Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Meta-Analysis
. 2021 Jun 28;16(6):e0253848.
doi: 10.1371/journal.pone.0253848. eCollection 2021.

Predictors of mortality in patients with drug-resistant tuberculosis: A systematic review and meta-analysis

Affiliations
Meta-Analysis

Predictors of mortality in patients with drug-resistant tuberculosis: A systematic review and meta-analysis

Ayinalem Alemu et al. PLoS One. .

Abstract

Background: Even though the lives of millions have been saved in the past decades, the mortality rate in patients with drug-resistant tuberculosis is still high. Different factors are associated with this mortality. However, there is no comprehensive global report addressing these risk factors. This study aimed to determine the predictors of mortality using data generated at the global level.

Methods: We systematically searched five electronic major databases (PubMed/Medline, CINAHL, EMBASE, Scopus, Web of Science), and other sources (Google Scholar, Google). We used the Joanna Briggs Institute Critical Appraisal tools to assess the quality of included articles. Heterogeneity assessment was conducted using the forest plot and I2 heterogeneity test. Data were analyzed using STATA Version 15. The pooled hazard ratio, risk ratio, and odd's ratio were estimated along with their 95% CIs.

Result: After reviewing 640 articles, 49 studies met the inclusion criteria and were included in the final analysis. The predictors of mortality were; being male (HR = 1.25,95%CI;1.08,1.41,I2;30.5%), older age (HR = 2.13, 95%CI;1.64,2.62,I2;59.0%,RR = 1.40,95%CI; 1.26, 1.53, I2; 48.4%) including a 1 year increase in age (HR = 1.01, 95%CI;1.00,1.03,I2;73.0%), undernutrition (HR = 1.62,95%CI;1.28,1.97,I2;87.2%, RR = 3.13, 95% CI; 2.17,4.09, I2;0.0%), presence of any type of co-morbidity (HR = 1.92,95%CI;1.50-2.33,I2;61.4%, RR = 1.61, 95%CI;1.29, 1.93,I2;0.0%), having diabetes (HR = 1.74, 95%CI; 1.24,2.24, I2;37.3%, RR = 1.60, 95%CI;1.13,2.07, I2;0.0%), HIV co-infection (HR = 2.15, 95%CI;1.69,2.61, I2; 48.2%, RR = 1.49, 95%CI;1.27,1.72, I2;19.5%), TB history (HR = 1.30,95%CI;1.06,1.54, I2;64.6%), previous second-line anti-TB treatment (HR = 2.52, 95% CI;2.15,2.88, I2;0.0%), being smear positive at the baseline (HR = 1.45, 95%CI;1.14,1.76, I2;49.2%, RR = 1.58,95%CI;1.46,1.69, I2;48.7%), having XDR-TB (HR = 2.01, 95%CI;1.50,2.52, I2;60.8%, RR = 2.44, 95%CI;2.16,2.73,I2;46.1%), and any type of clinical complication (HR = 2.98, 95%CI; 2.32, 3.64, I2; 69.9%). There are differences and overlaps of predictors of mortality across different drug-resistance categories. The common predictors of mortality among different drug-resistance categories include; older age, presence of any type of co-morbidity, and undernutrition.

Conclusion: Different patient-related demographic (male sex, older age), and clinical factors (undernutrition, HIV co-infection, co-morbidity, diabetes, clinical complications, TB history, previous second-line anti-TB treatment, smear-positive TB, and XDR-TB) were the predictors of mortality in patients with drug-resistant tuberculosis. The findings would be an important input to the global community to take important measures.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flowchart diagram describing a selection of studies for the systematic review and meta-analysis on the predictors of mortality in patients with drug-resistant tuberculosis.
Fig 2
Fig 2. Forest plot for pooled incidence of mortality in patients with drug-resistant tuberculosis.
Fig 3
Fig 3. Forest plot for pooled incidence density rate of mortality in patients with drug-resistant tuberculosis.
Fig 4
Fig 4. Funnel plot showing publication bias among studies used to compute the incidence of mortality in patients with drug-resistant tuberculosis.
Fig 5
Fig 5. Funnel plot showing publication bias among studies used to compute the incidence density rate of mortality in patients with drug-resistant tuberculosis.
Fig 6
Fig 6. Forest plot for pooled incidence of mortality in patients with multi drug-resistant tuberculosis.
Fig 7
Fig 7. Forest plot for pooled incidence of mortality in patients with extensively drug-resistant tuberculosis.
Fig 8
Fig 8. Funnel plot showing publication bias among studies used to compute the incidence of mortality in patients with multi drug-resistant tuberculosis.
Fig 9
Fig 9. Funnel plot showing publication bias among studies used to compute the incidence of mortality in patients with extensively drug-resistant tuberculosis.
Fig 10
Fig 10. Forest plot for predictors of mortality in patients with drug-resistant tuberculosis.
A. Male sex B. Older age C. For every age D. Undernutrition E. Presence of any co-morbidity F. Diabetes G. HIV co-infection H. TB history I. Previous second-line treatment J. Smear positive K. XDR-TB L. Presence of clinical complication.

References

    1. WHO. Global Tuberculosis Report. Geneva, Switzerland: World Health Organization; 2019.
    1. Chuang CU, Van Weezennbeek C, Mori T, Enaeso DA. Challenges to the global control of tuberculosis. Respirology. 2013; 18: 596–604. doi: 10.1111/resp.12067 - DOI - PubMed
    1. Dye C, Garnett GP, Sleeman K, Williams BG. Prospects for worldwide tuberculosis control under the WHO DOTS strategy. The Lancet. 1998; 352(9144):1886–91. doi: 10.1016/s0140-6736(98)03199-7 - DOI - PubMed
    1. WHO. What is DOTS? A Guide to Understanding the WHO-recommended TB Control Strategy Known as DOTS. World Health Organization; 1999.
    1. WHO. Global Tuberculosis Report. Geneva, Switzerland: World Health Organization; 2014.