Use of Anti-Retroviral Therapy in Tuberculosis Patients on Second-Line Anti-TB Regimens: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Use of antiretroviral therapy (ART) during treatment of drug susceptible tuberculosis (TB) improves survival. However, data from HIV infected individuals with drug resistant TB are lacking. Second line TB drugs when combined with ART may increase drug interactions and lead to higher rates of toxicity and greater noncompliance. This systematic review sought to determine the benefit of ART in the setting of second line drug therapy for drug resistant TB. METHODS: We included individual patient data from studies that evaluated treatment of drug-resistant tuberculosis in HIV-1 infected individuals published between January 1980 and December of 2009. We evaluated the effect of ART on treatment outcomes, time to smear and culture conversion, and adverse events. RESULTS: Ten observational studies, including data from 217 subjects, were analyzed. Patients using ART during TB treatment had increased likelihood of cure (hazard ratio (HR) 3.4, 95% CI 1.6-7.4) and decreased likelihood of death (HR 0.4, 95% CI 0.3-0.6) during treatment for drug resistant TB. These associations remained significant in patients with a CD4 less than 200 cells/mm(3) and less than 50 cells/mm(3), and when correcting for drug resistance pattern. LIMITATIONS: We identified only observational studies from which individual patient data could be drawn. Limitations in study design, and heterogeneity in a number of the outcomes of interest had the potential to introduce bias. DISCUSSION: While there are insufficient data to determine if ART use increases adverse drug interactions when used with second line TB drugs, ART use during treatment of drug resistant TB appears to improve cure rates and decrease risk of death. All individuals with HIV appear to benefit from ART use during treatment for TB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".