Therapeutic Drug Monitoring in the Treatment of Active Tuberculosis
Bibliographic record
Abstract
Therapeutic drug monitoring ensures optimal dosing while aiming to reduce toxicity. However, due to the high costs and complexity of testing, therapeutic drug monitoring is not routinely used in the treatment of individuals with active tuberculosis, despite the efficacy demonstrated in several randomized trials. This study reviewed data spanning five years regarding the frequency of finding low drug levels in patients with tuberculosis, the dosing adjustments that were required to achieve adequate levels and the factors associated with low drug levels. BACKGROUND: Therapeutic drug monitoring (TDM) is used to optimize dosing that maximizes therapeutic benefit while minimizing toxicity. In the treatment of active tuberculosis (TB), TDM is not routine, yet low levels of anti-TB drugs can be associated with poorer treatment outcomes. METHODS: In a retrospective case control study, patients with active TB in whom TDM was performed were considered cases and compared with controls who did not undergo TDM, and matched according to year of diagnosis and the results of direct smear microscopy. Medical records were reviewed to abstract demographic, clinical, radiographic and microbiological data including time until smear and culture conversion. RESULTS: In total, 20 patients were identified in whom TDM was performed, of whom 17 (87%) had at least one low drug concentration. Overall, 27 of 45 (60%) initial drug concentrations were low and resulted in an increased drug dosage. Low drug levels were found in 13 of 15 (87%) isoniazid, four of five (80%) rifabutin and eight of 12 (67%) rifampin measurements, but in only two of 13 (15%) pyrazinamide measurements. Within cases only, the 17 patients with low serum drug levels were significantly more likely to have comorbid illnesses, be smear positive, have lower serum albumin levels and had nonsignificantly longer time to culture conversion, compared with the three cases in whom all drug levels were within therapeutic ranges. CONCLUSIONS: TB drug levels were frequently below clinically acceptable levels in patients with active TB, particularly in those with HIV infection or other comorbidities. TDM is potentially useful for the treatment of active TB, but is currently underused.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".