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

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          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 I 2 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,I 2;30.5%), older age (HR = 2.13, 95%CI;1.64,2.62,I 2;59.0%,RR = 1.40,95%CI; 1.26, 1.53, I 2; 48.4%) including a 1 year increase in age (HR = 1.01, 95%CI;1.00,1.03,I 2;73.0%), undernutrition (HR = 1.62,95%CI;1.28,1.97,I 2;87.2%, RR = 3.13, 95% CI; 2.17,4.09, I 2;0.0%), presence of any type of co-morbidity (HR = 1.92,95%CI;1.50–2.33,I 2;61.4%, RR = 1.61, 95%CI;1.29, 1.93,I 2;0.0%), having diabetes (HR = 1.74, 95%CI; 1.24,2.24, I 2;37.3%, RR = 1.60, 95%CI;1.13,2.07, I 2;0.0%), HIV co-infection (HR = 2.15, 95%CI;1.69,2.61, I 2; 48.2%, RR = 1.49, 95%CI;1.27,1.72, I 2;19.5%), TB history (HR = 1.30,95%CI;1.06,1.54, I 2;64.6%), previous second-line anti-TB treatment (HR = 2.52, 95% CI;2.15,2.88, I 2;0.0%), being smear positive at the baseline (HR = 1.45, 95%CI;1.14,1.76, I 2;49.2%, RR = 1.58,95%CI;1.46,1.69, I 2;48.7%), having XDR-TB (HR = 2.01, 95%CI;1.50,2.52, I 2;60.8%, RR = 2.44, 95%CI;2.16,2.73,I 2;46.1%), and any type of clinical complication (HR = 2.98, 95%CI; 2.32, 3.64, I 2; 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.

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          Most cited references66

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          The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations.

          The PRISMA statement is a reporting guideline designed to improve the completeness of reporting of systematic reviews and meta-analyses. Authors have used this guideline worldwide to prepare their reviews for publication. In the past, these reports typically compared 2 treatment alternatives. With the evolution of systematic reviews that compare multiple treatments, some of them only indirectly, authors face novel challenges for conducting and reporting their reviews. This extension of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) statement was developed specifically to improve the reporting of systematic reviews incorporating network meta-analyses. A group of experts participated in a systematic review, Delphi survey, and face-to-face discussion and consensus meeting to establish new checklist items for this extension statement. Current PRISMA items were also clarified. A modified, 32-item PRISMA extension checklist was developed to address what the group considered to be immediately relevant to the reporting of network meta-analyses. This document presents the extension and provides examples of good reporting, as well as elaborations regarding the rationale for new checklist items and the modification of previously existing items from the PRISMA statement. It also highlights educational information related to key considerations in the practice of network meta-analysis. The target audience includes authors and readers of network meta-analyses, as well as journal editors and peer reviewers.
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            Funnel plots for detecting bias in meta-analysis: guidelines on choice of axis.

            Asymmetry in funnel plots may indicate publication bias in meta-analysis, but the shape of the plot in the absence of bias depends on the choice of axes. We evaluated standard error, precision (inverse of standard error), variance, inverse of variance, sample size and log sample size (vertical axis) and log odds ratio, log risk ratio and risk difference (horizontal axis). Standard error is likely to be the best choice for the vertical axis: the expected shape in the absence of bias corresponds to a symmetrical funnel, straight lines to indicate 95% confidence intervals can be included and the plot emphasises smaller studies which are more prone to bias. Precision or inverse of variance is useful when comparing meta-analyses of small trials with subsequent large trials. The use of sample size or log sample size is problematic because the expected shape of the plot in the absence of bias is unpredictable. We found similar evidence for asymmetry and between trial variation in a sample of 78 published meta-analyses whether odds ratios or risk ratios were used on the horizontal axis. Different conclusions were reached for risk differences and this was related to increased between-trial variation. We conclude that funnel plots of meta-analyses should generally use standard error as the measure of study size and ratio measures of treatment effect.
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              Interpretation of random effects meta-analyses

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: Software
                Role: MethodologyRole: SoftwareRole: Writing – review & editing
                Role: Investigation
                Role: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                28 June 2021
                2021
                : 16
                : 6
                : e0253848
                Affiliations
                [1 ] Ethiopian Public Health Institute, Addis Ababa, Ethiopia
                [2 ] St Paul’s Hospital Millennium Medical CollegeAddis Ababa, Addis Ababa, Ethiopia
                [3 ] College of Health and Medical Sciences, Harar, Ethiopia
                [4 ] College of Health Science, Debre Markos University, Debre Markos, Ethiopia
                [5 ] School of Public Health, Faculty of Health, University of Technology Sydney, Sydney, Australia
                Ruđer Bošković Institute, CROATIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-5307-8774
                https://orcid.org/0000-0003-2822-2062
                Article
                PONE-D-20-32540
                10.1371/journal.pone.0253848
                8238236
                34181701
                a59b5f62-bf04-4e3d-a454-295b03d7aa79
                © 2021 Alemu et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 29 October 2020
                : 14 June 2021
                Page count
                Figures: 10, Tables: 2, Pages: 24
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Bacterial Diseases
                Tuberculosis
                Extensively Drug-Resistant Tuberculosis
                Medicine and Health Sciences
                Medical Conditions
                Tropical Diseases
                Tuberculosis
                Extensively Drug-Resistant Tuberculosis
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Bacterial Diseases
                Tuberculosis
                Multi-Drug-Resistant Tuberculosis
                Medicine and Health Sciences
                Medical Conditions
                Tropical Diseases
                Tuberculosis
                Multi-Drug-Resistant Tuberculosis
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Bacterial Diseases
                Tuberculosis
                Medicine and Health Sciences
                Medical Conditions
                Tropical Diseases
                Tuberculosis
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Death Rates
                Biology and Life Sciences
                Nutrition
                Malnutrition
                Medicine and Health Sciences
                Nutrition
                Malnutrition
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Medicine and Health Sciences
                Pharmaceutics
                Drug Therapy
                Medicine and Health Sciences
                Diagnostic Medicine
                Tuberculosis Diagnosis and Management
                Custom metadata
                All relevant data are within the paper and its Supporting information files.

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