Introduction

Multidrug-Resistant Tuberculosis (MDRTB) refers to Mycobacterium tuberculosis (TB) strains with in vitro resistance to the two most effective anti-tuberculosis drugs, isoniazid (INH) and rifampin (RFP). MDRTB has become a major barrier to achieving successful control of TB, as therapy is less effective, associated with more adverse events and is more costly to treat when compared with standard first line therapy. According to a recent WHO report, approximately 490,000 MDRTB cases occur globally every year, corresponding to approximately 4.8% of the world's TB cases [1], [2]. The importance of addressing drug resistant TB is further amplified by more recent reports on extensively drug resistant TB (XDRTB) [3], which represented 7% of MDR isolates referred to supranational reference laboratories from 2000–2004 [1].

Inadequate treatment of MDRTB can lead to worse patient outcomes, while increasing the risk of extensive drug resistance [4]–[6]. Guidelines for the management of MDRTB have been developed over the past decade, but there is little evidence based on randomized controlled trials to support current recommendations [7], [8]. Moreover, treatment strategies have varied significantly and are difficult to compare between populations [8], [9]. This lack of evidence reflects a lack of political and financial will, in part from the perception that MDRTB is of limited epidemiological importance [7]. It also reflects the limited number of second line drugs that are available and the unequal distribution of access depending on local resources. The recent recognition of the increasing magnitude of MDRTB, along with the poor prognosis of XDRTB has created the impetus for a more evidence-based approach to the treatment of MDRTB.

Recently, standardized definitions were established to allow comparison between treatment groups and facilitate the development of a more evidence-based approach [9], [10]. We therefore decided to complete a systematic review of MDRTB treatment regimens. Where appropriate, we performed a meta-analysis to explore associations between MDRTB treatment outcomes and the clinical and microbiological factors that influence outcome. We aimed to identify all the published literature and to establish the best possible evidence base of clinical and microbiological predictors of treatment response.

Methods

Results

We identified 9835 citations from our initial electronic database search. Of these, 116 articles were identified for full text review. A further 17 articles were identified through manual search, bibliographic search, and reviewer suggestions. Of the 133 articles reviewed in full text, 36 articles, representing 31 distinct patient populations were analyzed based on our a priori inclusion-exclusion criteria and quality assessment [11]–[46]. Ninety-four studies were removed prior to analysis: 36 lacked adequate data on the patient population or outcomes; 17 evaluated an inappropriate population or overlapped with a larger study population; 5 reported outcomes based on first line therapy alone; 22 did not meet criteria for treatment duration or follow-up; 9 reported more than one-third default or loss to follow-up; 5 reported outcomes on <10 patients; and 3 were excluded based on study design (Figure 1).

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Figure 1. Forest plots of covariates.

https://doi.org/10.1371/journal.pone.0006914.g001

The 36 articles chosen for review reported treatment outcomes on 31 study populations from 21 countries on 5 continents (Table 1). The study periods ranged from 1973 to 2006. The majority were retrospective chart reviews, with five prospective cohorts [17], [28], [37], [42], [43] and one retrospective case control [32]. The average study population (range) was 210 (24–1407). The majority of patients were male, with a mean of 68% (48–92) across all studies. In 30 studies with available data, the mean age was 40 years. All studies reported on patients with a prior history of treatment. In studies with available data, 84% (32–100) of patients had previously received anti-tuberculous therapy. The average number of drugs to which resistance was shown was 4.1 for studies reporting resistance. For those reporting first and second-line resistance, the average number of drugs to which there was resistance was 4.4.

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Table 1. Summary of studies.

https://doi.org/10.1371/journal.pone.0006914.t001

In the 26 trials with a total 4959 patients reporting EOT outcomes 62% [95% CI 57–67] of patients met the definition of successful treatment, while 11% [9]–[13] of patients died and 8% [5]–[11] failed therapy (Table 2). The default rate was 13% [9]–[17], while 2% [1]–[4] had their care transferred to another jurisdiction. In 9 trials with a total of 1583 patients reporting outcomes at follow-up, the mean follow-up duration was 27 [15]–[51] months (Table 3). The percentage with successful outcomes at follow-up was 64% [56–72], while 14% [10]–[19] died, 13% [7]–[20] defaulted, 5% [2]–[9] failed therapy, 1% [0–2] had their care transferred and 2 [0–4] relapsed. Based on the data provided, the percentage of patients presenting with re-infection, rather than relapse cannot be determined.

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Table 2. End of Treatment Outcomes.

https://doi.org/10.1371/journal.pone.0006914.t002

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Table 3. Outcomes at Follow-up.

https://doi.org/10.1371/journal.pone.0006914.t003

Covariate analysis

Predetermined covariates (gender, diabetes, alcohol abuse, HIV positive, BMI, smear positive, cavitary disease, surgical intervention, prior treatment, ≥6 drug resistance, fluoroquinolone use, and presence of XDRTB) were pooled and analyzed. The following covariates were associated with poor outcome: male gender (pooled OR for successful outcome 0.61, [95% CI0.46–0.82]), alcohol abuse 0.49 [0.39–0.63], low BMI 0.41 [0.23–0.72], smear positivity 0.53 [0.31–0.91], fluoroquinolone resistance 0.45 [0.22–0.91], and XDR resistance pattern 0.57 [0.41–0.80]. Surgical intervention 1.91 [1.44–2.53], no prior TB treatment 1.42 [1.05–1.94] and fluoroquinolone use 2.20 [1.19–4.09] were associated with successful outcome. Diabetes, cavitary disease, HIV and resistance to ≥6 drug were not statistically associated with worse outcomes. Two covariates, fluoroquinolone use and XDRTB, were analyzed for an association with death. The use of fluoroquinolones was associated with decreased mortality 0.30 [0.15–0.61], while there was a trend towards increased mortality with XDR (Figure 2).

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Figure 2. Summary of literature search and study selection.

https://doi.org/10.1371/journal.pone.0006914.g002

Discussion

Drug resistant TB ultimately develops from the inadequate treatment of active pulmonary TB. There are multiple reasons for inadequate therapy; poor prescribing practices with insufficient treatment duration and poor drug selection are well-recognized contributors [5], [47]. Systemic problems, through inadequate public health resources and unpredictable drug supplies also play a role [4]. In addition, irregular medication intake–whether from insufficient patient education, adverse events, or socioeconomic determinants–contribute to resistance. There are also a significant proportion of patients who acquire drug resistant disease because they live in an environment with a high prevalence of drug resistant disease.

Despite these well-known causes of drug resistance, our review of 36 observational studies revealed very high rates of default and transfer of care. The majority of unsuccessful outcomes at end of treatment were the result of these factors, with relapse accounting for only 2% of patients in follow-up. Moreover, default appears to be a global phenomenon, with rates over 15% in several countries, including Korea (32%) [25], Taiwan (29%) [15], Russia (20%) [23], Italy (17%) [18], Spain (16%) [17], South Africa (29%) [35], Argentina (20%) [33], and Peru (19%) [29]. With the advent of DOTS and DOTS-Plus strategies, MDRTB rates have improved in some high prevalence countries, such as Latvia where new MDR cases have decreased more than two-fold between 2000 and 2007 (V. Leimane, unpublished data). Yet under these improved circumstances, published default rates were above 10% [27].

The consequences of inadequate therapy are also apparent in our data. On pooled analysis we have shown that prior treatment, fluoroquinolone resistance, and XDRTB were all associated with poor outcomes. While resistance to fluoroquinolones was associated with worse outcomes, fluoroquinolone use was associated with successful outcomes. This finding is consistent with several observational studies showing that fluoroquinolones are highly effective in MDRTB treatment [15], [38], [40], [45], [46], [48], [49]. The WHO Green Light Committee recommends that MDR patients receive a fluoroquinolone when their culture results reveal a susceptible isolate [10]. But judicious use of fluoroquinolones is crucial in suspected and confirmed cases of TB if we hope to impede the development of further resistance [29]. What is less clear is the role of newer generation fluoroquinolones in the treatment of fluoroquinolone resistant MDRTB. The findings of Yew suggest that newer generation fluoroquinolones may be effective against ofloxacin resistant strains [46]. Further studies will be necessary to clarify the role of fluoroquinolones in this population. The emerging XDRTB literature may be a resource to explore this issue.

Since the seminal report on XDRTB from KwaZulu-Natal, South Africa [3], multiple studies have reported outcomes of XDRTB treatment [12], [23]–[26], [30], [50]–[53]. In the XDR cohorts examined in our study, the majority of patients had poor outcomes, despite low HIV rates. The global burden of XDRTB reflects the multiple deficiencies of tuberculosis control [47]. With aggressive, comprehensive individualized therapy, programs can improve XDRTB outcomes [30]. But, targets for interventions need to be better defined.

An obvious target for comprehensive interventions is in the management of co-morbidities. The most commonly reported co-morbidities in our selected studies were alcohol abuse, low BMI, and diabetes. The association between poor outcome with BMI and alcohol abuse is not unexpected. Body mass index, as a symptom of severe disease and low socioeconomic status requires aggressive intervention [29]. To our knowledge, there are no comparative studies examining the optimal nutritional intervention in an MDRTB population. Moreover, there are no evidence-based guidelines on nutritional supplementation in adults or children [54]. Further studies on MDRTB could not only include a baseline nutritional intervention, but also study the impact of various nutritional interventions on MDRTB outcomes. Meanwhile, alcohol abuse has been associated with poor TB outcome in several studies [16], [23], [35], [36], [55] and measures to improve care for alcohol abuse should be undertaken. A recently published study in Tomsk, Russia, describes successful adoption of a program to address alcohol use disorders, with the intent to measure outcomes [56].

Diabetes mellitus is also a well-described co-morbidity in MDRTB patients. Not only are diabetics prone to reactivation TB [57], they may also be at increased risk of developing MDRTB [58], [59]. The reasons for this are unclear, but based on our pooled data, diabetics do not appear to have worse outcomes with MDRTB. These results stand in contrast to previously published literature on drug sensitive TB [60], and may relate to our small sample size of 68 diabetics.

Lastly, our data supports the role of surgery in the management of MDRTB. Surgical patients appear to have superior outcomes—this may reflect the practice of performing surgery on less sick, adherent patients who have responded favourably to initial medical therapy. Although trials in surgery are inherently difficult, they should be undertaken to examine the role of surgery in MDRTB patients. Multiple surgical series have been published on this topic and could be examined for predictors of successful outcomes. In addition, future trials examining the role of surgery on highly resistant strains such as fluoroquinolone and XDR strains may be valuable.

Our study has several limitations. The most important is our inability to identify any randomized control trials (RCTs). Instead we relied exclusively on observational data for treatment outcomes. This likely introduced confounding to our pooled analysis, since crude outcomes, rather than adjusted odds ratios were reported for most trials. Moreover, the analysis of outcomes and pre-determined covariates used heterogeneous data from variable treatment regimens. The trends introduced by this analysis are subject to bias and should be interpreted with caution. Our quality analysis also likely introduced bias since only systems with resources for record keeping, second-line therapy and prolonged treatment and follow-up were included for analysis. These systems likely have improved outcomes compared with the many poorly resourced TB control programs throughout the world. Indeed, a recent editorial noted an “exceptionality” bias when commenting on reported XDRTB outcomes [61].

Of note, our treatment outcome definitions were heterogeneous between populations. Of the 36 studies included for analysis, only 12 studies (representing nine patient populations) met the outcome definitions proposed in WHO guidelines [10], [16], [22]–[27], [30], [35], [36], [40], [41]. The effect of this heterogeneity is difficult to assess. We observed, however, that several studies did not use the stringent culture-based outcome definitions such as Cure or Failed. This likely biased results towards more successful outcomes. Despite this, studies that used WHO outcomes showed similar outcomes: 62% [5–69] of patients had successful outcomes with a 15% [9]–[22] default and 10% [7]–[14] death rate.

Another significant limitation is the lack of data on HIV positive patients. In total 143 of 6359 patients (2%) were known to be HIV positive, with the data from 39 patients available for outcome analysis. This limits the power to detect the effect of the presence of HIV infection. Some studies reported HIV infected populations with treatment and follow-up durations shorter than 12 and 18 months respectively [3], [62], [63]. Thus, our inclusion criteria for treatment duration and follow-up likely excluded a disproportionate population of HIV positive patients from our analysis. In addition, our criteria eliminated studies examining ‘outbreaks’ of MDRTB, where a more virulent strain spreads quickly through a community. Such outbreak investigations tend to have limited follow-up and outcome data given the urgency to report findings [64], [65].

Recently, a systematic review examining MDRTB treatment outcomes in 33 populations was published by Orenstein et al. [66]. In this review, 62% of patients were classified as successful outcomes. Covariate analysis revealed that the proportion of patients with successful treatment outcomes did not significantly change for several individual covariates. The authors concluded that this likely reflected study heterogeneity. Similarly, our review examined MDRTB treatment outcomes. We have, however, focused on underlying patient and disease characteristics–such as diabetes, alcoholism, and smear positivity—while, Orenstein et al. focused on programmatic characteristics such as length of treatment and the use of Directly Observed Therapy. Overall, our outcomes do not differ markedly from those of Orenstein et al., and we feel that our covariate analysis contributes to the literature on this topic.

Though treatment success remains dismally low in MDRTB populations, there is reason to be hopeful. With the advent of standardized nomenclature, outcomes of MDRTB treatment regimens can be evaluated with more precision. The addition of more comprehensive therapeutic strategies will also help improve outcomes. But, an evidence-based approach, in addition to comprehensive, individualized therapy, is necessary to contain MDRTB [7]. Without high quality evidence to support therapeutic decisions, including data from high-risk populations such as HIV positive and diabetic patients, high mortality, default, and failure rates will persist.

Acknowledgments

The authors would like to acknowledge Kadria Alasay for her assistance on this project. We would also like to recognize the multiple authors who communicated with us during data collection for this article. In particular, the contributions of Dr. V. Leimane, Dr. S. Shin, Dr. J.J. Yim, Dr. H.R. Kim, Dr. W.J. Kwon, and Dr. V. Bartu are recognized.