUN Q, YAN J, LIAO X L, et al. Trends and species diversity of non-tuberculous mycobacteria isolated from respiratory samples in northern China, 2014-2021[J]. Front Public Health, 2022, 10: 923968. DOI: 10.3389/fpubh.2022.923968.
    [26] LIU C F, SONG Y M, HE W C, et al. Nontuberculous mycobacteria in China: incidence and antimicrobial resistance spectrum from a nationwide survey[J]. Infect Dis Poverty, 2021, 10(1): 59. DOI: 10.1186/s40249-021-00844-1.
    [27] 周亚娣, 金法祥. 基质辅助激光解吸电离飞行时间质谱鉴定非结核分枝杆菌及药敏试验分析[J]. 中华全科医学, 2022, 20(9): 1548-1550.

    ZHOU Y D, JIN F X. Identification of nontuberculous mycobacteria by matrix-assisted laser desorption ionization time-of-flight mass spectrometry and analysis of drug-sensitivity test[J]. Chinese Journal of General Practice, 2022, 20(9): 1548-1550.
    [28] ZHANG H Z, LUO M, ZHANG K, et al. Species identification and antimicrobial susceptibility testing of non-tuberculous mycobacteria isolated in Chongqing, Southwest China[J]. Epidemiol Infect, 2020, 149: e7. DOI: 10.1017/S0950268820003088.
    [29] ZHANG Y Y, SUN R Y, YU C L, et al. Spatial heterogeneity of nontuberculous mycobacterial pulmonary disease in Shanghai: insights from a ten-year population-based study[J]. Int J Infect Dis, 2024, 143: 107001. DOI: 10.1016/j.ijid.2024.107001.
    [30] PANG Y, LU J, SU B Y, et al. Misdiagnosis of tuberculosis associated with some species of nontuberculous mycobacteria by GeneXpert MTB/RIF assay[J]. Infection, 2017, 45(5): 677-681. doi: 10.1007/s15010-017-1044-x
    [31] TANG Y Y, YU J, YANG G R, et al. Probe A shown in the GeneXpert MTB/RIF assay during the detection of Mycobacterium intracellular infections[J]. Diagn Microbiol Infect Dis, 2021, 99(2): 115243. DOI: 10.1016/j.diagmicrobio.2020.115243.
    [32] 李娜, 陈晓白, 彭琳. 痰涂片联合Xpert检测对基层结核病医院NTM肺病早期诊断的应用价值[J]. 中国医药指南, 2020, 18(15): 90-91.

    LI N, CHEN X B, PENG L. Sputum smear combined with Xpert detection for the application value in early diagnosis of nontuberculous mycobacterial pulmonary disease in primary tuberculosis hospitals[J]. Guide of China Medicine, 2020, 18(15): 90-91.
    [33] PENG J, SONG J, WANG F, et al. Harnessing big data to optimize an algorithm for rapid diagnosis of pulmonary tuberculosis in a real-world setting[J]. Front Cell Infect Microbiol, 2021, 11: 650163. DOI: 10.3389/fcimb.2021.650163.
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  • 收稿日期:  2025-05-24
  • 网络出版日期:  2026-04-11

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