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. 2021 Jun 28;16(6):e0253848. doi: 10.1371/journal.pone.0253848

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

Ayinalem Alemu 1,*, Zebenay Workneh Bitew 2, Teshager Worku 3, Dinka Fikadu Gamtesa 1, Animut Alebel 4,5
Editor: Ivan Sabol6
PMCID: PMC8238236  PMID: 34181701

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.

Introduction

Tuberculosis (TB) is the top cause of mortality from a single infectious disease [1]. In addition to the low detection rate, poor treatment outcome is becoming a major challenge of TB [2]. The World Health Organization (WHO) identified and introduced a directly observed treatment, short-course (DOTS) strategy to improve the treatment cure rate of TB [3, 4]. The treatment usually takes six to eight months: however, it takes a longer time if drug-resistant tuberculosis (DR-TB) is diagnosed [4]. Drug-resistant tuberculosis is caused by Mycobacterium bacteria that are resistant to at least one first-line anti-TB drug [5]. Nowadays, the emergence of DR-TB has become a major public health challenge globally, notably in resources limited settings, and it is commonly associated with unsuccessful treatment outcomes [6]. When the bacteria become resistant to more anti-TB drugs such as MDR-TB and XDR-TB, the treatment outcome worsens [7]. According to the 2019 WHO estimate, the global treatment success rate of MDR/RR-TB was 56% and XDR-TB was 39% [1].

A high mortality rate was observed among patients with DR-TB globally. Different patient and programmatic related factors are contributing to this high mortality rate [8–13]. Patient-related determinants include demographic characteristics (age and sex), behavioral factors (smoking, alcohol use, and substance addiction), and clinical factors (comorbidities, HIV, undernutrition, anemia, clinical complications, adverse effects, and type of drug resistance). Programmatic management of drug-resistant TB is important to limit TB, prevent the emergence of DR-TB, and have a successful treatment outcome [5]. Though the mortality rate among DR-TB patients is high, it highly varies across countries and settings. Different predictors contribute to this unacceptable high level of mortality. Even though there are previously conducted systematic reviews regarding the poor treatment outcome of DR-TB and its predictors, most of the studies are geographically restricted or restricted to a certain study group [14–16]. For example, our team performed a systematic review and meta-analysis to assess the poor treatment outcome and its predictors among DR-TB patients in Ethiopia. The study estimate revealed that the proportion and incidence density rate of mortality among DR-TB patients in Ethiopia was 15.13% and 9.28/1000 person-months respectively. Besides, the study revealed that the predictors of poor treatment outcome include; older age undernutrition, clinical complications, lower body weight, HIV positivity, anemia, non-HIV comorbidities, treatment delay, and extrapulmonary involvement [14]. However, there is limited information that specifically addressed the predictors of mortality among DR-TB patients at the global level. Thus, our systematic review and meta-analysis study aimed to assess the predictors of mortality among patients with DR-TB based on available studies globally.

Methods

Search strategy and study selection

This systematic review and meta-analysis was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist [17, 18] (S1 Table). We systematically searched five major databases; PubMed/Medline, CINAHL, EMBASE, Scopus, and Web of Science. We also searched Google Scholar and Google for gray literature. The search was conducted from the 5th to the 20th of June 2020. We used the following keywords: Predictors, Indicators, Mortality, Drug-resistant, Tuberculosis. The keywords were searched in combination with the Boolean words AND/OR. The search string applied for the Ovid Embase database was (‘predictors’/exp OR predictors OR ‘indicators’/exp OR indicators) AND (‘mortality’/exp OR mortality) AND ‘drug resistant’ AND (tuberculosis’/exp OR tuberculosis). Two authors (AA1, TW) independently searched articles published in English under the guidance of a senior librarian working at the Ethiopian Public Health Institute and Haramaya University College of Health Science without the time and boundary restrictions (S2 Table). Original studies assessing the predictors of mortality in patients with DR-TB during anti-TB treatment were included. Drug-resistant tuberculosis, defined as when someone is infected with Mycobacterium tuberculosis, which is resistant to at least one first-line anti-TB drug. The laboratory diagnostic methods to rule out DR-TB could be conventional phenotypic drug-susceptibility tests or molecular methods like Xpert MTB/RIF assay and Line Probe Assay (MTBDRplus, MTBDRsl). Excluded were case reports and studies that included a mixed population (both DR-TB and drug-susceptible TB) (Fig 1) (S3 Table).

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 1

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Based on the study questions and inclusion criteria, in the first stage, we screened articles for titles and abstracts. In the second stage, articles were assessed for full-text review. Two authors (ZWB and AA1) independently performed the study eligibility assessment. The inconsistencies were resolved through discussion, and PICOS (participants, interventions, comparison, outcome, and study setting) criteria were used to review the articles. Data were extracted from the included articles by two authors (AA1 and ZWB). The exacted data were; author, publication year, study period, study population, country, study setting, study design, sample size, number of deaths, and total follow-up period (Table 1). Also, we extracted data for different predictors of mortality using crude HR, RR, and OR along with the 95% CI (Table 2). The extracted data were stored in Microsoft Excel 2016.

Table 1. Characteristics of individual studies on the predictors of mortality in patients with drug-resistant tuberculosis, included in the current systematic review and meta-analysis.

Author, Year Study country Study design Study period Study age-group Study setting Sample size Number of deaths Death Quality score
Proportion Incidence density
Bajehson et al., 2019 [10] Nigeria CC 2015–16 All Kano, Katsina and Bauchi states of Nigeria 147 38 25.85% - High
Balabanova et al., 2016 [11] Latvia, Lithuania, Estonia and Bucharest city RC 2007–12 All National TB and Infectious Diseases University Hospital in Vilnius, Clinic of TB and Lung Diseases at Riga East University hospital, Lung Hospital at Tartu University, Estonia and Marius Nasta Institute of Pneumology, Bucharest, Romania. 737 227 30.80% 3.00 per 10,000 days High
Bei et al., 2018 [12] China RC 2013–17 All Changsha Central Hospital, Wuhan Treatment Center, the Third People’s Hospital of Hengyang, and the Second People’s Hospital of Chenzhou 67 20 29.85% 3.51 per 10,000 days High
Bhering et al., 2019 [19] Brazil RC 2000–16 All Tuberculosis Surveillance System in Rio de Janeiro State 2269 1,005 44.29% - High
Brust et al., 2018 [20] South Africa RC 2011–13 All KwaZulu-Natal province 191 24 12.57% - High
Chingonzoh et al., 2018 [13] South Africa RC 2011–13 ≥18 Yrs Registered on the routine DR-TB reporting database in the Eastern Cape Province 3,729 1,445 38.75% - High
Delgado et al., 2015 [21] Peru RC 2000–12 ≥18 Yrs Clinical records of the National Strategy for Prevention and Control of Tuberculosis in Lima 236 44 18.64% - High
Dheda et al., 2010 [22] South Africa RC 2002–08 >16 Yrs Four (Western Cape, Eastern Cape Gauteng Northern Cape) dedicated provincial facilities for the treatment of XDR tuberculosis 174 62 35.63% - High
Fantaw et al., 2018 [23] Ethiopia RC 2013–17 All Adama and Bishoftu General Hospitals 164 30 18.29% 4.75 per 10,000 days High
Farley et al., 2011 [24] South Africa RC 2000–04 ≥18 Yrs Ten participating MDR-TB treatment centers from eight South African provinces 757 177 23.38% - Medium
Gandhi et a., 2012 (MDR-TB) [25] South Africa CC 2005–06 All Tugela Ferry 123 78 63.41% - High
Gandhi et a., 2012 (XDR-TB) [25] 139 111 79.86% - High
Gayoso et al., 2018 [26] Brazil RC 2005–12 All HélioFraga Reference Center (ENSP-FIOCRUZ) 3802 479 12.60% - High
Gebre et al., 2020 [27] Ethiopia RC 2012–17 Adults Dil Chora Referral Hospital, Amir Nur Health Center, and Hailemariam Referral Hospital. 362 55 15.19% 4.14 per 10,000 days High
Getachew et al., 2013 [28] Ethiopia RC 2009–12 All St. Peter’s Specialized Tuberculosis Hospital 188 29 15.43% 3.64 per 10,000 days High
Girum et al, 2017 [8] Ethiopia RC 2013–17 All Yirgalem and Queen Eleni Memorial Hospital 154 13 8.44% 1.91 per 10,000 days High
Janmeja et al., 2018 [29] India RC 2012–14 All Department of Pulmonary Medicine, Government Medical College, and Hospital, Chandigarh. 278 61 21.94% - High
Jeon et al., 2011 [30] South Korea RC 2004 ≥16Yrs National Mokpo Tuberculosis Hospital, Mokpo, National Masan Tuberculosis Hospital, Masan, and Seobuk Hospital, Seoul, Korea 202 127 62.87% - High
Kang et al., 2013 [31] South Korea RC 2000–02 ≥20 Yrs All national TB hospitals (n = 360), all Korean National Tuberculosis Association (KNTA) chest clinics (n = 836) and eight randomly selected university hospitals near Seoul (n = 211). 1,407 470 33.40% - High
Kanwal et al., 2017 [9] Pakistan RC 2010–15 All 11 programmatic management of DR-TB centers in Punjab 1,136 472 41.55% - Medium
Kassa et al., 2020 [32] Ethiopia RC 2010–2017 All University of Gondar, Borumeda, and Debre-Markos Referral Hospital 451 46 10.20% 2.03 per 10,000 days High
Kashongwe et al, 2017 [33] Democratic Republic of Congo RC 2015–17 All Kinshasa TB Referral Hospital 119 18 15.13% - High
Kim et al., 2010 [34] South Korea RC 2000–02 ≥13 Yrs Registry of the Korea National Statistical Office 1407 144 10.23% - High
Kizito et al., 2021 [35] Uganda CC 2016 All National MDR-TB cohort. 198 - - - High
Kurbatova et al., 2012 [36] Estonia, Latvia, Philippine, Russia, Peru RC 2000–04 Adults DOTS-Plus programs 1768 200 11.31% - High
Makhmudova et al., 2019 [37] Tajikistan RC 2012–13 ≥18 Yrs 32 of 37 TB facilities in the selected districts. 601 89 14.81% - High
Manda et al, 2014 [38] South Africa RC 2000–14 All Standardized Programmatic Management of MDR-TB 1619 367 22.67% - High
Milanov et al., 2015 [39] Bulgaria, RC 2009–10 ≥18 Yrs Hospital for Lung Diseases in Gabrovo and the TB registers of the NRL-TB at the NCIPD in Sofia 50 19 38.00% - High
Mitnick et al., 2013 [40] Peru RC 1999–02 All All patients who were enrolled between 1 February 1999 and 31 July 2002 in Lima, Peru, in ambulatory treatment for MDR-TB, 669 139 20.78% - High
Molalign et al., 2015 [41] Ethiopia RC 2011–14 All ALERT and Gondar University Teaching and Referral Hospital 342 37 10.82% 2.33 per 10,000 days High
Mollel et al., 2017 [42] Tanzania RC 2012–14 All Kibong’oto Infectious Diseases Hospital (KIDH) 193 13 6.74% - High
O’Donnell et al., 2013 [43] South Africa RC 2006–2009 ≥18 years Public TB referral hospital in KwaZulu-Natal Province 114 48 42% - High
Olaleye et al., 2016 [44] South Africa RC 2001–10 ≥15 Yrs A specialized TB hospital in Witbank 442 151 34.16% 8.18 per 10,000 day High
Park et al., 2010 [45] South Korea RC 2004 All 21 private hospitals 170 12 7.06% - High
Pradipta et al., 2019 [46] Netherlands RC 2005–15 Adults Nationwide exhaustive registry of tuberculosis patients 103 3 2.91% - High
Prajapati et al., 2017 [47] Gujarat, India PC 2012–16 All except pregnant B. J. Medical College, Civil Hospital 112 58 51.79% - High
Rusisiro et al., 2019 [48] Rwanda RC 2014–17 All Rwanda National Tuberculosis Program: DR-TB excel database. 279 31 11.11% - High
Samali et al, 2017 [49] Tanzania RC 2009–2016 All Kibong’oto hospital 583 89 15.27% 4.80 per 10,000 days High
Schnippel et al., 2015 [50] South Africa RC 2009–11 All Electronic Drug-Resistant Tuberculosis Register by National TB Programme 10,763 2,987 27.75% - High
Seifert et al., 2017 [51] India, Moldova and South Africa PC 2012–13 All Selected hospitals and clinics with a high prevalence of drug-resistant TB in India, Moldova, and South Africa. 834 62 7.43% 3.91 per 10,000 days High
Seung et al., 2009 [52] Lesotho RC 2007–08 All Lesotho national MDR-TB program 76 22 28.95% - High
Shariff et al., 2016 [53] Malaysia RC 2009–13 All Patients receiving treatment at the Institute of Respiratory Medicine in Kuala Lumpur 426 65 15.26% 1.40 per 10,000 days High
Shenoi et al., 2012 [54] South Africa CC 2005–08 All Tugela Ferry 142 73 51.41% - High
Shimbre et al., 2020 [55] Ethiopia RC 2009–16 All Dile Chora, Yirgalem, Queen Eleni Mohamed Memorial and Shene Gibe Hospitals 462 38 8.23% 1.86 per 10,000 days High
Sun et al., 2015 [56] China RC 2001–02 All Henan Province 86 37 43.02% 1.07 per 10,000 days High
Suryawanshi et al., 2017 [57] India RC 2011–12 All PMDT records in Maharashtra state 3410 857 25.13% - High
Wai et al., 2017 [58] Myanmar RC 2015–17 All Community-Based in 33 townships of upper Myanmar 261 26 9.96% 5.50 per 10,000 days High
Wang et al., 2019 [59] China RC 2006–14 All TB management information system 552 - High
Wang et al., 2020 [60] China RC 2006–2011 Adult Wuhan Pulmonary Hospital 356 103 28.93% 10.44 per 10,000 days High
Woya et al., 2019 [61] Ethiopia RC Up to Feb 2018 All Different MDR-TB Hospitals of Amhara Region 207 61 29.47% 3.08 per 10,000 days High
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CC; Case-control, MDR-TB; Multi-Drug Resistant Tuberculosis, PC; Prospective Cohort, RC; Retrospective Cohort, TB; Tuberculosis, XDR-TB; Extensively Drug-resistant tuberculosis.

Table 2. The summary of the pooled estimates of the HR, RR, and OR per predicting factors of mortality in patients with drug-resistant tuberculosis.

Variable HR RR OR
Number of studies Estimate, 95%CI Heterogeneity Number of studies Estimate, 95%CI Heterogeneity Number of studies Estimate, 95%CI Heterogeneity
I2 P-value I2 P-value I2 P-value
Adverse effect 5 0.70(0.44,0.96) 69.3% 0.011 NA NA NA NA NA NA NA NA
Alcohol 8 1.19(0.65,1.73) 60.5% 0.013 3 1.87(0.98,2.76) 73.6% 0.023 4 1.59(0.28,2.91) 43.6% 0.150
Anemia 7 1.79(0.98,2.59) 84.3% <0.001 NA NA NA NA 2 3.56(0.07,7.06) 0.0% 0.841
BMI<18.5 12 1.62(1.28,1.97) 87.2% <0.001 3 3.13(2.17,4.09) 0.0% 0.608 2 2.79(0.31,13.50) 0.0% 0.334
Cavitation 5 1.16(0.88,1.44) 61.6% 0.034 4 1.04(0.72,1.36) 51.3% 0.104 6 0.78(0.57,0.98) 37.7% 0.155
Any comorbidity 19 1.92(1.50,2.35) 61.4% <0.001 6 1.61(1.29,1.93) 0.0% 0.543 6 1.58(1.09,2.06) 0.0% 0.730
Diabetes 9 1.74(1.24,2.24) 37.3% 0.120 2 1.60(1.13, 2.07) 0.0% 0.385 3 0.72(0.40, 1.04) 0.0% 0.722
EPTB involvement 5 1.52(0.96,2.08) 66.0% 0.019 7 0.96(0.47–1.46) 90.7% <0.001 5 0.71(-0.05, 1.48) 44.0% 0.129
HIV co-infection 16 2.15(1.69, 2.61) 48.2% 0.016 12 1.49(1.27, 1.72) 19.5% 0.253 13 1.62(1.41, 1.84) 0.0% 0.549
Male sex 17 1.25(1.08,1.41) 30.5% 0.113 12 0.93(0.88, 0.98) 35.3% 0.108 14 0.76(0.62, 0.90) 13.5% 0.306
Older age 17 2.13(1.64, 2.62) 59.0% 0.001 6 1.40(1.26, 1.53) 48.4% 0.084 10 1.51(0.95, 2.07) 50.3% 0.034
Previous TB history 18 1.30(1.06, 1.54) 64.6% <0.001 6 1.12(0.63, 1.61) 98.3% <0.001 8 1.41(0.43, 2.38) 95.5% <0.001
Previous SLD treatment 5 2.52(2.15,2.88) 0.0% 0.706 2 1.06(-0.05, 2.16) 93.7% <0.001 3 1.38(0.29, 2.46) 94.6% <0.001
Smear positive at baseline 7 1.45(1.14,1.76) 49.2% 0.066 3 1.58(1.46, 1.69) 48.7% 0.142 2 5.33(1.31, 9.36) 0.0% 0.639
Smoking 10 1.14(0.70,1.59) 51.6% 0.029 6 1.29(0.61, 1.97) 81.1% <0.001 6 0.81((0.17, 1.44) 42.6% 0.121
Substance addiction 5 1.44(0.57, 2.32) 80.8% <0.001 NA NA NA NA NA NA NA NA
Treatment delay 2 1.57(-0.39, 3.53) 37.7% 0.205 4 1.12(0.65,1.59) 62.9% 0.044 NA NA NA NA
XDR-TB 7 2.01(1.50, 2.52) 60.8% 0.018 3 2.44(2.16, 2.73) 46.1% 0.156 5 2.21(1.05, 3.37) 51.2% 0.084
Clinical complication 8 2.98(2.32, 3.64) 69.9% 0.002 NA NA NA NA NA NA NA NA
For a one year increase in age 10 1.01(1.00, 1.03) 73.0% <0.001 NA NA NA NA NA NA NA NA
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BMI; Body Mass Index, EPTB; Extra Pulmonary Tuberculosis, HR; Hazards Ratio, NA; Not applicable, OR; Odd’s Ratio, RR; Risk Ratio, SLD; Second-line Drugs, TB; Tuberculosis, XDR-TB; Extensively Drug-resistant tuberculosis.

PICOS criteria

Participants: Patients with drug-resistant tuberculosis.

Interventions: Anti-tuberculosis treatment.

Comparators: Alive in the treatment period.

Outcomes: Death from any cause during the treatment period among DR-TB patients.

Study type: Cohort and case-control studies.

Study setting: Any country in the globe.

Quality assessment

We evaluated the quality of eligible articles using the Joanna Briggs Institute Critical Appraisal (JBI) tools designed for case-control and cohort studies [62]. The cohort checklist consists of 11 indicators and the case-control checklist consists of 10 indicators. These indicators were turned into 100% and the quality score was graded as high if >80%, a medium between 60–80%, and low <60%. Two authors (AA1 and DFG) conducted the quality assessment, and the third author TW managed the inconsistencies (S4 Table).

Outcomes

Mortality from any cause in patients with DR-TB during their anti-TB treatment course was the primary outcome. Predictors of mortality were the second outcome. The pooled HR, RR, and OR along with their 95% CIs were estimated to assess these predictors of mortality in patients with DR-TB.

Data analysis

Data extracted in Microsoft excel 2016 were imported into STATA Version 15 for analysis. We estimated the proportion, incidence, and predictors of mortality in patients with DR-TB. The proportion was estimated by dividing the number of deaths by the total sample size, while the incidence rate was described per 10,000 person-days of follow-up. To assess the predictors, the pooled HR, RR, and OR with 95% CI were estimated by assuming the true effect size varies between studies. For studies that did not present the measures of association, we analyzed the estimates along with 95% CI. We presented the meta-analysis results using a forest plot. Also, we assessed the heterogeneity among the studies using the forest plot and I2 heterogeneity test [63]. We used a fixed-effects model for I2< 50% and a random-effects model for I2>50% to perform the analysis [64]. Besides, publication bias was explored using visual inspection of the funnel plot, and Egger’s regression test was carried out to check the statistical symmetry of the funnel plot.

Role of the funding source

No fund was obtained to execute this systematic review and meta-analysis.

Results

Study characteristics

From the whole search, we assessed 640 articles for eligibility. After 203 studies were removed by duplication, 437 articles were screened by title and abstract. Then, 351 articles were excluded and full-text screening was conducted on 86. Accordingly, 41 studies were excluded from the study due to mixed study groups (12), overlapped studies (10), did not have specific outcomes (9), and incomplete records (9). Therefore, in this systematic review and meta-analysis, 49 studies [8–13, 19–61] were included in the final analysis (Fig 1). These 49 individual studies were conducted in 25 different countries located in four continents (Africa, Asia, Europe, and South America). More than half (25, 51%) of the included studies were from Africa. The remaining 15 studies were from Asia; four from South America, three from Europe, and two from multi-center studies. South Africa (11 studies) and Ethiopia (8 studies) contributed to the large proportion of individual studies included in the current study followed by India (4 studies) and South Korea (4 studies). The majority of the primary studies were based on data collected from patients enrolled in hospitals for treatment. Data were collected either directly from patient registries or the health information system (national or regional database) or the prospective cohort research project database. Most (45, 91.8%) of the studies used a retrospective cohort study design: however, some studies also used either a case-control or prospective cohort study design. The study period ranged from 1999 to 2017. Besides, most of the studies were conducted on all age groups (Table 1). Based on the results found through the JBI quality assessment tool, the indicators were turned in to 100% and graded as high if >80%, medium between 60–80%, and low <60%. Accordingly, the majority of the studies (47 out of 49) were graded to have high quality, and only two studies were categorized under medium quality (S4 Table).

Proportion and incidence of death

The smallest sample size was 67 in a study done by Bei et al., 2018 [12], while the largest sample size was 10,763 in a study done by Schnippel et al., 2015 [50]. We estimated the pooled proportion of mortality and the incidence of mortality in patients with DR-TB based on 48 study results and 17 studies respectively. The proportion of death ranged from 6.74% [42] to 79.86% [25], while the incidence of mortality ranged from 1.07 per 10,000 person-days [56] to 10.44 per 10,000 person-days [60]. Based on the random-effects model, the proportion of death and the incidence of mortality in patients with DR-TB during their treatment follow-up period were 25.62% (95%CI; 20.91, 30.33, I2; 99.31%) (Fig 2), and 3.75 per 10,000 person-days (95%CI; 2.65, 4.86, I2; 97.61%), respectively (Fig 3). We evaluated the publication bias using the visual inspection of the funnel plot and Egger’s test. Accordingly, the funnel plot revealed that there was no publication bias, and the symmetry of the funnel plot was confirmed by a non-significant Egger’s test result (death incidence, p = 0.465) (Fig 4), (death incidence density rate, p = 0.051) (Fig 5). The sensitivity analysis was done for the pooled incidence of death, and it was found that no single study affected the pooled death incidence. In this study, we separately analyzed the mortality incidence for MDR-TB patients and XDR-TB patients. Accordingly, the proportion of death in patients with MDR-TB and XDR-TB during their treatment follow-up period was 20.21% (95%CI; 16.45, 23.97, I2; 98.76%) (Fig 6), and 43.53% (95%CI; 35.08, 51.97, I2; 96.29%) respectively (Fig 7). Besides, the funnel plot revealed that there was no publication bias both for MDR-TB (Fig 8) and for XDR-TB (Fig 9).

Fig 2. Forest plot for pooled incidence of mortality in patients with drug-resistant tuberculosis.

Fig 2

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Fig 3. Forest plot for pooled incidence density rate of mortality in patients with drug-resistant tuberculosis.

Fig 3

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Fig 4. Funnel plot showing publication bias among studies used to compute the incidence of mortality in patients with drug-resistant tuberculosis.

Fig 4

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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 5

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Fig 6. Forest plot for pooled incidence of mortality in patients with multi drug-resistant tuberculosis.

Fig 6

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Fig 7. Forest plot for pooled incidence of mortality in patients with extensively drug-resistant tuberculosis.

Fig 7

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Fig 8. Funnel plot showing publication bias among studies used to compute the incidence of mortality in patients with multi drug-resistant tuberculosis.

Fig 8

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Fig 9. Funnel plot showing publication bias among studies used to compute the incidence of mortality in patients with extensively drug-resistant tuberculosis.

Fig 9

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Predictors of mortality

We assessed the pooled estimate for different predictors. The predictors included demographic (sex and age), behavioral (alcohol use, smoking and substance addiction) and clinical (adverse effect, anemia, undernutrition, comorbidities, diabetes, EPTB involvement, HIV sero-status, cavitation, previous TB history, previous SLD treatment, clinical complication, treatment delay, smear positive TB and drug resistance pattern) characteristics. Based on the pooled analysis of the hazards ratio and risk ratio, under nutrition (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%), male sex (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 (HR = 1.01, 95%CI;1.00,1.03,I2;73.0%), previous 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, 46.1%), and any type of clinical complication (HR = 2.98,95%CI; 2.32, 3.64, I2; 69.9%) were the predictors of mortality in patients with DR-TB (Fig 10). Also, our pooled analysis of the odds ratio showed that any cause of mortality in patients with DR-TB is associated with the presence of any type of comorbidity (OR = 1.58, 95%CI;1.09,2.06, I2; 0.0%), HIV co-infection (OR = 1.62, 95%CI;1.41, 1.84, I2; 0.0%), being smear positive at the baseline (OR = 5.33, 95%CI;1.31, 9.36 I2; 0.0%), and having XDR-TB (OR = 2.21, 95%CI = 1.05, 3.37, I2; 51.2%) (Table 2).

Fig 10. Forest plot for predictors of mortality in patients with drug-resistant tuberculosis.

Fig 10

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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.

However, statistically significant differences or associations was not observed for alcohol consumption (HR = 1.19, 95% CI; 0.65,1.73, I2; 60.5%RR = 1.87,95%CI;0.98,2.76, I2; 73.6%), presence of any grade of anemia (HR = 1.79,95%CI = 0.98,2.59, I2;84.3%), presence of cavitation (HR = 1.16%CI; 0.88–1.44, I2;61.6%, RR = 1.04, 95%CI;0.72,1.36, I2; 51.3%), extrapulmonary involvement (HR = 1.52, 95%CI;0.96,2.08, I2;66.0%,RR = 0.96, 95%CI;0.47–1.46, I2; 90.7%), smoking (HR = 1.14, 95%CI; 0.70,1.59, I2;51.6%, RR = 1.29, 95% CI;0.61, 1.97, I2; 81.1%), addiction to substances (HR = 1.44, 95%CI;0.57, 2.32, I2; 80.8%), and treatment delay (HR = 1.57, 95%CI;-0.39, 3.53, I2; 37.7%, RR = 1.12, 95%CI; 0.65,1.59, I2; 62.9%) (Table 2).

Predictors of mortality per drug-resistant tuberculosis categories

In the current study, we performed a sub-group analysis to assess the predictors of mortality based on the drug-resistance category of TB. As per the presentation in the studies included in this study, the resistance category is classified as; 1) RR/MDR 2) mixed MDR and XDR 3) XDR 4) DR-TB. However, the fourth category (DR-TB) is not specified due to the mix-up of different resistance included in the individual studies. Thus, we presented based on the data available in the specific studies; A) DR-TB; Poly DR, RR, MDR, XDR B) DR-TB; Poly DR, MDR, XDR C) DR-TB; RIF/INH-mono resistant, MDR, XDR D) DR-TB; mono resistance, poly resistance, MDR E) DR-TB; mono resistance, poly resistance, MDR, XDR.

Specific to the type of DR-TB, the predictors of mortality among RR/MDR patients includes; older age (HR = 1.72, 95%CI;1.15, 2.29, I2; 31.5%), one year increase in age (HR = 1.01, 95%CI;1.00, 1.02, I2; 73.7%), presence of any type of co-morbidity (HR = 2.39, 95%CI;1.57, 3.21, I2; 72.2%), having diabetes (HR = 2.05, 95%CI;1.40,2.70, I2;0.00%), HIV co-infection (HR = 2.35, 95%CI;1.68,2.82, I2;24.1%), previous TB history (HR = 1.46, 95%CI;1.19, 1.72, I2;0.00%), being smear-positive at the baseline (HR = 3.05, 95%CI; 2.17, 4.29), and any type of clinical complication (HR = 2.80, 95%CI; 1.86, 3.74, I2; 62.1%). While, the predictors of mortality among mixed MDR and XDR-TB patients includes; being male (HR = 1.52, 95%CI;1.30, 1.73, I2; 0.00%), older age (HR = 2.39, 95%CI;1.75, 3.03, I2; 61.3%), one year increase in age (HR = 1.02, 95%CI;1.01, 1.04, I2; 0.00%), undernutrition (HR = 2.33, 95%CI;1.19, 3.47, I2; 94.1%), presence of any type of co-morbidity (HR = 1.87, 95%CI;1.36, 2.37, I2; 33.8%), previous second-line anti-TB treatment (HR = 2.50, 95%CI;2.13,2.87, I2;0.00%), and being smear-positive at the baseline (HR = 1.35, 95%CI; 1.16, 1.53, I2; 0.00%). Among XDR-TB patients the predictors of mortality were; older age (HR = 2.82, 95%CI; 1.08, 7.35, single study), undernutrition (HR = 4.30, 95%CI; 1.26, 14.72), presence of any type of co-morbidity (HR = 5.16, 95%CI; 2.05, 13.00), and previous second-line anti-TB treatment (HR = 3.73, 95%CI; 1.69, 8.22). Besides, among DR-TB patients with a mix of different resistance categories, the predictors of mortality were as follows. Among DR-TB patients with a mix of mono drug-resistant, poly drug-resistant, and MDR-TB, older age is a predictor of mortality (HR = 5.29, 95%CI; 1.02, 27.29). While among DR-TB patients with a mix of RIF/INH-mono drug-resistant, MDR, and XDR-TB, a one-year increase in age (HR = 1.02, 95%CI; 1.01, 2.20), and undernutrition (HR = 1.96, 95%CI; 1.15, 3.35) were the predictors of mortality. The presence of any type of co-morbidity is a predictor of mortality among DR-TB patients with a mix of poly drug-resistant, RR, MDR, and XDR-TB (HR = 1.34, 95%CI; 1.03, 1.74), and among DR-TB patients with a mix of mono drug-resistant, poly drug-resistant, and MDR-TB (HR = 8.44, 95%CI; 3.04, 23.44) (Fig 10).

Discussion

In this systematic review and meta-analysis, we analyzed the pooled data to assess the predictors of mortality in patients with DR-TB based on studies conducted in different countries and settings at the global level. The case definition for drug-resistant TB in this study was according to the WHO definition such that any TB case caused by Mycobacterium tuberculosis resistant to at least one anti-TB drug. Based on the pooled estimates, the predictors of mortality include: male sex, older age, undernutrition, HIV co-infection, presence of any type of co-morbidity, having diabetes, any type of clinical complication, previous TB history, previous second-line anti-TB treatment, smear-positive at the baseline, and having XDR-TB.

The results of this study revealed that patient demographic characteristics such as male sex and older age are important predictors of mortality. Likewise, this meta-analysis revealed that male patients are more likely to die in the early TB treatment as compared to female patients. A previous study also confirmed that TB disapropriately affects males than females [65]. This could be possibly due to different factors. Males are more likely to practice smoking and alcohol drinking that might worsen the treatment outcome [66]. Additionally, evidence suggested that men are more likely to default from TB treatment, which might result in poor treatment outcomes [67]. The other demographic factor significantly associated with mortality is the age group. Oder age is operationalized in the current study as the highest age category in the individual studies and most of the studies it is above 60 years. Our pooled analysis revealed that older individuals are at a higher risk of death. As age increases in one unit, the incidence of death increases by 1%. The impaired immune status in this age group could be one factor, and older people are more likely to have other comorbidities/chronic illnesses/ that might increase the risk of mortality [68].

Another finding of this study revealed that clinical factors or patient conditions are important predictors of mortality. Those patients with any type of comorbidities were at a higher risk of death. The hazard of death in DR-TB patients with any type of comorbidities was two times as compared to their counterparts. The risk of death in comorbid DR-TB patients was 1.61 times compared with their counterparts. Among the co-morbidities, we generated a pooled estimate of DM and HIV co-infections. The risk of death increased by 92% among HIV-positive patients with DR-TB. Also, the risk of death in DM co-infected DR-TB patients was increased by 74%. Collaborative efforts are needed to decrease the impact of this synergy. Besides, the presence of any type of clinical complication is associated with mortality. The prognosis of DR-TB patients who developed clinical complications in their follow-up period was poor. Along with these predicting factors, undernutrition (BMI<18.5 kg/m2) is one predicting factor. The risk of death in undernourished DR-TB patients was 3.13 times. Undernutrition is associated with drug toxicity that can contribute to default and could finally result in death, as described in previous studies [69–72].

The result of the current study also revealed that patients with smear-positive DR-TB at the baseline had a higher risk of death. The hazard of death among patients with smear-positive DR-TB at the baseline was 1.45 times higher as compared to smear-negative DR-TB patients. Smear-positive patients have a higher bacterial load in their sputum that reflects the infectiousness and severity of the diseases that might be associated with mortality. Also, history of TB infection and history of treatment with second-line anti-TB drugs increased the risk of death. Besides, the risk of death doubled in patients with XDR-TB. This could be due to the toxic effects of the drugs [7].

In the current study, we also performed a sub-group analysis to assess the predictors of mortality across different resistance categories; 1) RR/MDR 2) mixed MDR and XDR 3) XDR 4) DR-TB. Among RR/MDR-TB patients, older age, a one-year increase in age, presence of any type of co-morbidity, having diabetes, HIV co-infection, previous TB history, being smear-positive at the baseline, and any type of clinical complications were estimated to be predictors of mortality. In the second category that is on studies that reported mortality among mixed MDR and XDR cases, the predictors of mortality include being male, older age, one year increase in age, undernutrition, presence of any type of co-morbidity, previous second-line anti-TB treatment, and smear-positive at the baseline. The predictors of mortality among XDR-TB patients include; older age, undernutrition, presence of any type of co-morbidity, and previous second-line anti-TB treatment. The fourth category is among different combinations of DR-TB including mono-resistance, poly-resistance, MDR and XDR-TB, older age, a one-year increase in age, undernutrition, and presence of any co-morbidity. Some predictors of mortality are specific to a certain group. For example, DM co-infection, HIV co-infection, and clinical complication were found to be predictors of mortality in the first group (RR/MDR-TB) and male sex is a predictor of mortality only in the second category (mixed MDR-TB and XDR-TB). Also, being smear-positive at the baseline, and a one-year increase in age were the predictors of mortality in the first (RR/MDR-TB), and second (mixed MDR-TB and XDR-TB) categories. Previous treatment with second-line anti-TB treatment is a predictor in the second (mixed MDR-TB and XDR-TB) and third XDR-TB) categories. Thus, considering the risk factors of mortality to each drug-resistance category during anti-TB treatment would help to improve the treatment outcome. Generally, the common predictors of mortality among different drug-resistance categories identified based on the pooled estimates in the current study include; older age, presence of any type of co-morbidity, and undernutrition. Therefore, the elders need special attention during DR-TB management. Also, supportive intervention such as nutritional supplementations to DR-TB patients would improve the treatment outcome. Besides, it would be important to give special attention to DR-TB patients with underlying co-morbidities to improve the treatment outcome.

In the end, in the current study, the quality assessment revealed that the majority of the studies (44 out of 46) were graded to have high quality, and only two studies were categorized under medium quality. Also, the sensitivity analysis revealed that no single study affected the pooled incidence of mortality in drug-resistant TB patients. This implies that the quality of the studies might not affect the results in the current systematic review and meta-analysis.

Limitation of the study

Finally, this study has some limitations. First, this study was based on studies published only in the English language. Besides, the risk factors were not separately assessed based on the place where the individual studies were conducted.

Conclusion

In conclusion, the findings of this study revealed that different patient-related factors increased early mortality in patients with DR-TB. The presence of different co-morbidities and developing clinical complications worsen the treatment outcome in addition to the gender and age differences. Special considerations and personalized treatment and follow-up of patients with other co-morbidities, the elder ones, those who develop clinical complications, and those with previous anti-TB treatments could be essential to have a good prognosis.

Supporting information

S1 Table. Completed PRISMA 2009 checklist.

(DOC)

Click here for additional data file. (64KB, doc)
S2 Table. Search engines.

(DOCX)

Click here for additional data file. (27.7KB, docx)
S3 Table. Inclusion and exclusion criteria for selection of studies.

(DOCX)

Click here for additional data file. (13.1KB, docx)
S4 Table. Quality assessment for the included studies in meta-analysis.

(DOCX)

Click here for additional data file. (56.6KB, docx)

Acknowledgments

We acknowledge the authors of the primary studies. Our acknowledgment also goes to the Ethiopian Public Health Institute and Haramaya University College of Health Science for non-financial supports including access to internet searching.

Data Availability

All relevant data are within the paper and its Supporting information files.

Funding Statement

The author(s) received no specific funding for this work.

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Decision Letter 0

Ivan Sabol

9 Feb 2021

PONE-D-20-32540

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

PLOS ONE

Dear Dr. Alemu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Additional Editor Comments:

The mortality was associated with “older age” separately from “1 year increase in age” suggesting “older age” is a category. However “older age” is not defined?

Table 2 lists “for one year increase” at the last row of the table. While this implies age in the context of the rest of the text, it should be revised so that the table is understandable on its own.

Some sorting of the Table 1 /Figure 2 would make the data more easy to read. For example if the studies were listed in alphabetical order, date of publication or something similar allowing easier cross referencing between table and figure.

While not an expert on meta analysis, it is strange that Figure 2 has a note mentioning weights, while no weighting information is shown. Figure 3 has the same note but also the “% weight“ column.

In the combined PDF document there are 2 versions of the manuscript, one version from page 5 to page 23. And another version from page 24 to page 39 (?). Some figures are repeated (Fig 1 at page 40 and 45. More importantly there are 2 different figures labelled Figure 4 (one at page 44 and one at page 48). One version of the manuscript specifically mentions figures 1-4 but doesn’t mention figures 5-7. Which manuscript is final?

Figure 4= Figure 7 subpanels should be made more uniform

Figure 6 is difficult to read.

Typos and trivial

The manuscript should be thoroughly proofread since it contains many typographical spacing errors starting at Line 4 with authors names

The word inconvenience(s) as used at lines 94, 113 should probably be replaced by inconsistencies

Line 441 – 442 font sizes are different

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: I Don't Know

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In this systematic review, authors aimed to determine the predictors of mortality in patients with drug-resistant tuberculosis.

The major limitation of this analysis is that it does not distinguish between different types of drug resistance. They analyzed all patients with „resistance to any TB drug“ which means that patients with i.e. isoniazid- or ethambutol or PZA- monoresistance are mixed with patients with MDR and even XDR TB. Given that these are very different populations of patients with very different outcomes (according to all knowledge from the literature), it does not make sense to mix them together in order to try to have some conclusions that consequentially cannot be applied for neither on of those subgroups among the hat of „drug resistance“. Accordingly, the calculated proportion of death is 25.35, but ranges from 6.74% to 79.86%, which is, most probably, a reflection of very different populations in studied cohorts. I would suggest to differentiate patients populations and focus on a subgroup (i.e. monoresistance, MDRTB or XDR TB) in order to have information that could be useful or applied in real life.

Other remarks include many typo errors and a general need to improve the language of the manuscript as well as duplication of the figures in the uploaded file.

Reviewer #2: Dear authors - I appreciate the opportunity to read your manuscript. I was asked to review the methods, so I will restrict my comments to that section. I have a few comments.

1. The databases searched are suitable. Please include the platforms (e.g. Ovid Embase or Elsevier Embase, etc)

2. I appreciate the attempt to search grey literature.

3. Thank you for reporting the search date. It's from June 2020. Consider an update to make the search more current.

4. I appreciate the inclusion of an additional file with the searches. Rather than include screenshots of the database, please include just the searches in the document. The search terms in PubMed are cut off, for example.

5. Each search would benefit with explicit use of keywords and controlled terms. For example, the common structure would be something like this:

#1 'drug resistant tuberculosis'/exp OR 'drug resistant tuberculosis':ti,ab

#2 'mortality'/exp OR mortalit*:ti,ab OR death*:ti,ab

#3 predict*:ti,ab OR indicat*:ti,ab

#4 #1 AND #2 AND #3

This is for Elsevier Embase. Each one of these search lines could be expanded further with synonyms and other variations. But this brings in several times the results of the current search reported, and I don't think they'd all be too far afield. So, I recommend broadening the searches and reporting them more accessibly in the 'additional file'.

Reviewer #3: The reviewer congratulates the authors on this submission. Below are some comments for consideration.

Existing literature:

- There are a number of previous systematic reviews looking at outcomes of drug-resistant TB, one even written by the same team as the current review (Alemu et al 2020, Poor treatment outcome and its predictors among drug-resistant tuberculosis patients in Ethiopia: A systematic review and meta-analysis.) It would be important to emphasize the value added by the new work - what is different about the current review? (If it is just that the other review focused on treatment outcomes generally, and this one is focusing on mortality, then this should be stated).

Introduction:

- Introduction is somewhat brief, could expand upon specific prior work and findings regarding risk factors for mortality among DR TB patients, and why previous systematic reviews have been insufficient / how your review adds value.

Methods:

- Specific table of inclusion/exclusion criteria would be helpful (e.g. age range, locations of studies, etc)

- Clarify PICOS elements, e.g. for DR TB, state diagnostic criteria that participants need to fulfill in order to be included (case definition is mentioned in discussion, but should be moved up to be mentioned earlier, in methods). For outcome (mortality), state whether this was defined as death from any cause during the treatment period, or whether it was DR TB specific mortality

- Justification of using both fixed and random effects MA? Why not use random effects for all analyses given that the studies are done in different populations?

Results:

- Quality assessment results not described in results section, need to add this. In addition to description, would be good to add a visual representation, e.g. summarizing the information in Supplementary file 3.

Minor comments:

- line 133: start by mentioning total number of records returned.

Discussion:

- Need to add discussion of study quality and how this may have affected results.

Tables and Figues:

- Table 1: Not sure if this is ordered in any way but if not, could order by year, country, or author's last name.

- Figure 1: Recommend to use original PRISMA version of screening flowchart. For studies excluded at full text review, reasons for exclusion need to be stated (e.g. case reports, n=X, etc). (It seems this has now been included in revised version).

- Figure 6 - not possible to see study author names/years, need higher quality image.

- Figure 7 estimates difficult to see, suggest larger/higher quality images.

- Additional file 2: please display specific search terms in a table rather than as a screenshot of databases. The search terms are sometimes cut-off in the screenshots, so not clear to see.

General comments:

- It is mentioned that all data is contained in the manuscript and supplementary files - will code be shared as well?

- in line 31, replace "electronic five major" with "five major electronic"

- other minor grammar errors throughout, please verify.

- line 113: inconvenience? unclear. Maybe meant to say inconsistencies / discrepancies between reviewers?

**********

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Reviewer #1: No

Reviewer #2: Yes: Mark MacEachern

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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PLoS One. 2021 Jun 28;16(6):e0253848. doi: 10.1371/journal.pone.0253848.r002

Author response to Decision Letter 0

27 Feb 2021

Revisions based on the Editor’s and the reviewers’ comments and suggestions

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

Editor Comments and suggestions

1. Critically, only a single manuscript version should be included in the final text.

Thank you for the valuable comment. The problem occurred due to the second manuscript submitted after the technical check comments without removing the first submission. The current revised manuscript is a single version.

2. Please take extra care to address all issues relating to the methodology

Thank you for the comment. We revised the manuscript as per the comments and suggestions that were given by the editor and the reviewers.

3. Additionally, thorough proofreading of the text is necessary

Thank you for the constructive comment. The current manuscript is revised, and proofread by someone with better English language skills.

Additional Editor Comments:

1. The mortality was associated with “older age” separately from “1 year increase in age” suggesting “older age” is a category. However “older age” is not defined?

Thank you for the valuable comment. We operationalized “older age” in the revised manuscript as; the highest age category in the individual studies and most of the studies, it is above 60 years.

2. Table 2 lists “for one year increase” at the last row of the table. While this implies age in the context of the rest of the text, it should be revised so that the table is understandable on its own.

Thank you for the comment, now revised accordingly as a one-year increase in age.

3. Some sorting of the Table 1 /Figure 2 would make the data more easy to read. For example if the studies were listed in alphabetical order, date of publication or something similar allowing easier cross referencing between table and figure.

Thank you for the comment and suggestion. In the revised manuscript, the studies in Table 1 are listed in alphabetical order.

4. While not an expert on meta analysis, it is strange that Figure 2 has a note mentioning weights, while no weighting information is shown. Figure 3 has the same note but also the “% weight“ column.

Thank you for the valuable comment. In the revised submission, all figures have weighting information

5. In the combined PDF document there are 2 versions of the manuscript, one version from page 5 to page 23. And another version from page 24 to page 39 (?). Some figures are repeated (Fig 1 at page 40 and 45. More importantly there are 2 different figures labelled Figure 4 (one at page 44 and one at page 48). One version of the manuscript specifically mentions figures 1-4 but doesn’t mention figures 5-7. Which manuscript is final?

Figure 4= Figure 7 subpanels should be made more uniform

Figure 6 is difficult to read.

Thank you for the valuable comment; this was due to the mix-up of two uploaded manuscripts. The current manuscript is revised accordingly. Since figure 6 is not readable due to a large number of studies, we removed it from the revised manuscript. However, we captured information. The number of figures in the revised manuscript is revised due to the suggestion of Reviewer 1 to perform a separate analysis based on the drug-resistance pattern.

6. Typos and trivial

Thank you for the valuable comment. The entire manuscript is checked for typos and proofread by someone with better English language skills.

7. The manuscript should be thoroughly proofread since it contains many typographical spacing errors starting at Line 4 with authors names

Thank you for the comment. It is corrected.

8. The word inconvenience(s) as used at lines 94, 113 should probably be replaced by inconsistencies

Thank you for the suggestion. We replaced it.

9. Line 441 – 442 font sizes are different.

Thank you for the comment. We corrected it.

Reviewer 1

Reviewer #1: In this systematic review, authors aimed to determine the predictors of mortality in patients with drug-resistant tuberculosis.

1. The major limitation of this analysis is that it does not distinguish between different types of drug resistance. They analyzed all patients with „resistance to any TB drug“ which means that patients with i.e. isoniazid- or ethambutol or PZA- monoresistance are mixed with patients with MDR and even XDR TB. Given that these are very different populations of patients with very different outcomes (according to all knowledge from the literature), it does not make sense to mix them together in order to try to have some conclusions that consequentially cannot be applied for neither on of those subgroups among the hat of „drug resistance“. Accordingly, the calculated proportion of death is 25.35, but ranges from 6.74% to 79.86%, which is, most probably, a reflection of very different populations in studied cohorts. I would suggest to differentiate patients populations and focus on a subgroup (i.e. monoresistance, MDRTB or XDR TB) in order to have information that could be useful or applied in real life.

Thank you for your valuable comment and suggestion. In the revised manuscript, we analyzed the pooled estimate of mortality for MDR-TB and XDR-TB separately.

2. Other remarks include many typo errors and a general need to improve the language of the manuscript as well as duplication of the figures in the uploaded file.

Thank you for the pertinent comment. The current version is checked by someone with better English language skills, and the duplicate figures are removed. The duplicates were due to uploading the revised manuscript after a technical check without removing the first manuscript.

Reviewer 2

Reviewer #2: Dear authors - I appreciate the opportunity to read your manuscript. I was asked to review the methods, so I will restrict my comments to that section. I have a few comments.

1. The databases searched are suitable. Please include the platforms (e.g. Ovid Embase or Elsevier Embase, etc)

Thank you for the valuable comment. We included the Ovid Embase platform in the revised manuscript.

The search string applied for the Ovid Embase database was (‘predictors’/exp OR predictors OR ‘indicators’/exp OR indicators) AND (‘mortality’/exp OR mortality) AND ‘drug resistant’ AND (tuberculosis’/exp OR tuberculosis).

2. I appreciate the attempt to search grey literature.

3. Thank you for reporting the search date. It's from June 2020. Consider an update to make the search more current.

Thank you for the valuable comment. We tried to find newly published articles but we could not find any relevant articles for the current study.

4. I appreciate the inclusion of an additional file with the searches. Rather than include screenshots of the database, please include just the searches in the document. The search terms in PubMed are cut off, for example.

Thank you for the comment. We uploaded the full search text of the PubMed search and included it in the additional file 2.

5. Each search would benefit with explicit use of keywords and controlled terms. For example, the common structure would be something like this:

#1 'drug resistant tuberculosis'/exp OR 'drug resistant tuberculosis':ti,ab

#2 'mortality'/exp OR mortalit*:ti,ab OR death*:ti,ab

#3 predict*:ti,ab OR indicat*:ti,ab

#4 #1 AND #2 AND #3

Thank you for the valuable comment. We included the keywords and controlled terms in the searching strategy part of the methods section.

6. This is for Elsevier Embase. Each one of these search lines could be expanded further with synonyms and other variations. But this brings in several times the results of the current search reported, and I don't think they'd all be too far afield. So, I recommend broadening the searches and reporting them more accessibly in the 'additional file'.

Thank you for the valuable comment. The search string we used for the Elsevier Embase was (Predictors OR indicators AND mortality AND drug-resistant AND tuberculosis). We tried to broaden the searches based on your recommendation, but we could not get additional relevant articles for the current study.

Reviewer 3

Reviewer #3: The reviewer congratulates the authors on this submission. Below are some comments for consideration.

1. Existing literature:

There are a number of previous systematic reviews looking at outcomes of drug-resistant TB, one even written by the same team as the current review (Alemu et al 2020, Poor treatment outcome and its predictors among drug-resistant tuberculosis patients in Ethiopia: A systematic review and meta-analysis.) It would be important to emphasize the value added by the new work - what is different about the current review? (If it is just that the other review focused on treatment outcomes generally, and this one is focusing on mortality, then this should be stated).

Thank you for the valuable comment. We included the justification in the current manuscript as per the suggestion.

2. Introduction:

- Introduction is somewhat brief, could expand upon specific prior work and findings regarding risk factors for mortality among DR TB patients, and why previous systematic reviews have been insufficient / how your review adds value.

Thank you for the comment. We revised it as follows. Even though there are previously conducted systematic reviews regarding the poor treatment outcome of DR-TB and its predictors, most of the studies are geographically restricted. However, there is limited information that specifically addressed the predictors of mortality among DR-TB patients at the global level.

3. Methods:

- Specific table of inclusion/exclusion criteria would be helpful (e.g. age range, locations of studies, etc)

Thank you for the comment and suggestion. The details of the individual studies (study country, setting, period, age-group, study design, etc) included in the study are described in Table 1. Figure 1 addressed the inclusion and exclusion criteria. In this study, there is no place and time restriction with the limitation of including only those studies published in English

- Clarify PICOS elements, e.g. for DR TB, state diagnostic criteria that participants need to fulfill in order to be included (case definition is mentioned in discussion, but should be moved up to be mentioned earlier, in methods). For outcome (mortality), state whether this was defined as death from any cause during the treatment period, or whether it was DR TB specific mortality.

Thank you for the pertinent comment. We revised it and the diagnostic criteria we used is included. “Drug-resistant tuberculosis, defined when someone is infected with Mycobacterium tuberculosis, which is resistant to at least one first-line anti-TB drug…already found in the methods part”. “The laboratory diagnostic methods to rule out DR-TB could be conventional phenotypic drug-susceptibility test or molecular methods like Xpert MTB/RIF assay and Line Probe Assay (MTBDRplus, MTBDRsl)”.

- Justification of using both fixed and random effects MA? Why not use random effects for all analyses given that the studies are done in different populations?

Thank you for the valid comment. We planned to perform a fixed or random effect model based on the heterogeneity, unfortunately, since the studies are from different populations all the pooled estimates were based on the random effect model.

4. Results:

- Quality assessment results not described in results section, need to add this. In addition to description, would be good to add a visual representation, e.g. summarizing the information in Supplementary file 3.

Thank you for the comment. We revised as per the suggestion. Based on the results found through the JBI quality assessment tool, the indicators were turned in to 100% and graded as high if >80%, medium between 60–80%, and low <60%. Accordingly, the majority of the studies (44 out of 46) graded to have high quality, and only two studies were categorized under medium quality.

5. Minor comments:

- line 133: start by mentioning total number of records returned.

Thank you, we revised it.

“From the whole search, we assessed 640 articles for eligibility. After 203 studies were removed by duplication, 437 articles were screened by title and abstract. Then, 351 articles were excluded and full-text screening was conducted on 86. Accordingly, 41 studies were excluded from the study due to mixed study groups (12), overlapped studies (10), did not have the specific outcome (9) and incomplete records (9)”.

6. Discussion:

- Need to add discussion of study quality and how this may have affected results.

Thank you for the valuable comment; we included it in the revised manuscript.

7. Tables and Figues:

- Table 1: Not sure if this is ordered in any way but if not, could order by year, country, or author's last name.

Thank you for the valuable comment and suggestion, now it is alphabetically ordered using the Authors name.

- Figure 1: Recommend to use original PRISMA version of screening flowchart. For studies excluded at full text review, reasons for exclusion need to be stated (e.g. case reports, n=X, etc). (It seems this has now been included in revised version).

Thank you for the comment. We already corrected it during the technical check.

- Figure 6 - not possible to see study author names/years, need higher quality image.

Thank you for the comment. Due to a large number of studies, it is not visible, thus we removed it from the revised manuscript. However, we capture the information.

- Figure 7 estimates difficult to see, suggest larger/higher quality images.

Thank you for the comment and suggestion. It is due to the small size figures, but its quality is good. If this manuscript is accepted the size would be decided during production.

- Additional file 2: please display specific search terms in a table rather than as a screenshot of databases. The search terms are sometimes cut-off in the screenshots, so not clear to see.

Thank you for the valuable comment; we revised it.

General comments:

- It is mentioned that all data is contained in the manuscript and supplementary files - will code be shared as well?

Thank you for the question, yes it is possible.

- in line 31, replace "electronic five major" with "five major electronic"

Thank you; now corrected.

- other minor grammar errors throughout, please verify.

- line 113: inconvenience? unclear. Maybe meant to say inconsistencies / discrepancies between reviewers?

Thank you for the question. Now corrected to inconsistencies.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (24.4KB, docx)

Decision Letter 1

Ivan Sabol

19 Apr 2021

PONE-D-20-32540R1

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

PLOS ONE

Dear Dr. Alemu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands.

The critical concern of reviewer 1 remains unmet after revision. Thus another reviewer was invited to additionally assess the study and raised similar concerns. The data was inappropriately combined and thus the conclusions drawn fail the publication criteria 4. https://journals.plos.org/plosone/s/criteria-for-publication

Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process and critically address the issue of MDR and XDR data in your predictors of mortality analysis.

Please submit your revised manuscript by Jun 03 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Ivan Sabol

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #3: (No Response)

Reviewer #4: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #3: Yes

Reviewer #4: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Authors have partly addressed major remarks and have done the analyse of the pooled estimate of mortality for MDR-TB and XDR-TB separately. Still, the risk factors were assessed for all DR-TB as a group. Given that these are very different populations of patients with very different outcomes (according to all knowledge from the literature), it does not make sense to mix them together as the conclusions that consequentially cannot be applied for neither on of those subgroups among the hat of „drug resistance“.

Given the amount of quality work and analysis that was put into this manuscript, it would be of a great value to do the risk analyse separately for different groups of resistance (or at least mono-resistance vs MDR and XDR) as it would have much more sense and value for every day clinical work. I would suggest at least to put a subgroup analysis in a supplemental (if the numbers of subgroups under-power the analysis)

Reviewer #3: The reviewer thanks the authors for their efforts to address reviewer suggestions. Some minor comments remain unaddressed, see below:

1. Introduction:

Introduction still somewhat brief - need to discuss findings of prior reviews. Nice to see you have now mentioned that other reviews exist on the topic, but you have not cited them. Suggest to cite them where you first mention them (Line 77 of the introduction), and briefly mention their findings and what remains to be explored.

2. Methods:

As mentioned in the previous round of reviews, a table clearly listing the inclusion and exclusion criteria would be helpful. Although you have mentioned reasons for exclusion in the Figure 1 flowchart, these are vague (e.g. “mixed study groups”, “overlapped studies”). It would be good to have a table of minimum requirements for inclusion, i.e. which study designs included, minimum requirements for data reported to be included, etc…

3. Additional file 2:

Please display specific search terms in a table rather than as a screenshot of databases. The author response sheet says this has been corrected but I am not sure where, as the screenshots are still in additional file 2.

Reviewer #4: I have read with interest the article by Alemu and co-authors. This systematic review and meta-analysis, aiming to identify predictors of mortality for patients with multidrug-resistant tuberculosis, has already been reviewed in detail by three Reviewers. I will therefore limit my comments to high-level overall considerations.

1. In agreement with Reviewer 1, I think that the main criticism to this study is related to the inclusion of all types of drug-resistance. Pooling resistance “to at least one first-line anti-TB drug” does not make sense from a clinical perspective. I appreciate that the authors tried, in the revised version of the manuscript, to analyse separately mortality rates for MDR-TB and XDR-TB. However, this is not applied to the main objective of the study, the identification of predictors of mortality. And, this does not solve the issue of including patients with monoresistance to a TB drug (like isoniazid-monoresistance for instance) which have different mortality risk and may have different mortality predictors. My advice, as suggested by Reviewer 1, would be to restrict the systematic review to specific groups of drug resistance (for example, a) isoniazid-resistant, rifampicin-susceptible; b) rifampicin-resistant, fluoroquinolone-susceptible; and c) rifampicin- and fluoroquinolone-resistant) and identify mortality rates and predictors for ach of this groups. This way, the results would be clear and more meaningful.

2. Performing and analysis stratified by clinically-relevant subgroups of drug-resistant TB patients, as suggested in point 1, would also increase the novelty and interest of the results of this study, compared to existing literature (as per comment of Reviewer 3).

3. The search should be updated to identify more recent studies, as noted by Reviewer 2.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #1: No

Reviewer #3: No

Reviewer #4: No

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PLoS One. 2021 Jun 28;16(6):e0253848. doi: 10.1371/journal.pone.0253848.r004

Author response to Decision Letter 1

10 May 2021

Revisions based on the Editor’s and the reviewers’ comments and suggestions

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

Editor Comments and suggestions

1. The critical concern of reviewer 1 remains unmet after revision. Thus another reviewer was invited to additionally assess the study and raised similar concerns. The data was inappropriately combined and thus the conclusions drawn fail the publication criteria 4. https://journals.plos.org/plosone/s/criteria-for-publication

Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process and critically address the issue of MDR and XDR data in your predictors of mortality analysis.

Thank you for the valuable comments and suggestions. We performed a sub-group analysis on the predictors of mortality per the types of resistance categories based on the data presented in the primary studies. Besides, we broadened our searching and four primary studies included in the revised manuscript. We performed the pooled analysis by considering these new studies.

Reviewers’ comments and suggestions

Reviewer #1

1. Reviewer #1: Authors have partly addressed major remarks and have done the analyse of the pooled estimate of mortality for MDR-TB and XDR-TB separately. Still, the risk factors were assessed for all DR-TB as a group. Given that these are very different populations of patients with very different outcomes (according to all knowledge from the literature), it does not make sense to mix them together as the conclusions that consequentially cannot be applied for neither on of those subgroups among the hat of „drug resistance“.

Given the amount of quality work and analysis that was put into this manuscript, it would be of a great value to do the risk analyse separately for different groups of resistance (or at least mono-resistance vs MDR and XDR) as it would have much more sense and value for every day clinical work. I would suggest at least to put a subgroup analysis in a supplemental (if the numbers of subgroups under-power the analysis)

Thank you for the constructive comment. As per the suggestion given, we performed a sub-group analysis per the resistance category presented in the primary studies.

Reviewer #3:

Reviewer #3: The reviewer thanks the authors for their efforts to address reviewer suggestions. Some minor comments remain unaddressed, see below:

1. Introduction:

Introduction still somewhat brief - need to discuss findings of prior reviews. Nice to see you have now mentioned that other reviews exist on the topic, but you have not cited them. Suggest to cite them where you first mention them (Line 77 of the introduction), and briefly mention their findings and what remains to be explored.

Thank you for the comment once again. In the revised we cited some previous studies and also we presented the findings of a systematic review and meta-analysis study performed by our team in 2020 that showed the incidence of poor treatment outcome and its predictors among DR-TB patients in Ethiopia. The study also estimated the mortality incidence density rate.

2. Methods:

As mentioned in the previous round of reviews, a table clearly listing the inclusion and exclusion criteria would be helpful. Although you have mentioned reasons for exclusion in the Figure 1 flowchart, these are vague (e.g. “mixed study groups”, “overlapped studies”). It would be good to have a table of minimum requirements for inclusion, i.e. which study designs included, minimum requirements for data reported to be included, etc…

Thank you for the comment and suggestion. In the revised manuscript, we prepared a table for inclusion and exclusion criteria as a supplementary table 1.

3. Additional file 2:

Please display specific search terms in a table rather than as a screenshot of databases. The author response sheet says this has been corrected but I am not sure where, as the screenshots are still in additional file 2.

Thank you for the valuable comment. Previously we presented the PubMed search terms in the table, but in the current version, we displayed the search terms for the remaining search engines in table.

Reviewer #4:

Reviewer #4: I have read with interest the article by Alemu and co-authors. This systematic review and meta-analysis, aiming to identify predictors of mortality for patients with multidrug-resistant tuberculosis, has already been reviewed in detail by three Reviewers. I will therefore limit my comments to high-level overall considerations.

1. In agreement with Reviewer 1, I think that the main criticism to this study is related to the inclusion of all types of drug-resistance. Pooling resistance “to at least one first-line anti-TB drug” does not make sense from a clinical perspective. I appreciate that the authors tried, in the revised version of the manuscript, to analyse separately mortality rates for MDR-TB and XDR-TB. However, this is not applied to the main objective of the study, the identification of predictors of mortality. And, this does not solve the issue of including patients with monoresistance to a TB drug (like isoniazid-monoresistance for instance) which have different mortality risk and may have different mortality predictors. My advice, as suggested by Reviewer 1, would be to restrict the systematic review to specific groups of drug resistance (for example, a) isoniazid-resistant, rifampicin-susceptible; b) rifampicin-resistant, fluoroquinolone-susceptible; and c) rifampicin- and fluoroquinolone-resistant) and identify mortality rates and predictors for ach of this groups. This way, the results would be clear and more meaningful.

Thank you for the valuable comment and suggestion. As per the suggestion, we performed a sub-group analysis to assess the predictors of mortality among different resistance categories as presented in the primary studies.

2. Performing and analysis stratified by clinically-relevant subgroups of drug-resistant TB patients, as suggested in point 1, would also increase the novelty and interest of the results of this study, compared to existing literature (as per comment of Reviewer 3).

Thank you for the comment. We performed analysis as per the suggestion.

3. The search should be updated to identify more recent studies, as noted by Reviewer 2.

Thank you for the comment. We updated the search and we identified additional four studies. We analyzed the mortality rates and the predictors of mortality by including the additional identified studies.

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file. (17.8KB, docx)

Decision Letter 2

Ivan Sabol

7 Jun 2021

PONE-D-20-32540R2

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

PLOS ONE

Dear Dr. Alemu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but needs a few very minor changes. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

All reviewers commend the addition of the subgroup analysis, but 2 reviewers note that this addition was not adequately included in the results and discussion and removed from the limitations. Please try to revise at least  the discussion text accordingly and mention this in the abstract at least briefly.

Please submit your revised manuscript by Jul 22 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Ivan Sabol

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

Reviewer #4: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: N/A

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Authors have added the subgroup analyses. Few sentences regarding that should also be added in the discussion (i.e. to discuss the differences (or absence of) between the subgroups regarding the mortality predictors. Also, as this is no more a limitation of the study, the sentence under the line 281 (about not performing sub-group analysis) should be removed.

Reviewer #3: The reviewer thanks the authors for their efforts to address the reviewer’s suggestions. The reviewer has no further suggestions, as the previous suggestions have now been addressed. The reviewer recommends acceptance of the manuscript, provided remaining comments from the other reviewers have also been adequately addressed.

Reviewer #4: I thank the authors for submitting this revised version of the manuscript.

The authors should be commended for including a sub-group analysis stratified by drug resistance pattern (as requested) and for updating their search and increasing the number of papers included in the meta-analysis.

However, the sub-group analysis is currently only presented as a secondary paragraphin the results, not commented in the discussion, and not included at all in the abstract. In my opinion, what is currently presented as sub-group analysis should actually represent the main result of the study: as such, the results of the stratified analysis should be presented in the abstract and developed in the discussion section.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: No

Reviewer #4: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2021 Jun 28;16(6):e0253848. doi: 10.1371/journal.pone.0253848.r006

Author response to Decision Letter 2

9 Jun 2021

Response

Thank you to all the reviewers and the editor for the valuable comments and suggestions that improved the quality of the manuscript. At this stage, we included the predictors of mortality across different drug-resistance categories at the abstract section (line 45-47) and at the discussion section (line 276-298). We removed the sentence “Also, subgroup analysis based on the type of drug-resistance pattern was not performed.” as this is no more a limitation of the study.

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file. (14.8KB, docx)

Decision Letter 3

Ivan Sabol

15 Jun 2021

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

PONE-D-20-32540R3

Dear Dr. Alemu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Ivan Sabol

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Ivan Sabol

18 Jun 2021

PONE-D-20-32540R3

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

Dear Dr. Alemu:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Ivan Sabol

Academic Editor

PLOS ONE

Associated Data

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    Supplementary Materials

    S1 Table. Completed PRISMA 2009 checklist.

    (DOC)

    Click here for additional data file. (64KB, doc)
    S2 Table. Search engines.

    (DOCX)

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    S3 Table. Inclusion and exclusion criteria for selection of studies.

    (DOCX)

    Click here for additional data file. (13.1KB, docx)
    S4 Table. Quality assessment for the included studies in meta-analysis.

    (DOCX)

    Click here for additional data file. (56.6KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (24.4KB, docx)
    Attachment

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    Click here for additional data file. (17.8KB, docx)
    Attachment

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    Data Availability Statement

    All relevant data are within the paper and its Supporting information files.

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