Efficacy and completion rates of rifapentine and isoniazid (3HP) compared to other treatment regimens for latent tuberculosis infection: a systematic review with network meta-analyses
Bibliographic record
Abstract
BACKGROUND: We conducted a systematic review and network meta-analysis (NMA) to examine the efficacy and completion rates of treatments for latent tuberculosis infection (LTBI). While a previous review found newer, short-duration regimens to be effective, several included studies did not confirm LTBI, and analyses did not account for variable follow-up or assess completion. METHODS: We searched MEDLINE, Embase, CENTRAL, PubMed, and additional sources to identify RCTs in patients with confirmed LTBI that involved a regimen of interest and reported on efficacy or completion. Regimens of interest included isoniazid (INH) with rifapentine once weekly for 12 weeks (INH/RPT-3), 6 and 9 months of daily INH (INH-6; INH-9), 3-4 months daily INH plus rifampicin (INH/RFMP 3-4), and 4 months daily rifampicin alone (RFMP-4). NMAs were performed to compare regimens for both endpoints. RESULTS: Sixteen RCTs (n = 44,149) and 14 RCTs (n = 44,128) were included in analyses of efficacy and completion. Studies were published between 1968 and 2015, and there was diversity in patient age and comorbidities. All regimens of interest except INH-9 showed significant benefits in preventing active TB compared to placebo. Comparisons between active regimens did not reveal significant differences. While definitions of regimen completion varied across studies, regimens of 3-4 months were associated with a greater likelihood of adequate completion. CONCLUSIONS: Most of the active regimens showed an ability to reduce the risk of active TB relative to no treatment, however important differences between active regimens were not found. Shorter rifamycin-based regimens may offer comparable benefits to longer INH regimens. Regimens of 3-4 months duration are more likely to be completed than longer regimens.
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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 | 0.023 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".