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Machine learning reveals that Mycobacterium tuberculosis genotypes and anatomic disease site impacts drug resistance and disease transmission among patients with proven extra-pulmonary tuberculosis

BACKGROUND: There is a general dearth of information on extrapulmonary tuberculosis (EPTB). Here, we investigated Mycobacterium tuberculosis (Mtb) drug resistance and transmission patterns in EPTB patients treated in the Tshwane metropolitan area, in South Africa. METHODS: Consecutive Mtb culture-po...

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Autores principales: Sibandze, Doctor B., Magazi, Beki T., Malinga, Lesibana A., Maningi, Nontuthuko E., Shey, Bong-Akee, Pasipanodya, Jotam G., Mbelle, Nontombi N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7393820/
https://www.ncbi.nlm.nih.gov/pubmed/32736602
http://dx.doi.org/10.1186/s12879-020-05256-4
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author Sibandze, Doctor B.
Magazi, Beki T.
Malinga, Lesibana A.
Maningi, Nontuthuko E.
Shey, Bong-Akee
Pasipanodya, Jotam G.
Mbelle, Nontombi N.
author_facet Sibandze, Doctor B.
Magazi, Beki T.
Malinga, Lesibana A.
Maningi, Nontuthuko E.
Shey, Bong-Akee
Pasipanodya, Jotam G.
Mbelle, Nontombi N.
author_sort Sibandze, Doctor B.
collection PubMed
description BACKGROUND: There is a general dearth of information on extrapulmonary tuberculosis (EPTB). Here, we investigated Mycobacterium tuberculosis (Mtb) drug resistance and transmission patterns in EPTB patients treated in the Tshwane metropolitan area, in South Africa. METHODS: Consecutive Mtb culture-positive non-pulmonary samples from unique EPTB patients underwent mycobacterial genotyping and were assigned to phylogenetic lineages and transmission clusters based on spoligotypes. MTBDRplus assay was used to search mutations for isoniazid and rifampin resistance. Machine learning algorithms were used to identify clinically meaningful patterns in data. We computed odds ratio (OR), attributable risk (AR) and corresponding 95% confidence intervals (CI). RESULTS: Of the 70 isolates examined, the largest cluster comprised 25 (36%) Mtb strains that belonged to the East Asian lineage. East Asian lineage was significantly more likely to occur within chains of transmission when compared to the Euro-American and East-African Indian lineages: OR = 10.11 (95% CI: 1.56–116). Lymphadenitis, meningitis and cutaneous TB, were significantly more likely to be associated with drug resistance: OR = 12.69 (95% CI: 1.82–141.60) and AR = 0.25 (95% CI: 0.06–0.43) when compared with other EPTB sites, which suggests that poor rifampin penetration might be a contributing factor. CONCLUSIONS: The majority of Mtb strains circulating in the Tshwane metropolis belongs to East Asian, Euro-American and East-African Indian lineages. Each of these are likely to be clustered, suggesting on-going EPTB transmission. Since 25% of the drug resistance was attributable to sanctuary EPTB sites notorious for poor rifampin penetration, we hypothesize that poor anti-tuberculosis drug dosing might have a role in the development of resistance.
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spelling pubmed-73938202020-08-04 Machine learning reveals that Mycobacterium tuberculosis genotypes and anatomic disease site impacts drug resistance and disease transmission among patients with proven extra-pulmonary tuberculosis Sibandze, Doctor B. Magazi, Beki T. Malinga, Lesibana A. Maningi, Nontuthuko E. Shey, Bong-Akee Pasipanodya, Jotam G. Mbelle, Nontombi N. BMC Infect Dis Research Article BACKGROUND: There is a general dearth of information on extrapulmonary tuberculosis (EPTB). Here, we investigated Mycobacterium tuberculosis (Mtb) drug resistance and transmission patterns in EPTB patients treated in the Tshwane metropolitan area, in South Africa. METHODS: Consecutive Mtb culture-positive non-pulmonary samples from unique EPTB patients underwent mycobacterial genotyping and were assigned to phylogenetic lineages and transmission clusters based on spoligotypes. MTBDRplus assay was used to search mutations for isoniazid and rifampin resistance. Machine learning algorithms were used to identify clinically meaningful patterns in data. We computed odds ratio (OR), attributable risk (AR) and corresponding 95% confidence intervals (CI). RESULTS: Of the 70 isolates examined, the largest cluster comprised 25 (36%) Mtb strains that belonged to the East Asian lineage. East Asian lineage was significantly more likely to occur within chains of transmission when compared to the Euro-American and East-African Indian lineages: OR = 10.11 (95% CI: 1.56–116). Lymphadenitis, meningitis and cutaneous TB, were significantly more likely to be associated with drug resistance: OR = 12.69 (95% CI: 1.82–141.60) and AR = 0.25 (95% CI: 0.06–0.43) when compared with other EPTB sites, which suggests that poor rifampin penetration might be a contributing factor. CONCLUSIONS: The majority of Mtb strains circulating in the Tshwane metropolis belongs to East Asian, Euro-American and East-African Indian lineages. Each of these are likely to be clustered, suggesting on-going EPTB transmission. Since 25% of the drug resistance was attributable to sanctuary EPTB sites notorious for poor rifampin penetration, we hypothesize that poor anti-tuberculosis drug dosing might have a role in the development of resistance. BioMed Central 2020-07-31 /pmc/articles/PMC7393820/ /pubmed/32736602 http://dx.doi.org/10.1186/s12879-020-05256-4 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Sibandze, Doctor B.
Magazi, Beki T.
Malinga, Lesibana A.
Maningi, Nontuthuko E.
Shey, Bong-Akee
Pasipanodya, Jotam G.
Mbelle, Nontombi N.
Machine learning reveals that Mycobacterium tuberculosis genotypes and anatomic disease site impacts drug resistance and disease transmission among patients with proven extra-pulmonary tuberculosis
title Machine learning reveals that Mycobacterium tuberculosis genotypes and anatomic disease site impacts drug resistance and disease transmission among patients with proven extra-pulmonary tuberculosis
title_full Machine learning reveals that Mycobacterium tuberculosis genotypes and anatomic disease site impacts drug resistance and disease transmission among patients with proven extra-pulmonary tuberculosis
title_fullStr Machine learning reveals that Mycobacterium tuberculosis genotypes and anatomic disease site impacts drug resistance and disease transmission among patients with proven extra-pulmonary tuberculosis
title_full_unstemmed Machine learning reveals that Mycobacterium tuberculosis genotypes and anatomic disease site impacts drug resistance and disease transmission among patients with proven extra-pulmonary tuberculosis
title_short Machine learning reveals that Mycobacterium tuberculosis genotypes and anatomic disease site impacts drug resistance and disease transmission among patients with proven extra-pulmonary tuberculosis
title_sort machine learning reveals that mycobacterium tuberculosis genotypes and anatomic disease site impacts drug resistance and disease transmission among patients with proven extra-pulmonary tuberculosis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7393820/
https://www.ncbi.nlm.nih.gov/pubmed/32736602
http://dx.doi.org/10.1186/s12879-020-05256-4
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