Evidence map›Paper›PMID 26860394›Full record

ArticleScientific reports2016

The role of Xpert MTB/RIF in diagnosing pulmonary tuberculosis in post-mortem tissues.

Alberto L García-Basteiro, Mamudo R Ismail, Carla Carrilho, Esperança Ussene, Paola Castillo, Dércio Chitsungo, Cristina Rodríguez, Lucília Lovane, Andrea Vergara, Elisa López-Varela and 6 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.9field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 24 citations in OpenAlex.

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  4. Performance of Xpert Ultra nasopharyngeal swab for identification of tuberculosis deaths in northern Tanzania.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 5 institutions in 3 countries.

Alberto L García-BasteiroCentro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique.
Mamudo R IsmailDepartment of Pathology, Faculty of Medicine/Eduardo Mondlane University and Maputo Central Hospital, Maputo, Mozambique.
Carla CarrilhoDepartment of Pathology, Faculty of Medicine/Eduardo Mondlane University and Maputo Central Hospital, Maputo, Mozambique.
Esperança UsseneDepartment of Pathology, Faculty of Medicine/Eduardo Mondlane University and Maputo Central Hospital, Maputo, Mozambique.
Paola CastilloISGlobal, Barcelona Ctr. Int. Health Res. (CRESIB), Hospital Clínic - Universitat de Barcelona, Barcelona, Spain.
Dércio ChitsungoDepartment of Pathology, Faculty of Medicine/Eduardo Mondlane University and Maputo Central Hospital, Maputo, Mozambique.
Cristina RodríguezISGlobal, Barcelona Ctr. Int. Health Res. (CRESIB), Hospital Clínic - Universitat de Barcelona, Barcelona, Spain.
Lucília LovaneDepartment of Pathology, Faculty of Medicine/Eduardo Mondlane University and Maputo Central Hospital, Maputo, Mozambique.
Andrea VergaraDepartment of Clinical Microbiology, Hospital Clinic, Universitat de Barcelona, Spain.
Elisa López-VarelaCentro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique.
Inacio MandomandoCentro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique.
Cesaltina LorenzoniDepartment of Pathology, Faculty of Medicine/Eduardo Mondlane University and Maputo Central Hospital, Maputo, Mozambique.
Jaume OrdiISGlobal, Barcelona Ctr. Int. Health Res. (CRESIB), Hospital Clínic - Universitat de Barcelona, Barcelona, Spain.
Clara MenéndezCentro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique.
Quique BassatCentro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique.
Miguel J MartínezISGlobal, Barcelona Ctr. Int. Health Res. (CRESIB), Hospital Clínic - Universitat de Barcelona, Barcelona, Spain.
Barcelona Institute for Global Health · ESEduardo Mondlane University · MZUniversitat de Barcelona · ESManhiça Health Research Centre · MZMaputo Central Hospital · MZ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The extent to which the Xpert MTB/RIF (Gene Xpert) contributes to tuberculosis (TB) diagnosis in samples other than sputum and cerebrospinal fluid remains uncertain. We aimed to assess the role of Xpert MTB/RIF for detecting M. tuberculosis in post-mortem tissues. We conducted a study among 30 complete diagnostic autopsies (CDA) performed at the Maputo Central Hospital (Mozambique). Lung tissues were screened for TB in all cases. In addition other tissues were tested when compatible lesions were identified in the histological exam. We used in-house real time PCR and LAMP assays to confirm the presence of M. tuberculosis DNA. The diagnosis of tuberculosis at death was established based on microbiological and histopathological results. Eight out of 30 cases (26.7%) were diagnosed of tuberculosis. Xpert had a sensitivity to detect TB in lung tissue of 87.5% (95% CI 47.3-99.7) and a specificity of 95.7% (95% CI: 78.1-99.9). In-house DNA amplification methods and Xpert showed 93.6% concordance for lung tissue and 100% concordance for brain and liver tissues. The final cause of death was attributable to tuberculosis in four cases. Xpert MTB/RIF may represent a valuable, easy-to perform technique for post-mortem TB diagnosis.

Indexed as

Nucleic Acid Amplification TechniquesAdolescentAdultAgedBrainCause of DeathDNA, BacterialDrug Resistance, Multiple, BacterialFemaleHumansLiverLungMaleMiddle AgedMycobacterium tuberculosisReagent Kits, DiagnosticDNA, BacterialReagent Kits, DiagnosticRifampin

Identifiers

PMID26860394
PMCPMC4748254
OpenAlexW2272336453

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.