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Observational Study
. 2019 Oct 1;54(3):1900312.
doi: 10.1183/13993003.00312-2019. Print 2019 Sep.

Unmasking the hidden tuberculosis mortality burden in a large post mortem study in Maputo Central Hospital, Mozambique

Affiliations
Observational Study

Unmasking the hidden tuberculosis mortality burden in a large post mortem study in Maputo Central Hospital, Mozambique

Alberto L Garcia-Basteiro et al. Eur Respir J. .

Abstract

Sensitive tools are needed to accurately establish the diagnosis of tuberculosis (TB) at death, especially in low-income countries. The objective of this study was to evaluate the burden of TB in a series of patients who died in a tertiary referral hospital in sub-Saharan Africa using an in-house real time PCR (TB-PCR) and the Xpert MTB/RIF Ultra (Xpert Ultra) assay.Complete diagnostic autopsies were performed in a series of 223 deaths (56.5% being HIV-positive), including 54 children, 57 maternal deaths and 112 other adults occurring at the Maputo Central Hospital, Mozambique. TB-PCR was performed in all lung, cerebrospinal fluid and central nervous system samples in HIV-positive patients. All samples positive for TB-PCR or showing histological findings suggestive of TB were analysed with the Xpert Ultra assay.TB was identified as the cause of death in 31 patients: three out of 54 (6%) children, five out of 57 (9%)maternal deaths and 23 out of 112 (21%) other adults. The sensitivity of the main clinical diagnosis to detect TB as the cause of death was 19.4% (95% CI 7.5-37.5) and the specificity was 97.4% (94.0-99.1) compared to autopsy findings. Concomitant TB (TB disease in a patient dying of other causes) was found in 31 additional cases. Xpert Ultra helped to identify 15 cases of concomitant TB. In 18 patients, Mycobacterium tuberculosis DNA was identified by TB-PCR and Xpert Ultra in the absence of histological TB lesions. Overall, 62 (27.8%) cases had TB disease at death and 80 (35.9%) had TB findings.The use of highly sensitive, easy to perform molecular tests in complete diagnostic autopsies may contribute to identifying TB cases at death that would have otherwise been missed. Routine use of these tools in certain diagnostic algorithms for hospitalised patients needs to be considered. Clinical diagnosis showed poor sensitivity for the diagnosis of TB at death.

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Conflict of interest statement

Conflict of interest: A.L. Garcia-Basteiro has nothing to disclose. Conflict of interest: J.C. Hurtado has nothing to disclose. Conflict of interest: P. Castillo has nothing to disclose. Conflict of interest: F. Fernandes has nothing to disclose. Conflict of interest: M. Navarro has nothing to disclose. Conflict of interest: L. Lovane has nothing to disclose. Conflict of interest: I. Casas has nothing to disclose. Conflict of interest: L. Quinto has nothing to disclose. Conflict of interest: D. Jordao has nothing to disclose. Conflict of interest: M.R. Ismail has nothing to disclose. Conflict of interest: C. Lorenzoni has nothing to disclose. Conflict of interest: C. Carrilho has nothing to disclose. Conflict of interest: A. Sanz has nothing to disclose. Conflict of interest: N. Rakislova has nothing to disclose. Conflict of interest: A. Mira has nothing to disclose. Conflict of interest: M.J. Alvarez-Martinez has nothing to disclose. Conflict of interest: A. Cossa has nothing to disclose. Conflict of interest: F. Cobelens has nothing to disclose. Conflict of interest: I. Mandomando has nothing to disclose. Conflict of interest: J. Vila has nothing to disclose. Conflict of interest: Q. Bassat has nothing to disclose. Conflict of interest: C. Menendez has nothing to disclose. Conflict of interest: J. Ordi has nothing to disclose. Conflict of interest: M.J. Martinez has nothing to disclose.

Figures

FIGURE 1
FIGURE 1
Algorithm for determination of tuberculosis (TB) diagnosis used in samples from complete diagnostic autopsies. CNS: central nervous system; CSF: cerebrospinal fluid; M. tuberculosis: Mycobacterium tuberculosis.
FIGURE 2
FIGURE 2
Alluvial diagram showing cause of death (CoD) as assigned in the complete diagnostic autopsy (CDA) and per clinical diagnosis whenever a tuberculosis (TB) diagnosis was involved. The right  column shows all the TB diagnoses (as CoD) in CDA (n=31) as well as the associated CDA diagnoses when clinicians assigned TB as the CoD (n=5). The left column shows the clinical diagnoses of CoD when TB was specified by clinicians as well as the associated clinical diagnosis when the results of the CDA assigned TB as the CoD.
FIGURE 3
FIGURE 3
Natural history of the tuberculosis (TB) model depending on bacillary burden and the likelihood of having immunological, microbiological, histological or radiological evidence at the time of death. Adapted from a model described by Esmail et al. [14].

Comment in

References

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    1. Institute for Health Metrics and Evaluation (IHME). GBD Compare. Seattle, WA, IHME, University of Washington, 2016. http://vizhub.healthdata.org/gbd-compare. Date last accessed: June 20, 2018.

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