Predictors of pulmonary tuberculosis treatment outcomes in South Korea: a prospective cohort study, 2005-2012

Tuberculosis (TB), a global concern for both developing and developed countries, has recently become more complex due to increasing levels of drug resistance and HIV co-infection [1]. Asian and African countries share the highest burden of tuberculosis, accounting for about 85% of the 8.6 million newly diagnosed TB cases reported globally in 2012 [2]. Efforts to reduce disease burden have been largely focused on improving treatment and diagnosis of patients with active disease [3]. Although HIV co-infection and multidrug-resistant (MDR) TB are major contributors to the global TB epidemic [4], a deeper understanding of other risk factors for poor outcome can suggest interventions that might help reduce morbidity and mortality. Poor socioeconomic status, including poverty, lack of education, and urbanization are known risk factors for active tuberculosis [5, 6]. Smoking and other behaviors such as alcohol consumption and drug use are also associated with poor treatment outcomes [7‐10]. Clinical characteristics including diabetes, baseline disease severity (on chest X-ray), previous treatment history, and drug-resistance have all been shown to be independent risk factors for poor treatment outcomes in previous studies [11‐13].

Few studies have prospectively identified factors associated with long-term prognosis. A retrospective study by Kim et al [14] evaluated long-term prognostic factors among MDR-TB patients and found that having extensively drug resistant (XDR) TB was the strongest predictor of poor outcomes. Two other studies evaluated the characteristics of patients with tuberculosis relapse after treatment completion [15, 16]. In one, a prior history of TB treatment was the largest risk factor associated with TB recurrence (HR: 5.2, CI: 1.7-16.2) [16] and, in another, DNA fingerprinting of cases of recurrent TB suggested that those previously infected with TB are at increased risk of developing TB again when re-infected [15]. This study aims to identify predictors with unfavorable outcome at the end of therapy and the long-term unfavorable outcome after treatment completion in South Korea.

Multiple previous cohort studies have reported extensively on the risk factors of tuberculosis outcomes, using various measures including relapse [15, 16], mortality [7, 10, 14, 19], MDR or XDR-TB status [11, 13, 20], and treatment failure, TI, or death [12, 21, 22]. In addition, differing study end points were used, including the end of therapy [12, 21, 22], long term follow up after treatment completion [14‐16], and cross-sectional comparison between M/XDR and potential risk factors [11, 13, 20]. Our study identified factors associated with poor outcomes among all patients at EOT and after long-term follow-up at EOS. In addition, in an effort to separate EOS risk factors from EOT risk factors, an analysis was done to identify factors for relapse or death at EOS just among those considered cured at EOT. At EOT, having diabetes, ≥3 previous treatment episodes, ≥1 significant regimen changes, and MDR-TB were all significantly associated with treatment failure or death (Table 3). At EOS, having diabetes and MDR-TB at baseline continued to be significantly associated with treatment failure, death, or relapse. In addition, baseline age ≥35 and BMI <18.5 were also poor prognostic factors, but the number of previous treatment episodes was no longer significant (Table 4). These risk factors, including age, BMI, and drug resistance pattern, are consistent with those of a previous study with long-term follow-up [14]. These results may suggest that older age and low BMI contribute to relapse or death risk after treatment completion. In isolating risk factors significantly associated with poor outcomes at EOS among cures at EOT, only age ≥50 and baseline BMI <18.5 were identified (Table 6), suggesting that once a patient is cured, many traditional baseline predictive variables, such as chest x-ray status, previous TB treatment history, and MDR-TB status may no longer be as relevant for prognosis. A different study analyzed factors for TB recurrence at EOS after a median 4-year follow-up after EOT and identified inner-city residence, HIV infection, and history of TB treatment as risk factors [16]. This study, however, included all treatment-completed patients at EOT regardless of culture conversion status and thus included some patients not fully cured. Future relapse studies among cured patients should be conducted to understand if their risk factors revert back to those similar to the general population.

Some previous studies included TI as an unfavorable outcome (24-26). Because our study population had a relatively high proportion of TI (18.2%) and reasons for TI may be very different from reasons for failure, death, or relapse, we analyzed this outcome separately, comparing TI cases only to cured cases. Other studies have also identified risk factors associated specifically with treatment default, including long distance to health facilities, substance use including alcohol consumption, a fear of social stigmatism, unemployment and economic constraint [10, 19, 23‐25]. In our analysis, alcohol consumption and smoking history were associated with TI in the univariate analysis but the significance was lost in the multivariate analysis. Not surprisingly, our study also identified an increased risk for TI among those who previously had a TI or treatment failure. For unclear reasons, having bilateral disease on baseline chest x-ray was associated with TI. Finally, being a blue-collar worker was also risk factor for TI, compared to those who were unemployed. This suggests that perhaps low wage workers had more difficulty getting time off for medical care and may require healthcare policy changes to resolve this issue. Further studies may need to be done to understand better the reasons for TI among service sector employees and laborers. Understanding the causes of TI and how to reduce treatment default is critically important because of the association of TI with the development of drug resistance [26].

Our study has several limitations. First, the study was conducted at two tertiary referral hospitals, which usually manage patients with more severe and extensive disease. This is reflected in our cohort demographics, with most patients having more severe advanced disease on x-ray, including cavities and bilateral disease, as well as the proportion of previously treated cases (compared to new cases) and MDR-TB. Our results, therefore, may not be representative of patients with less severe disease. Second, we only had DST results for 581/669 (86.8%). Analyses that include DST results exclude patients without DST results, which may cause a selection bias and the point estimates may be over or under-estimated due to the excluded cases. Third, we were not able to measure certain factors possibly related to treatment outcome, including hemoglobin A1c and bacterial load (time to positivity in liquid culture system). Fourth, diabetes status collected by self-report and types I vs. II could not be differentiated. Finally, the study was not designed to identify a causal pathway between the independent and outcome variables and was limited to identifying factors associated with unfavorable outcomes and TI.

This study of risk factors associated with poor treatment outcomes at EOT and EOS highlights both patient specific factors that are difficult to change, such as diabetes status and age, as well as disease specific factors, such as multidrug resistance, that may be affected by programmatic factors and could be altered to improve prognosis. Risk factors related to prolonged disease at diagnosis, including history of multiple previous treatment episodes, emphasize that the initial treatment episode is also likely the best chance of cure. Program management changes should be considered to emphasize public health measures such as directly observed therapy to help ensure that the first treatment episode is also the last one, in conjunction with improving adherence and decreasing the risk of MDR-TB. Policy changes to enable all TB patients, regardless of work status, to have time off to get appropriate treatment would also be important. Finally, steps to improve nutrition and therefore BMI among those successfully treated may help prevent longer-term relapses or recurrences. The treatment of tuberculosis requires a multifaceted approach for the best chance of success.