Efficacy of isoniazid in paediatric tuberculosis: an individual participant data meta-analysis
Bibliographic record
Abstract
Background Isoniazid is a cornerstone of management therapy for tuberculosis (TB). Our aim was to determine the association between isoniazid exposure and clinical outcomes, to develop a pharmacokinetic model, and to optimise the dosing regimen in children treated for drug-susceptible (DS)-TB. Methods For this individual participant data meta-analysis, PubMed was searched for observational studies, involving children (aged 0–18 years), being treated for DS-TB. The relationship between isoniazid exposure and clinical outcomes was analysed using a mixed effects logistic regression model. Pharmacokinetic parameters were described using non-linear mixed effects modelling. The pharmacokinetic target was the median adult area under the concentration–time curve at steady-state (AUC ss ) of 23.4 mg·h·L −1 . Results Six studies provided clinical outcomes, including 405 patients, of which 21% had unfavourable outcomes. 16 studies (1255 patients) were included in the pharmacokinetic model. Unfavourable outcomes were only related to lower body mass index (BMI) for age z-score (BAZ) (OR 0.96, 95% CI 0.93–0.99; p<0.05). Isoniazid exposure was impacted by N -acetyltransferase 2 (NAT2) genotype, weight, age and nutritional status (using BAZ). With currently recommended World Health Organization (WHO) doses, isoniazid exposure was similar to that of adults. Pharmacokinetic target attainment was 71.7% and 29.5% for slow and fast metabolisers, respectively (p<0.05); 50.5% for patients with BAZ >0 and 42.6% for malnourished patients (BAZ < −2) (p<0.05). The model-informed dosing regimen showed that fast metabolisers could benefit from higher isoniazid dosing, especially in malnourished children. Conclusion Our findings showed that the only predictor of unfavourable clinical outcomes was a lower BAZ. We support the current WHO-recommended dosing regimen for isoniazid. To equalise and attain our pharmacological target for all children, dosing regimens could be adjusted on NAT2 genotype and nutritional status.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.055 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".