Therapeutic Drug Monitoring in the Treatment of Active Tuberculosis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».