A retrospective study on Xpert MTB/RIF for detection of tuberculosis in a teaching hospital in China
Background The Xpert MTB/RIF assay is an automated molecular test that is designed to simultaneously detect Mycobacterium tuberculosis (MTB) complex and rifampin resistance. However, there are relatively few studies on this method in China. Xpert has been routinely used at Peking University People’s...
Ausführliche Beschreibung
Autor*in: |
Li, Shuguang [verfasserIn] Lin, Liyan [verfasserIn] Zhang, Feifei [verfasserIn] Zhao, Chunjiang [verfasserIn] Meng, Han [verfasserIn] Wang, Hui [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2020 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
Enthalten in: BMC infectious diseases - London : BioMed Central, 2001, 20(2020), 1 vom: 24. Mai |
---|---|
Übergeordnetes Werk: |
volume:20 ; year:2020 ; number:1 ; day:24 ; month:05 |
Links: |
---|
DOI / URN: |
10.1186/s12879-020-05004-8 |
---|
Katalog-ID: |
SPR039815331 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | SPR039815331 | ||
003 | DE-627 | ||
005 | 20230519161417.0 | ||
007 | cr uuu---uuuuu | ||
008 | 201007s2020 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1186/s12879-020-05004-8 |2 doi | |
035 | |a (DE-627)SPR039815331 | ||
035 | |a (SPR)s12879-020-05004-8-e | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 610 |q ASE |
084 | |a 44.00 |2 bkl | ||
100 | 1 | |a Li, Shuguang |e verfasserin |4 aut | |
245 | 1 | 2 | |a A retrospective study on Xpert MTB/RIF for detection of tuberculosis in a teaching hospital in China |
264 | 1 | |c 2020 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a Background The Xpert MTB/RIF assay is an automated molecular test that is designed to simultaneously detect Mycobacterium tuberculosis (MTB) complex and rifampin resistance. However, there are relatively few studies on this method in China. Xpert has been routinely used at Peking University People’s Hospital (PKUPH) since November 2016. Thus, the aim of this study was to evaluate the performance of Xpert, and provide a reference and guidance for the detection and diagnosis of TB in non-TB specialized hospitals. Methods The medical records of inpatients simultaneously tested with Xpert, acid-fast bacilli (AFB) smear microscopy, and interferon-gamma release assay (IGRA, by T-SPOT®.TB) at PKUPH from November 2016 to October 2018 were reviewed. Active TB cases were considered according to a composite reference standard (CRS). Then, the three methods were evaluated and compared. Results In total, 787 patients simultaneously tested with Xpert, AFB, and IGRA were enrolled; among them 11.3% (89/787) were diagnosed and confirmed active pulmonary TB (PTB, 52 cases), extrapulmonary TB (EPTB, 17 cases), and tuberculous pleurisy (TP, 20 cases). The sensitivity of Xpert in detecting PTB, EPTB, and TP was 88.5, 76.5, and 15.0%, respectively, which was slightly lower than IGRA (96.2, 82.4, and 95.0%, respectively), but higher than AFB (36.5, 11.8, and 0%, respectively); IGRA showed the highest sensitivity, but its specificity (55.9, 67.1, and 45.2%, respectively) was significantly lower than Xpert (99.6, 99.4, and 100%, respectively) and AFB (99.0, 99.4, and 100%, respectively) (P < 0.001). The sensitivity of Xpert in detecting lung tissue, cerebrospinal fluid, lymph nodes, and joint fluid was 100%, followed by sputum (88.5%), alveolar lavage (85.7%), and bronchoscopy secretion (81.2%); the pleural fluid sensitivity was the lowest, only 15.0%. For AFB negative patients, the sensitivity of Xpert in detecting PTB, EPTB, and TP was 84.9, 73.3, and 15.0%, respectively. Conclusions Xpert showed both high sensitivity and high specificity, and suggested its high value in TB diagnosis; however, the application of pleural fluid is still limited, and should be improved. Owing to the high sensitivity of IGRA, it is recommended for use as a supplementary test, especially for assisting in the diagnosis of TP and EPTB. | ||
650 | 4 | |a Xpert MTB/RIF |7 (dpeaa)DE-He213 | |
650 | 4 | |a Tuberculosis |7 (dpeaa)DE-He213 | |
650 | 4 | |a Pulmonary tuberculosis |7 (dpeaa)DE-He213 | |
650 | 4 | |a Extra-pulmonary tuberculosis |7 (dpeaa)DE-He213 | |
650 | 4 | |a Tuberculous pleurisy |7 (dpeaa)DE-He213 | |
700 | 1 | |a Lin, Liyan |e verfasserin |4 aut | |
700 | 1 | |a Zhang, Feifei |e verfasserin |4 aut | |
700 | 1 | |a Zhao, Chunjiang |e verfasserin |4 aut | |
700 | 1 | |a Meng, Han |e verfasserin |4 aut | |
700 | 1 | |a Wang, Hui |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t BMC infectious diseases |d London : BioMed Central, 2001 |g 20(2020), 1 vom: 24. Mai |w (DE-627)326645381 |w (DE-600)2041550-3 |x 1471-2334 |7 nnns |
773 | 1 | 8 | |g volume:20 |g year:2020 |g number:1 |g day:24 |g month:05 |
856 | 4 | 0 | |u https://dx.doi.org/10.1186/s12879-020-05004-8 |z kostenfrei |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_SPRINGER | ||
912 | |a SSG-OLC-PHA | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_74 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_206 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_702 | ||
912 | |a GBV_ILN_2001 | ||
912 | |a GBV_ILN_2003 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2006 | ||
912 | |a GBV_ILN_2008 | ||
912 | |a GBV_ILN_2009 | ||
912 | |a GBV_ILN_2010 | ||
912 | |a GBV_ILN_2011 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2015 | ||
912 | |a GBV_ILN_2020 | ||
912 | |a GBV_ILN_2021 | ||
912 | |a GBV_ILN_2025 | ||
912 | |a GBV_ILN_2031 | ||
912 | |a GBV_ILN_2038 | ||
912 | |a GBV_ILN_2044 | ||
912 | |a GBV_ILN_2048 | ||
912 | |a GBV_ILN_2050 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_2056 | ||
912 | |a GBV_ILN_2057 | ||
912 | |a GBV_ILN_2061 | ||
912 | |a GBV_ILN_2111 | ||
912 | |a GBV_ILN_2113 | ||
912 | |a GBV_ILN_2190 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
936 | b | k | |a 44.00 |q ASE |
951 | |a AR | ||
952 | |d 20 |j 2020 |e 1 |b 24 |c 05 |
author_variant |
s l sl l l ll f z fz c z cz h m hm h w hw |
---|---|
matchkey_str |
article:14712334:2020----::rtopcietdoxettrfodtcinfueclssnt |
hierarchy_sort_str |
2020 |
bklnumber |
44.00 |
publishDate |
2020 |
allfields |
10.1186/s12879-020-05004-8 doi (DE-627)SPR039815331 (SPR)s12879-020-05004-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Li, Shuguang verfasserin aut A retrospective study on Xpert MTB/RIF for detection of tuberculosis in a teaching hospital in China 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background The Xpert MTB/RIF assay is an automated molecular test that is designed to simultaneously detect Mycobacterium tuberculosis (MTB) complex and rifampin resistance. However, there are relatively few studies on this method in China. Xpert has been routinely used at Peking University People’s Hospital (PKUPH) since November 2016. Thus, the aim of this study was to evaluate the performance of Xpert, and provide a reference and guidance for the detection and diagnosis of TB in non-TB specialized hospitals. Methods The medical records of inpatients simultaneously tested with Xpert, acid-fast bacilli (AFB) smear microscopy, and interferon-gamma release assay (IGRA, by T-SPOT®.TB) at PKUPH from November 2016 to October 2018 were reviewed. Active TB cases were considered according to a composite reference standard (CRS). Then, the three methods were evaluated and compared. Results In total, 787 patients simultaneously tested with Xpert, AFB, and IGRA were enrolled; among them 11.3% (89/787) were diagnosed and confirmed active pulmonary TB (PTB, 52 cases), extrapulmonary TB (EPTB, 17 cases), and tuberculous pleurisy (TP, 20 cases). The sensitivity of Xpert in detecting PTB, EPTB, and TP was 88.5, 76.5, and 15.0%, respectively, which was slightly lower than IGRA (96.2, 82.4, and 95.0%, respectively), but higher than AFB (36.5, 11.8, and 0%, respectively); IGRA showed the highest sensitivity, but its specificity (55.9, 67.1, and 45.2%, respectively) was significantly lower than Xpert (99.6, 99.4, and 100%, respectively) and AFB (99.0, 99.4, and 100%, respectively) (P < 0.001). The sensitivity of Xpert in detecting lung tissue, cerebrospinal fluid, lymph nodes, and joint fluid was 100%, followed by sputum (88.5%), alveolar lavage (85.7%), and bronchoscopy secretion (81.2%); the pleural fluid sensitivity was the lowest, only 15.0%. For AFB negative patients, the sensitivity of Xpert in detecting PTB, EPTB, and TP was 84.9, 73.3, and 15.0%, respectively. Conclusions Xpert showed both high sensitivity and high specificity, and suggested its high value in TB diagnosis; however, the application of pleural fluid is still limited, and should be improved. Owing to the high sensitivity of IGRA, it is recommended for use as a supplementary test, especially for assisting in the diagnosis of TP and EPTB. Xpert MTB/RIF (dpeaa)DE-He213 Tuberculosis (dpeaa)DE-He213 Pulmonary tuberculosis (dpeaa)DE-He213 Extra-pulmonary tuberculosis (dpeaa)DE-He213 Tuberculous pleurisy (dpeaa)DE-He213 Lin, Liyan verfasserin aut Zhang, Feifei verfasserin aut Zhao, Chunjiang verfasserin aut Meng, Han verfasserin aut Wang, Hui verfasserin aut Enthalten in BMC infectious diseases London : BioMed Central, 2001 20(2020), 1 vom: 24. Mai (DE-627)326645381 (DE-600)2041550-3 1471-2334 nnns volume:20 year:2020 number:1 day:24 month:05 https://dx.doi.org/10.1186/s12879-020-05004-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 20 2020 1 24 05 |
spelling |
10.1186/s12879-020-05004-8 doi (DE-627)SPR039815331 (SPR)s12879-020-05004-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Li, Shuguang verfasserin aut A retrospective study on Xpert MTB/RIF for detection of tuberculosis in a teaching hospital in China 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background The Xpert MTB/RIF assay is an automated molecular test that is designed to simultaneously detect Mycobacterium tuberculosis (MTB) complex and rifampin resistance. However, there are relatively few studies on this method in China. Xpert has been routinely used at Peking University People’s Hospital (PKUPH) since November 2016. Thus, the aim of this study was to evaluate the performance of Xpert, and provide a reference and guidance for the detection and diagnosis of TB in non-TB specialized hospitals. Methods The medical records of inpatients simultaneously tested with Xpert, acid-fast bacilli (AFB) smear microscopy, and interferon-gamma release assay (IGRA, by T-SPOT®.TB) at PKUPH from November 2016 to October 2018 were reviewed. Active TB cases were considered according to a composite reference standard (CRS). Then, the three methods were evaluated and compared. Results In total, 787 patients simultaneously tested with Xpert, AFB, and IGRA were enrolled; among them 11.3% (89/787) were diagnosed and confirmed active pulmonary TB (PTB, 52 cases), extrapulmonary TB (EPTB, 17 cases), and tuberculous pleurisy (TP, 20 cases). The sensitivity of Xpert in detecting PTB, EPTB, and TP was 88.5, 76.5, and 15.0%, respectively, which was slightly lower than IGRA (96.2, 82.4, and 95.0%, respectively), but higher than AFB (36.5, 11.8, and 0%, respectively); IGRA showed the highest sensitivity, but its specificity (55.9, 67.1, and 45.2%, respectively) was significantly lower than Xpert (99.6, 99.4, and 100%, respectively) and AFB (99.0, 99.4, and 100%, respectively) (P < 0.001). The sensitivity of Xpert in detecting lung tissue, cerebrospinal fluid, lymph nodes, and joint fluid was 100%, followed by sputum (88.5%), alveolar lavage (85.7%), and bronchoscopy secretion (81.2%); the pleural fluid sensitivity was the lowest, only 15.0%. For AFB negative patients, the sensitivity of Xpert in detecting PTB, EPTB, and TP was 84.9, 73.3, and 15.0%, respectively. Conclusions Xpert showed both high sensitivity and high specificity, and suggested its high value in TB diagnosis; however, the application of pleural fluid is still limited, and should be improved. Owing to the high sensitivity of IGRA, it is recommended for use as a supplementary test, especially for assisting in the diagnosis of TP and EPTB. Xpert MTB/RIF (dpeaa)DE-He213 Tuberculosis (dpeaa)DE-He213 Pulmonary tuberculosis (dpeaa)DE-He213 Extra-pulmonary tuberculosis (dpeaa)DE-He213 Tuberculous pleurisy (dpeaa)DE-He213 Lin, Liyan verfasserin aut Zhang, Feifei verfasserin aut Zhao, Chunjiang verfasserin aut Meng, Han verfasserin aut Wang, Hui verfasserin aut Enthalten in BMC infectious diseases London : BioMed Central, 2001 20(2020), 1 vom: 24. Mai (DE-627)326645381 (DE-600)2041550-3 1471-2334 nnns volume:20 year:2020 number:1 day:24 month:05 https://dx.doi.org/10.1186/s12879-020-05004-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 20 2020 1 24 05 |
allfields_unstemmed |
10.1186/s12879-020-05004-8 doi (DE-627)SPR039815331 (SPR)s12879-020-05004-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Li, Shuguang verfasserin aut A retrospective study on Xpert MTB/RIF for detection of tuberculosis in a teaching hospital in China 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background The Xpert MTB/RIF assay is an automated molecular test that is designed to simultaneously detect Mycobacterium tuberculosis (MTB) complex and rifampin resistance. However, there are relatively few studies on this method in China. Xpert has been routinely used at Peking University People’s Hospital (PKUPH) since November 2016. Thus, the aim of this study was to evaluate the performance of Xpert, and provide a reference and guidance for the detection and diagnosis of TB in non-TB specialized hospitals. Methods The medical records of inpatients simultaneously tested with Xpert, acid-fast bacilli (AFB) smear microscopy, and interferon-gamma release assay (IGRA, by T-SPOT®.TB) at PKUPH from November 2016 to October 2018 were reviewed. Active TB cases were considered according to a composite reference standard (CRS). Then, the three methods were evaluated and compared. Results In total, 787 patients simultaneously tested with Xpert, AFB, and IGRA were enrolled; among them 11.3% (89/787) were diagnosed and confirmed active pulmonary TB (PTB, 52 cases), extrapulmonary TB (EPTB, 17 cases), and tuberculous pleurisy (TP, 20 cases). The sensitivity of Xpert in detecting PTB, EPTB, and TP was 88.5, 76.5, and 15.0%, respectively, which was slightly lower than IGRA (96.2, 82.4, and 95.0%, respectively), but higher than AFB (36.5, 11.8, and 0%, respectively); IGRA showed the highest sensitivity, but its specificity (55.9, 67.1, and 45.2%, respectively) was significantly lower than Xpert (99.6, 99.4, and 100%, respectively) and AFB (99.0, 99.4, and 100%, respectively) (P < 0.001). The sensitivity of Xpert in detecting lung tissue, cerebrospinal fluid, lymph nodes, and joint fluid was 100%, followed by sputum (88.5%), alveolar lavage (85.7%), and bronchoscopy secretion (81.2%); the pleural fluid sensitivity was the lowest, only 15.0%. For AFB negative patients, the sensitivity of Xpert in detecting PTB, EPTB, and TP was 84.9, 73.3, and 15.0%, respectively. Conclusions Xpert showed both high sensitivity and high specificity, and suggested its high value in TB diagnosis; however, the application of pleural fluid is still limited, and should be improved. Owing to the high sensitivity of IGRA, it is recommended for use as a supplementary test, especially for assisting in the diagnosis of TP and EPTB. Xpert MTB/RIF (dpeaa)DE-He213 Tuberculosis (dpeaa)DE-He213 Pulmonary tuberculosis (dpeaa)DE-He213 Extra-pulmonary tuberculosis (dpeaa)DE-He213 Tuberculous pleurisy (dpeaa)DE-He213 Lin, Liyan verfasserin aut Zhang, Feifei verfasserin aut Zhao, Chunjiang verfasserin aut Meng, Han verfasserin aut Wang, Hui verfasserin aut Enthalten in BMC infectious diseases London : BioMed Central, 2001 20(2020), 1 vom: 24. Mai (DE-627)326645381 (DE-600)2041550-3 1471-2334 nnns volume:20 year:2020 number:1 day:24 month:05 https://dx.doi.org/10.1186/s12879-020-05004-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 20 2020 1 24 05 |
allfieldsGer |
10.1186/s12879-020-05004-8 doi (DE-627)SPR039815331 (SPR)s12879-020-05004-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Li, Shuguang verfasserin aut A retrospective study on Xpert MTB/RIF for detection of tuberculosis in a teaching hospital in China 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background The Xpert MTB/RIF assay is an automated molecular test that is designed to simultaneously detect Mycobacterium tuberculosis (MTB) complex and rifampin resistance. However, there are relatively few studies on this method in China. Xpert has been routinely used at Peking University People’s Hospital (PKUPH) since November 2016. Thus, the aim of this study was to evaluate the performance of Xpert, and provide a reference and guidance for the detection and diagnosis of TB in non-TB specialized hospitals. Methods The medical records of inpatients simultaneously tested with Xpert, acid-fast bacilli (AFB) smear microscopy, and interferon-gamma release assay (IGRA, by T-SPOT®.TB) at PKUPH from November 2016 to October 2018 were reviewed. Active TB cases were considered according to a composite reference standard (CRS). Then, the three methods were evaluated and compared. Results In total, 787 patients simultaneously tested with Xpert, AFB, and IGRA were enrolled; among them 11.3% (89/787) were diagnosed and confirmed active pulmonary TB (PTB, 52 cases), extrapulmonary TB (EPTB, 17 cases), and tuberculous pleurisy (TP, 20 cases). The sensitivity of Xpert in detecting PTB, EPTB, and TP was 88.5, 76.5, and 15.0%, respectively, which was slightly lower than IGRA (96.2, 82.4, and 95.0%, respectively), but higher than AFB (36.5, 11.8, and 0%, respectively); IGRA showed the highest sensitivity, but its specificity (55.9, 67.1, and 45.2%, respectively) was significantly lower than Xpert (99.6, 99.4, and 100%, respectively) and AFB (99.0, 99.4, and 100%, respectively) (P < 0.001). The sensitivity of Xpert in detecting lung tissue, cerebrospinal fluid, lymph nodes, and joint fluid was 100%, followed by sputum (88.5%), alveolar lavage (85.7%), and bronchoscopy secretion (81.2%); the pleural fluid sensitivity was the lowest, only 15.0%. For AFB negative patients, the sensitivity of Xpert in detecting PTB, EPTB, and TP was 84.9, 73.3, and 15.0%, respectively. Conclusions Xpert showed both high sensitivity and high specificity, and suggested its high value in TB diagnosis; however, the application of pleural fluid is still limited, and should be improved. Owing to the high sensitivity of IGRA, it is recommended for use as a supplementary test, especially for assisting in the diagnosis of TP and EPTB. Xpert MTB/RIF (dpeaa)DE-He213 Tuberculosis (dpeaa)DE-He213 Pulmonary tuberculosis (dpeaa)DE-He213 Extra-pulmonary tuberculosis (dpeaa)DE-He213 Tuberculous pleurisy (dpeaa)DE-He213 Lin, Liyan verfasserin aut Zhang, Feifei verfasserin aut Zhao, Chunjiang verfasserin aut Meng, Han verfasserin aut Wang, Hui verfasserin aut Enthalten in BMC infectious diseases London : BioMed Central, 2001 20(2020), 1 vom: 24. Mai (DE-627)326645381 (DE-600)2041550-3 1471-2334 nnns volume:20 year:2020 number:1 day:24 month:05 https://dx.doi.org/10.1186/s12879-020-05004-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 20 2020 1 24 05 |
allfieldsSound |
10.1186/s12879-020-05004-8 doi (DE-627)SPR039815331 (SPR)s12879-020-05004-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Li, Shuguang verfasserin aut A retrospective study on Xpert MTB/RIF for detection of tuberculosis in a teaching hospital in China 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background The Xpert MTB/RIF assay is an automated molecular test that is designed to simultaneously detect Mycobacterium tuberculosis (MTB) complex and rifampin resistance. However, there are relatively few studies on this method in China. Xpert has been routinely used at Peking University People’s Hospital (PKUPH) since November 2016. Thus, the aim of this study was to evaluate the performance of Xpert, and provide a reference and guidance for the detection and diagnosis of TB in non-TB specialized hospitals. Methods The medical records of inpatients simultaneously tested with Xpert, acid-fast bacilli (AFB) smear microscopy, and interferon-gamma release assay (IGRA, by T-SPOT®.TB) at PKUPH from November 2016 to October 2018 were reviewed. Active TB cases were considered according to a composite reference standard (CRS). Then, the three methods were evaluated and compared. Results In total, 787 patients simultaneously tested with Xpert, AFB, and IGRA were enrolled; among them 11.3% (89/787) were diagnosed and confirmed active pulmonary TB (PTB, 52 cases), extrapulmonary TB (EPTB, 17 cases), and tuberculous pleurisy (TP, 20 cases). The sensitivity of Xpert in detecting PTB, EPTB, and TP was 88.5, 76.5, and 15.0%, respectively, which was slightly lower than IGRA (96.2, 82.4, and 95.0%, respectively), but higher than AFB (36.5, 11.8, and 0%, respectively); IGRA showed the highest sensitivity, but its specificity (55.9, 67.1, and 45.2%, respectively) was significantly lower than Xpert (99.6, 99.4, and 100%, respectively) and AFB (99.0, 99.4, and 100%, respectively) (P < 0.001). The sensitivity of Xpert in detecting lung tissue, cerebrospinal fluid, lymph nodes, and joint fluid was 100%, followed by sputum (88.5%), alveolar lavage (85.7%), and bronchoscopy secretion (81.2%); the pleural fluid sensitivity was the lowest, only 15.0%. For AFB negative patients, the sensitivity of Xpert in detecting PTB, EPTB, and TP was 84.9, 73.3, and 15.0%, respectively. Conclusions Xpert showed both high sensitivity and high specificity, and suggested its high value in TB diagnosis; however, the application of pleural fluid is still limited, and should be improved. Owing to the high sensitivity of IGRA, it is recommended for use as a supplementary test, especially for assisting in the diagnosis of TP and EPTB. Xpert MTB/RIF (dpeaa)DE-He213 Tuberculosis (dpeaa)DE-He213 Pulmonary tuberculosis (dpeaa)DE-He213 Extra-pulmonary tuberculosis (dpeaa)DE-He213 Tuberculous pleurisy (dpeaa)DE-He213 Lin, Liyan verfasserin aut Zhang, Feifei verfasserin aut Zhao, Chunjiang verfasserin aut Meng, Han verfasserin aut Wang, Hui verfasserin aut Enthalten in BMC infectious diseases London : BioMed Central, 2001 20(2020), 1 vom: 24. Mai (DE-627)326645381 (DE-600)2041550-3 1471-2334 nnns volume:20 year:2020 number:1 day:24 month:05 https://dx.doi.org/10.1186/s12879-020-05004-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 20 2020 1 24 05 |
language |
English |
source |
Enthalten in BMC infectious diseases 20(2020), 1 vom: 24. Mai volume:20 year:2020 number:1 day:24 month:05 |
sourceStr |
Enthalten in BMC infectious diseases 20(2020), 1 vom: 24. Mai volume:20 year:2020 number:1 day:24 month:05 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Xpert MTB/RIF Tuberculosis Pulmonary tuberculosis Extra-pulmonary tuberculosis Tuberculous pleurisy |
dewey-raw |
610 |
isfreeaccess_bool |
true |
container_title |
BMC infectious diseases |
authorswithroles_txt_mv |
Li, Shuguang @@aut@@ Lin, Liyan @@aut@@ Zhang, Feifei @@aut@@ Zhao, Chunjiang @@aut@@ Meng, Han @@aut@@ Wang, Hui @@aut@@ |
publishDateDaySort_date |
2020-05-24T00:00:00Z |
hierarchy_top_id |
326645381 |
dewey-sort |
3610 |
id |
SPR039815331 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR039815331</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519161417.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s12879-020-05004-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR039815331</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12879-020-05004-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Shuguang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A retrospective study on Xpert MTB/RIF for detection of tuberculosis in a teaching hospital in China</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background The Xpert MTB/RIF assay is an automated molecular test that is designed to simultaneously detect Mycobacterium tuberculosis (MTB) complex and rifampin resistance. However, there are relatively few studies on this method in China. Xpert has been routinely used at Peking University People’s Hospital (PKUPH) since November 2016. Thus, the aim of this study was to evaluate the performance of Xpert, and provide a reference and guidance for the detection and diagnosis of TB in non-TB specialized hospitals. Methods The medical records of inpatients simultaneously tested with Xpert, acid-fast bacilli (AFB) smear microscopy, and interferon-gamma release assay (IGRA, by T-SPOT®.TB) at PKUPH from November 2016 to October 2018 were reviewed. Active TB cases were considered according to a composite reference standard (CRS). Then, the three methods were evaluated and compared. Results In total, 787 patients simultaneously tested with Xpert, AFB, and IGRA were enrolled; among them 11.3% (89/787) were diagnosed and confirmed active pulmonary TB (PTB, 52 cases), extrapulmonary TB (EPTB, 17 cases), and tuberculous pleurisy (TP, 20 cases). The sensitivity of Xpert in detecting PTB, EPTB, and TP was 88.5, 76.5, and 15.0%, respectively, which was slightly lower than IGRA (96.2, 82.4, and 95.0%, respectively), but higher than AFB (36.5, 11.8, and 0%, respectively); IGRA showed the highest sensitivity, but its specificity (55.9, 67.1, and 45.2%, respectively) was significantly lower than Xpert (99.6, 99.4, and 100%, respectively) and AFB (99.0, 99.4, and 100%, respectively) (P < 0.001). The sensitivity of Xpert in detecting lung tissue, cerebrospinal fluid, lymph nodes, and joint fluid was 100%, followed by sputum (88.5%), alveolar lavage (85.7%), and bronchoscopy secretion (81.2%); the pleural fluid sensitivity was the lowest, only 15.0%. For AFB negative patients, the sensitivity of Xpert in detecting PTB, EPTB, and TP was 84.9, 73.3, and 15.0%, respectively. Conclusions Xpert showed both high sensitivity and high specificity, and suggested its high value in TB diagnosis; however, the application of pleural fluid is still limited, and should be improved. Owing to the high sensitivity of IGRA, it is recommended for use as a supplementary test, especially for assisting in the diagnosis of TP and EPTB.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Xpert MTB/RIF</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Tuberculosis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pulmonary tuberculosis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Extra-pulmonary tuberculosis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Tuberculous pleurisy</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lin, Liyan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Feifei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Chunjiang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Meng, Han</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Hui</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">BMC infectious diseases</subfield><subfield code="d">London : BioMed Central, 2001</subfield><subfield code="g">20(2020), 1 vom: 24. Mai</subfield><subfield code="w">(DE-627)326645381</subfield><subfield code="w">(DE-600)2041550-3</subfield><subfield code="x">1471-2334</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:20</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">day:24</subfield><subfield code="g">month:05</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s12879-020-05004-8</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.00</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">20</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="b">24</subfield><subfield code="c">05</subfield></datafield></record></collection>
|
author |
Li, Shuguang |
spellingShingle |
Li, Shuguang ddc 610 bkl 44.00 misc Xpert MTB/RIF misc Tuberculosis misc Pulmonary tuberculosis misc Extra-pulmonary tuberculosis misc Tuberculous pleurisy A retrospective study on Xpert MTB/RIF for detection of tuberculosis in a teaching hospital in China |
authorStr |
Li, Shuguang |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)326645381 |
format |
electronic Article |
dewey-ones |
610 - Medicine & health |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut aut |
collection |
springer |
remote_str |
true |
illustrated |
Not Illustrated |
issn |
1471-2334 |
topic_title |
610 ASE 44.00 bkl A retrospective study on Xpert MTB/RIF for detection of tuberculosis in a teaching hospital in China Xpert MTB/RIF (dpeaa)DE-He213 Tuberculosis (dpeaa)DE-He213 Pulmonary tuberculosis (dpeaa)DE-He213 Extra-pulmonary tuberculosis (dpeaa)DE-He213 Tuberculous pleurisy (dpeaa)DE-He213 |
topic |
ddc 610 bkl 44.00 misc Xpert MTB/RIF misc Tuberculosis misc Pulmonary tuberculosis misc Extra-pulmonary tuberculosis misc Tuberculous pleurisy |
topic_unstemmed |
ddc 610 bkl 44.00 misc Xpert MTB/RIF misc Tuberculosis misc Pulmonary tuberculosis misc Extra-pulmonary tuberculosis misc Tuberculous pleurisy |
topic_browse |
ddc 610 bkl 44.00 misc Xpert MTB/RIF misc Tuberculosis misc Pulmonary tuberculosis misc Extra-pulmonary tuberculosis misc Tuberculous pleurisy |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
BMC infectious diseases |
hierarchy_parent_id |
326645381 |
dewey-tens |
610 - Medicine & health |
hierarchy_top_title |
BMC infectious diseases |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)326645381 (DE-600)2041550-3 |
title |
A retrospective study on Xpert MTB/RIF for detection of tuberculosis in a teaching hospital in China |
ctrlnum |
(DE-627)SPR039815331 (SPR)s12879-020-05004-8-e |
title_full |
A retrospective study on Xpert MTB/RIF for detection of tuberculosis in a teaching hospital in China |
author_sort |
Li, Shuguang |
journal |
BMC infectious diseases |
journalStr |
BMC infectious diseases |
lang_code |
eng |
isOA_bool |
true |
dewey-hundreds |
600 - Technology |
recordtype |
marc |
publishDateSort |
2020 |
contenttype_str_mv |
txt |
author_browse |
Li, Shuguang Lin, Liyan Zhang, Feifei Zhao, Chunjiang Meng, Han Wang, Hui |
container_volume |
20 |
class |
610 ASE 44.00 bkl |
format_se |
Elektronische Aufsätze |
author-letter |
Li, Shuguang |
doi_str_mv |
10.1186/s12879-020-05004-8 |
dewey-full |
610 |
author2-role |
verfasserin |
title_sort |
retrospective study on xpert mtb/rif for detection of tuberculosis in a teaching hospital in china |
title_auth |
A retrospective study on Xpert MTB/RIF for detection of tuberculosis in a teaching hospital in China |
abstract |
Background The Xpert MTB/RIF assay is an automated molecular test that is designed to simultaneously detect Mycobacterium tuberculosis (MTB) complex and rifampin resistance. However, there are relatively few studies on this method in China. Xpert has been routinely used at Peking University People’s Hospital (PKUPH) since November 2016. Thus, the aim of this study was to evaluate the performance of Xpert, and provide a reference and guidance for the detection and diagnosis of TB in non-TB specialized hospitals. Methods The medical records of inpatients simultaneously tested with Xpert, acid-fast bacilli (AFB) smear microscopy, and interferon-gamma release assay (IGRA, by T-SPOT®.TB) at PKUPH from November 2016 to October 2018 were reviewed. Active TB cases were considered according to a composite reference standard (CRS). Then, the three methods were evaluated and compared. Results In total, 787 patients simultaneously tested with Xpert, AFB, and IGRA were enrolled; among them 11.3% (89/787) were diagnosed and confirmed active pulmonary TB (PTB, 52 cases), extrapulmonary TB (EPTB, 17 cases), and tuberculous pleurisy (TP, 20 cases). The sensitivity of Xpert in detecting PTB, EPTB, and TP was 88.5, 76.5, and 15.0%, respectively, which was slightly lower than IGRA (96.2, 82.4, and 95.0%, respectively), but higher than AFB (36.5, 11.8, and 0%, respectively); IGRA showed the highest sensitivity, but its specificity (55.9, 67.1, and 45.2%, respectively) was significantly lower than Xpert (99.6, 99.4, and 100%, respectively) and AFB (99.0, 99.4, and 100%, respectively) (P < 0.001). The sensitivity of Xpert in detecting lung tissue, cerebrospinal fluid, lymph nodes, and joint fluid was 100%, followed by sputum (88.5%), alveolar lavage (85.7%), and bronchoscopy secretion (81.2%); the pleural fluid sensitivity was the lowest, only 15.0%. For AFB negative patients, the sensitivity of Xpert in detecting PTB, EPTB, and TP was 84.9, 73.3, and 15.0%, respectively. Conclusions Xpert showed both high sensitivity and high specificity, and suggested its high value in TB diagnosis; however, the application of pleural fluid is still limited, and should be improved. Owing to the high sensitivity of IGRA, it is recommended for use as a supplementary test, especially for assisting in the diagnosis of TP and EPTB. |
abstractGer |
Background The Xpert MTB/RIF assay is an automated molecular test that is designed to simultaneously detect Mycobacterium tuberculosis (MTB) complex and rifampin resistance. However, there are relatively few studies on this method in China. Xpert has been routinely used at Peking University People’s Hospital (PKUPH) since November 2016. Thus, the aim of this study was to evaluate the performance of Xpert, and provide a reference and guidance for the detection and diagnosis of TB in non-TB specialized hospitals. Methods The medical records of inpatients simultaneously tested with Xpert, acid-fast bacilli (AFB) smear microscopy, and interferon-gamma release assay (IGRA, by T-SPOT®.TB) at PKUPH from November 2016 to October 2018 were reviewed. Active TB cases were considered according to a composite reference standard (CRS). Then, the three methods were evaluated and compared. Results In total, 787 patients simultaneously tested with Xpert, AFB, and IGRA were enrolled; among them 11.3% (89/787) were diagnosed and confirmed active pulmonary TB (PTB, 52 cases), extrapulmonary TB (EPTB, 17 cases), and tuberculous pleurisy (TP, 20 cases). The sensitivity of Xpert in detecting PTB, EPTB, and TP was 88.5, 76.5, and 15.0%, respectively, which was slightly lower than IGRA (96.2, 82.4, and 95.0%, respectively), but higher than AFB (36.5, 11.8, and 0%, respectively); IGRA showed the highest sensitivity, but its specificity (55.9, 67.1, and 45.2%, respectively) was significantly lower than Xpert (99.6, 99.4, and 100%, respectively) and AFB (99.0, 99.4, and 100%, respectively) (P < 0.001). The sensitivity of Xpert in detecting lung tissue, cerebrospinal fluid, lymph nodes, and joint fluid was 100%, followed by sputum (88.5%), alveolar lavage (85.7%), and bronchoscopy secretion (81.2%); the pleural fluid sensitivity was the lowest, only 15.0%. For AFB negative patients, the sensitivity of Xpert in detecting PTB, EPTB, and TP was 84.9, 73.3, and 15.0%, respectively. Conclusions Xpert showed both high sensitivity and high specificity, and suggested its high value in TB diagnosis; however, the application of pleural fluid is still limited, and should be improved. Owing to the high sensitivity of IGRA, it is recommended for use as a supplementary test, especially for assisting in the diagnosis of TP and EPTB. |
abstract_unstemmed |
Background The Xpert MTB/RIF assay is an automated molecular test that is designed to simultaneously detect Mycobacterium tuberculosis (MTB) complex and rifampin resistance. However, there are relatively few studies on this method in China. Xpert has been routinely used at Peking University People’s Hospital (PKUPH) since November 2016. Thus, the aim of this study was to evaluate the performance of Xpert, and provide a reference and guidance for the detection and diagnosis of TB in non-TB specialized hospitals. Methods The medical records of inpatients simultaneously tested with Xpert, acid-fast bacilli (AFB) smear microscopy, and interferon-gamma release assay (IGRA, by T-SPOT®.TB) at PKUPH from November 2016 to October 2018 were reviewed. Active TB cases were considered according to a composite reference standard (CRS). Then, the three methods were evaluated and compared. Results In total, 787 patients simultaneously tested with Xpert, AFB, and IGRA were enrolled; among them 11.3% (89/787) were diagnosed and confirmed active pulmonary TB (PTB, 52 cases), extrapulmonary TB (EPTB, 17 cases), and tuberculous pleurisy (TP, 20 cases). The sensitivity of Xpert in detecting PTB, EPTB, and TP was 88.5, 76.5, and 15.0%, respectively, which was slightly lower than IGRA (96.2, 82.4, and 95.0%, respectively), but higher than AFB (36.5, 11.8, and 0%, respectively); IGRA showed the highest sensitivity, but its specificity (55.9, 67.1, and 45.2%, respectively) was significantly lower than Xpert (99.6, 99.4, and 100%, respectively) and AFB (99.0, 99.4, and 100%, respectively) (P < 0.001). The sensitivity of Xpert in detecting lung tissue, cerebrospinal fluid, lymph nodes, and joint fluid was 100%, followed by sputum (88.5%), alveolar lavage (85.7%), and bronchoscopy secretion (81.2%); the pleural fluid sensitivity was the lowest, only 15.0%. For AFB negative patients, the sensitivity of Xpert in detecting PTB, EPTB, and TP was 84.9, 73.3, and 15.0%, respectively. Conclusions Xpert showed both high sensitivity and high specificity, and suggested its high value in TB diagnosis; however, the application of pleural fluid is still limited, and should be improved. Owing to the high sensitivity of IGRA, it is recommended for use as a supplementary test, especially for assisting in the diagnosis of TP and EPTB. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
container_issue |
1 |
title_short |
A retrospective study on Xpert MTB/RIF for detection of tuberculosis in a teaching hospital in China |
url |
https://dx.doi.org/10.1186/s12879-020-05004-8 |
remote_bool |
true |
author2 |
Lin, Liyan Zhang, Feifei Zhao, Chunjiang Meng, Han Wang, Hui |
author2Str |
Lin, Liyan Zhang, Feifei Zhao, Chunjiang Meng, Han Wang, Hui |
ppnlink |
326645381 |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.1186/s12879-020-05004-8 |
up_date |
2024-07-04T01:41:38.020Z |
_version_ |
1803610801331765248 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR039815331</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519161417.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s12879-020-05004-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR039815331</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12879-020-05004-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Shuguang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A retrospective study on Xpert MTB/RIF for detection of tuberculosis in a teaching hospital in China</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background The Xpert MTB/RIF assay is an automated molecular test that is designed to simultaneously detect Mycobacterium tuberculosis (MTB) complex and rifampin resistance. However, there are relatively few studies on this method in China. Xpert has been routinely used at Peking University People’s Hospital (PKUPH) since November 2016. Thus, the aim of this study was to evaluate the performance of Xpert, and provide a reference and guidance for the detection and diagnosis of TB in non-TB specialized hospitals. Methods The medical records of inpatients simultaneously tested with Xpert, acid-fast bacilli (AFB) smear microscopy, and interferon-gamma release assay (IGRA, by T-SPOT®.TB) at PKUPH from November 2016 to October 2018 were reviewed. Active TB cases were considered according to a composite reference standard (CRS). Then, the three methods were evaluated and compared. Results In total, 787 patients simultaneously tested with Xpert, AFB, and IGRA were enrolled; among them 11.3% (89/787) were diagnosed and confirmed active pulmonary TB (PTB, 52 cases), extrapulmonary TB (EPTB, 17 cases), and tuberculous pleurisy (TP, 20 cases). The sensitivity of Xpert in detecting PTB, EPTB, and TP was 88.5, 76.5, and 15.0%, respectively, which was slightly lower than IGRA (96.2, 82.4, and 95.0%, respectively), but higher than AFB (36.5, 11.8, and 0%, respectively); IGRA showed the highest sensitivity, but its specificity (55.9, 67.1, and 45.2%, respectively) was significantly lower than Xpert (99.6, 99.4, and 100%, respectively) and AFB (99.0, 99.4, and 100%, respectively) (P < 0.001). The sensitivity of Xpert in detecting lung tissue, cerebrospinal fluid, lymph nodes, and joint fluid was 100%, followed by sputum (88.5%), alveolar lavage (85.7%), and bronchoscopy secretion (81.2%); the pleural fluid sensitivity was the lowest, only 15.0%. For AFB negative patients, the sensitivity of Xpert in detecting PTB, EPTB, and TP was 84.9, 73.3, and 15.0%, respectively. Conclusions Xpert showed both high sensitivity and high specificity, and suggested its high value in TB diagnosis; however, the application of pleural fluid is still limited, and should be improved. Owing to the high sensitivity of IGRA, it is recommended for use as a supplementary test, especially for assisting in the diagnosis of TP and EPTB.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Xpert MTB/RIF</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Tuberculosis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pulmonary tuberculosis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Extra-pulmonary tuberculosis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Tuberculous pleurisy</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lin, Liyan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Feifei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Chunjiang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Meng, Han</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Hui</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">BMC infectious diseases</subfield><subfield code="d">London : BioMed Central, 2001</subfield><subfield code="g">20(2020), 1 vom: 24. Mai</subfield><subfield code="w">(DE-627)326645381</subfield><subfield code="w">(DE-600)2041550-3</subfield><subfield code="x">1471-2334</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:20</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">day:24</subfield><subfield code="g">month:05</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s12879-020-05004-8</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.00</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">20</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="b">24</subfield><subfield code="c">05</subfield></datafield></record></collection>
|
score |
7.399781 |