The effect of previous SARS-CoV-2 infection on systemic immune responses in individuals with tuberculosis.

Background: The impact of previous SARS-CoV-2 infection on the systemic immune response during tuberculosis (TB) disease has not been explored. Methods: An observational, cross-sectional cohort was established to evaluate the systemic immune response in persons with pulmonary tuberculosis with or without previous SARS-CoV-2 infection. Those participants were recruited in an outpatient referral clinic in Rio de Janeiro, Brazil. TB was defined as a positive Xpert-MTB/RIF Ultra and/or a positive culture of Mycobacterium tuberculosis from sputum. Stored plasma was used to perform specific serology to identify previous SARS-CoV-2 infection (TB/Prex-SCoV-2 group) and confirm the non- infection of the tuberculosis group (TB group). Plasmatic cytokine/chemokine/growth factor profiling was performed using Luminex technology. Tuberculosis severity was assessed by clinical and laboratory parameters. Participants from TB group (4.55%) and TB/Prex-SCoV-2 (0.00%) received the complete COVID-19 vaccination. Results: Among 35 participants with pulmonary TB, 22 were classified as TB/Prex-SCoV-2. The parameters associated with TB severity, together with hematologic and biochemical data were similar between the TB and TB/Prex-SCoV-2 groups. Among the signs and symptoms, fever and dyspnea were significantly more frequent in the TB group than the TB/Prex-SCoV-2 group (p < 0,05). A signature based on lower amount of plasma EGF, G-CSF, GM-CSF, IFN-α2, IL-12(p70), IL-13, IL-15, IL-17, IL-1ß, IL-5, IL-7, and TNF-ß was observed in the TB/Prex-SCoV-2 group. In contrast, MIP-1ß was significantly higher in the TB/Prex-SCoV-2 group than the TB group. Conclusion: TB patients previously infected with SARS-CoV-2 had an immunomodulation that was associated with lower plasma concentrations of soluble factors associated with systemic inflammation. This signature was associated with a lower frequency of symptoms such as fever and dyspnea but did not reflect significant differences in TB severity parameters observed at baseline.

A Clinical Prediction Model for Unsuccessful Pulmonary Tuberculosis Treatment Outcomes.

BACKGROUND: Despite widespread availability of curative therapy, tuberculosis (TB) treatment outcomes remain suboptimal. Clinical prediction models can inform treatment strategies to improve outcomes. Using baseline clinical data, we developed a prediction model for unsuccessful TB treatment outcome and evaluated the incremental value of human immunodeficiency virus (HIV)-related severity and isoniazid acetylator status. METHODS: Data originated from the Regional Prospective Observational Research for Tuberculosis Brazil cohort, which enrolled newly diagnosed TB patients in Brazil from 2015 through 2019. This analysis included participants with culture-confirmed, drug-susceptible pulmonary TB who started first-line anti-TB therapy and had ≥12 months of follow-up. The end point was unsuccessful TB treatment: composite of death, treatment failure, regimen switch, incomplete treatment, or not evaluated. Missing predictors were imputed. Predictors were chosen via bootstrapped backward selection. Discrimination and calibration were evaluated with c-statistics and calibration plots, respectively. Bootstrap internal validation estimated overfitting, and a shrinkage factor was applied to improve out-of-sample prediction. Incremental value was evaluated with likelihood ratio-based measures. RESULTS: Of 944 participants, 191 (20%) had unsuccessful treatment outcomes. The final model included 7 baseline predictors: hemoglobin, HIV infection, drug use, diabetes, age, education, and tobacco use. The model demonstrated good discrimination (c-statistic = 0.77; 95% confidence interval, .73-.80) and was well calibrated (optimism-corrected intercept and slope, -0.12 and 0.89, respectively). HIV-related factors and isoniazid acetylation status did not improve prediction of the final model. CONCLUSIONS: Using information readily available at treatment initiation, the prediction model performed well in this population. The findings may guide future work to allocate resources or inform targeted interventions for high-risk patients.

Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults.

OBJECTIVE: To systematically review and critically evaluate prediction models developed to predict tuberculosis (TB) treatment outcomes among adults with pulmonary TB. DESIGN: Systematic review. DATA SOURCES: PubMed, Embase, Web of Science and Google Scholar were searched for studies published from 1 January 1995 to 9 January 2020. STUDY SELECTION AND DATA EXTRACTION: Studies that developed a model to predict pulmonary TB treatment outcomes were included. Study screening, data extraction and quality assessment were conducted independently by two reviewers. Study quality was evaluated using the Prediction model Risk Of Bias Assessment Tool. Data were synthesised with narrative review and in tables and figures. RESULTS: 14 739 articles were identified, 536 underwent full-text review and 33 studies presenting 37 prediction models were included. Model outcomes included death (n=16, 43%), treatment failure (n=6, 16%), default (n=6, 16%) or a composite outcome (n=9, 25%). Most models (n=30, 81%) measured discrimination (median c-statistic=0.75; IQR: 0.68-0.84), and 17 (46%) reported calibration, often the Hosmer-Lemeshow test (n=13). Nineteen (51%) models were internally validated, and six (16%) were externally validated. Eighteen (54%) studies mentioned missing data, and of those, half (n=9) used complete case analysis. The most common predictors included age, sex, extrapulmonary TB, body mass index, chest X-ray results, previous TB and HIV. Risk of bias varied across studies, but all studies had high risk of bias in their analysis. CONCLUSIONS: TB outcome prediction models are heterogeneous with disparate outcome definitions, predictors and methodology. We do not recommend applying any in clinical settings without external validation, and encourage future researchers adhere to guidelines for developing and reporting of prediction models. TRIAL REGISTRATION: The study was registered on the international prospective register of systematic reviews PROSPERO (CRD42020155782).