Unraveling the Secrets Behind the Multidrug-Resistant Tuberculosis Treatment Outcome in Chronic Renal Failure Patients Requiring Hemodialysis: A Systematic Review
Notice bibliographique
Résumé
Multidrug-resistant/rifampicin-resistant tuberculosis (MDR/RR TB) is a global concern, with 450,000 new cases and 191,000 deaths in 2021. TB and chronic kidney disease (CKD) have been associated since 1974, with suggested explanations such as oxidative stress, malnutrition, dysfunction in vitamin D metabolism, and a compromised cell-mediated immune response. End-stage renal failure patients are more likely to acquire drug resistance due to poor adherence, adverse drug reactions, and inappropriate dose adjustment. We then aim to evaluate the therapeutic outcome of multidrug-resistant TB of the lungs in patients who require hemodialysis in terms of successful treatment (cured and treatment completed) and the associated factors for a favorable outcome. Our secondary goal is to identify unfavorable treatment outcomes (treatment failed, patient died, or patient lost to follow-up) and the underlying associated factors. We conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Guidelines for this systematic review. We included adults (>19 years old) with chronic kidney disease who needed hemodialysis and had microbiologically confirmed multidrug-resistant pulmonary TB, excluding patients who had a renal allograft transplant, were on peritoneal dialysis, had extrapulmonary TB, were children and pregnant patients. We searched PubMed, MEDLINE, PubMed Central, ScienceDirect, Public Library of Science (PLOS), and Google Scholar. Keywords were combined with the Boolean "AND" operator to gather results as well as the medical subject heading (MeSH) search strategy. After screening study articles by reading their titles and abstracts, the following tools were used to assess the risk of bias: the Newcastle-Ottawa scale for observational studies, the Assessment of Multiple Systematic Reviews (AMSTAR) checklist for systematic reviews, and the Joanna Briggs Institute (JBI) assessment tool for case reports. Primary and secondary outcomes were assessed, and a conclusion was made. We gathered 21,570 studies from the databases between 2013 and 2023, with 30,062 total participants. There were eight eligible studies for review. Patients with CKD, particularly those on dialysis, are at increased risk of TB due to a combination of factors that contribute to immunosuppression. TB reactivation is common in chronic renal failure patients. Diagnostic samples such as sputum and pleural fluid had lower sensitivity rates compared to tissue samples, which led to delays in diagnosis and treatment and, most importantly, contributed to drug resistance. All new dialysis patients should undergo interferon-gamma release assay testing. TB-infected patients with severe renal disease (eGFR 30 ml/min) had increased morbidity and mortality; however, the use of directly observed treatment, short-course (DOTS), and renal-dose adjustment of anti-TB medications significantly reduced these risks. Drug-induced hepatitis and cutaneous reactions were common adverse effects of anti-TB medications. A therapeutic drug monitoring guideline is required to reduce these adverse events and even mortality. Additional research is required to assess the safety and efficacy of therapeutic regimens, as well as their outcomes, in this population with multidrug-resistant TB.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,011 | 0,011 |
| Bibliométrie | 0,007 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».