Background: Successful tuberculosis (TB) treatment is necessary for disease control. The World Health Organization (WHO) has a target TB treatment success rate of ≥90%. We assessed whether the different types of unfavorable TB treatment outcome had different predictors. Methods: Using data from Regional Prospective Observational Research for Tuberculosis-Brazil, we evaluated biological and behavioral factors associated with each component of unsuccessful TB outcomes, recently updated by WHO (death, loss to follow-up [LTFU], and treatment failure). We included culture-confirmed, drug-susceptible, pulmonary TB participants receiving standard treatment in 2015–2019. Multinomial logistic regression models with inverse probability weighting were used to evaluate the distinct determinants of each unsuccessful outcome. Results: Of 915 participants included, 727 (79%) were successfully treated, 118 (13%) were LTFU, 44 (5%) had treatment failure, and 26 (3%) died. LTFU was associated with current drug-use (adjusted odds ratio [aOR] = 5.3; 95% confidence interval [CI], 3.0–9.4), current tobacco use (aOR = 2.9; 95% CI, 1.7–4.9), and being a person with HIV (PWH) (aOR = 2.0; 95% CI, 1.1–3.5). Treatment failure was associated with PWH (aOR = 2.7; 95% CI, 1.2–6.2) and having diabetes (aOR = 2.2; 95% CI, 1.1–4.4). Death was associated with anemia (aOR = 5.3; 95% CI, 1.4–19.7), diabetes (aOR = 3.1; 95% CI, 1.4–6.7), and PWH (aOR = 3.9; 95% CI, 1.3–11.4). Direct observed therapy was protective for treatment failure (aOR = 0.5; 95% CI, .3–.9) and death (aOR = 0.5; 95% CI, .2–1.0). Conclusions: The treatment success rate was below the WHO target. Behavioral factors were most associated with LTFU, whereas clinical comorbidities were correlated with treatment failure and death. Because determinants of unsuccessful outcomes are distinct, different intervention strategies may be needed to improve TB outcomes.

Background: Tuberculosis (TB) treatment–related adverse drug reactions (TB-ADRs) can negatively affect adherence and treatment success rates. Methods: We developed prediction models for TB-ADRs, considering participants with drug-susceptible pulmonary TB who initiated standard TB therapy. TB-ADRs were determined by the physician attending the participant, assessing causality to TB drugs, the affected organ system, and grade. Potential baseline predictors of TB-ADR included concomitant medication (CM) use, human immunodeficiency virus (HIV) status, glycated hemoglobin (HbA1c), age, body mass index (BMI), sex, substance use, and TB drug metabolism variables (NAT2 acetylator profiles). The models were developed through bootstrapped backward selection. Cox regression was used to evaluate TB-ADR risk. Results: There were 156 TB-ADRs among 102 of the 945 (11%) participants included. Most TB-ADRs were hepatic (n = 82 [53%]), of moderate severity (grade 2; n = 121 [78%]), and occurred in NAT2 slow acetylators (n = 62 [61%]). The main prediction model included CM use, HbA1c, alcohol use, HIV seropositivity, BMI, and age, with robust performance (c-statistic = 0.79 [95% confidence interval {CI}, .74–.83) and fit (optimism-corrected slope and intercept of −0.09 and 0.94, respectively). An alternative model replacing BMI with NAT2 had similar performance. HIV seropositivity (hazard ratio [HR], 2.68 [95% CI, 1.75–4.09]) and CM use (HR, 5.26 [95% CI, 2.63–10.52]) increased TB-ADR risk. Conclusions: The models, with clinical variables and with NAT2, were highly predictive of TB-ADRs.

Background: Exposure to first-line anti-tuberculosis (TB) drugs varies significantly between individuals and populations. We developed population pharmacokinetic (PopPK) models using observational data from persons receiving fixed-dose combination (FDC) to evaluate the association of HIV, DM, and toxicity with drug exposure. Methods: RePORT-Brazil participants with drug-susceptible, culture-confirmed pulmonary TB treated with rifampin, isoniazid, pyrazinamide, and ethambutol (FDC) were included. Samples were obtained at month 1 of TB treatment. Age, sex, weight, DM- (according to glycated hemoglobin, non-DM ≤5·7%, pre-DM 5·8–6·4%, and DM ≥6·5%), HIV-status, and NAT2 profiles were examined. Adverse drug reactions (ADRs) were graded for severity. Liquid chromatography/Mass Spectrometry quantified drug concentration. Time since last dose was self-reported; samples >8h post-dose and with undetectable levels were excluded. Monolix® was used for model building and individual parameter estimation. AUC0-8 exposures were compared by DM-, HIV-status, and ADRs. Findings: Of 388 unique samples (one per participant), 49 (13%) were excluded. Rifampin had the highest rate of undetectable levels (n=45, 13%), which was associated with shorter self-reported time between dose intake and sample collection (median 1·8 vs 3·0 hours, p<0·001) but not with other baseline characteristics. One-compartment PopPK models adequately described all drugs. AUC0-8 was lower among people with HIV (PWH) for rifampin (p=0·008) and ethambutol (p=0·022). Pre-DM and DM were associated with lower rifampin exposure (p=0·026 and p=0·004, respectively), and pre-DM with lower pyrazinamide exposure (p=0·027). PWH were more likely to experience grade ≥2 ADRs compared to HIV-negative individuals (30% vs 15%, p=0·003). Isoniazid exposure was higher among participants with grade ≥2 ADRs compared to those without (79·7 vs 73·5 mg·h/L, p=0·02). Interpretation: In people receiving TB treatment, four PopPK models demonstrated lower rifampin and ethambutol exposure among PWH, lower rifampin exposure with pre-DM and DM, lower pyrazinamide exposure with pre-DM, and higher isoniazid exposure among those with grade ≥2 ADRs. Funding: National Institutes of Health

BACKGROUND Tuberculosis/human immunodeficiency virus (TB/HIV) coinfection is associated with advanced HIV disease and variable responses to antiretroviral therapy (ART). OBJECTIVES We examined whether baseline HIV severity markers, ART regimes, or human genetic variants influenced HIV-1 virologic suppression in HIV-associated TB. METHODS We included TB/HIV participants from Regional Prospective Observational Research in Tuberculosis (RePORT)-Brazil study, who received standard TB therapy and antiretroviral treatment. The primary endpoint was HIV-1 virologic suppression (≤ 1,000 copies/mL); Baseline characteristics, viral load (VL), CD4 cell count, timing of ART initiation, and ART regimens were included. We genotyped UGT1A1 (rs887829; integrase strand transfer inhibitor-related) and CYP2B6 [rs3745274, rs28399499, rs4803419; efavirenz (EFZ)-related]; all have defined normal, intermediate, and slow genotypes. Genotyping was performed by MassARRAY iPLEX Gold; Kaplan-Meier curves compared time-to-suppression with log-rank tests; Cox proportional hazards models estimated hazard ratios. FINDINGS Among 194 participants, 68% (n = 132) achieved virologic suppression (≤ 1,000 copies/mL). Median time-to-suppression: 84 days [95% confidence interval (CI): 42-125]. Participants with higher baseline viral load (BVL) (≥ 5 log10 copies/mL) had delayed suppression compared with those with lower VL (< 5 log10; log-rank χ² = 75.9; p < 0.001). Individuals with CD4 ≤ 200 cells/µL suppressed more slowly than those with CD4 > 200 cells/µL (log-rank χ² = 29.6; p < 0.001). Participants starting ART before TB treatment achieved suppression faster than ART-naïve individuals (32 vs. 147 days; log-rank χ² = 48.5; p < 0.001). Higher BVL was associated with reduced hazard of suppression [adjusted hazard ratio (aHR) = 0.67; 95% CI: 0.61-0.75], while higher baseline CD4 count increased the hazard of suppression (per 100 cells/µL: aHR = 1.11; 95% CI: 1.01-1.21). ART-naïve status was associated with lower hazard of suppression in univariate analysis (Hazard Ratio = 0.51; 95% CI: 0.36-0.72) but not after adjustment. ART regimen class and pharmacogenetic metabolizer profiles were not significantly associated with virologic suppression. MAIN CONCLUSIONS BVL and CD4 count were the strongest determinants of virologic suppression in TB/HIV patients. Suppression rates were low, and neither ART regimen nor pharmacogenetic profiles significantly influenced the likelihood of suppression.

Background: Tuberculosis (TB) treatment-related adverse drug reactions (TB-ADR) can negatively affect adherence and treatment success rates. Methods: We developed two prediction models for TB-ADR. We included drug-susceptible pulmonary TB participants who initiated standard TB therapy. TB-ADR were determined by physician-assigned attributions of causality, and described according to affected organ system, timing, and grade. Potential predictors of TB-ADR included concomitant medication (CM) use, HIV-status, glycated hemoglobin (HbA1c), age, body mass index (BMI), sex, substance use, and TB drug metabolism variables (e.g., NAT2 acetylator profile). Bootstrapped backwards selection was used to develop the models. Cox proportional hazards regression was used to evaluate TB-ADR risk. Results: There were 156 TB-ADR among 102 (11%) of the 945 participants included. Most TB-ADR were hepatic (n=82;53%), grade 2 (n=121;78%), and occurred in NAT2 slow acetylators (n=62;61%). The main prediction model included CM use, HbA1c, alcohol-use, HIV-infection, BMI, and age. The alternative model included the same variables, except replaced BMI with NAT2 . Both models had good performance and fit. CM use and HIV-infection increased TB-ADR risk. Conclusions: The model with only clinical variables and that with NAT2 were highly predictive of TB-ADR. The NAT2 model provides rationale to evaluate isoniazid dose adjustment and ADR risk.

Tuberculosis (TB) and Covid-19 are respiratory diseases and their interaction could increase lung impairment and mortality. However, few cases of concomitant TB and Covid-19 have been reported, and biomarkers are still poorly described. This cross-sectional study included in-hospital adult (>18 years old) participants, with pulmonary TB (TB group), with Covid-19 (Covid-19 group), and with TB/Covid-19 co-infection (TB/Covid-19 group). We compared baseline demographic, clinical, laboratory, and inflammatory profile features across the groups. Inflammatory soluble factors were assessed via MILLIPLEX MAP Human Cytokine/Chemokine Premixed 29 Plex and Luminex Intelliflex xMAP. We used the Chi-square test for categorical variables, and the Mann-Whitney U or the Kruskal-Wallis tests for continuous variables. A random forest model evaluated the variables importance in discriminating the groups, assessed by the Mean Decrease Gini Index (≥1.5). Among the 76 participants included, 33 (43%) with pulmonary TB, 13 (17%) with TB/Covid-19, and 30 (39%) with Covid-19. Male participants were predominant in all groups. The TB and TB/Covid-19 groups had lower median body mass index (BMI) (18.4 [interquartile range (IQR) 16–22] and 19.4 [IQR 16.1–20.3], respectively) compared to the Covid-19 group (25.5 [IQR 22.0–31.7]; p<0.001). People living with HIV/AIDS were more frequent in TB (63%) and TB/Covid-19 (61%) groups than in Covid-19 (20%; p=0.001). Participants in the Covid-19 group had significantly more comorbidities (63%) than those in the TB (9%) and TB/Covid-19 (15%) groups (p<0.001). Comorbidities included HIV seropositivity, diabetes mellitus, chronic obstructive pulmonary disease, and hypertension. Cavitation on chest CT scan was observed in 45% of TB and 61% of TB/Covid-19 participants, but none in the Covid-19 group (p<0.001). Covid-19 participants exhibited lower platelet counts (p=0.004), higher creatinine (p0.70 in both cases). MCP-1 was significantly elevated in Covid-19 compared to TB and TB/Covid-19 (p=0.014). No inflammatory signature differentiated TB/Covid-19 from single infections. TB and Covid-19 are clinically similar and TB/Covid-19 co-infection is uncommon. Overall, the plasma inflammatory response could not differentiate Covid-19, TB and TB/Covid-19. Comorbidities, radiologic findings, and laboratory tests, such as urea, hemoglobin, hematocrit and platelets, may be useful tools to distinguish TB and TB/Covid-19, as well as Covid-19 and TB/Covid-19.