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Enregistrement W2337119289 · doi:10.4103/0970-2113.180799

Tuberculosis therapy in Mumbai: Critical importance of drug-susceptibility testing

2016· article· en· W2337119289 sur OpenAlexaff
Madhukar Pai, Amrita Daftary

Notice bibliographique

RevueLung India · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueTuberculosis Research and Epidemiology
Établissements canadiensMcGill UniversityMcGill University Health Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineTuberculosisRegimenIntensive care medicineDrug resistanceExtensively drug-resistant tuberculosisInternal medicineMycobacterium tuberculosisPathology

Résumé

récupéré en direct d'OpenAlex

India is the ground zero for global tuberculosis (TB) epidemic, and Mumbai is the ground zero for India's epidemic of multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB). In this issue of Lung India, Udwadia et al.[1] present data that highlight major concerns with the use of standardized, empiric treatment regimens for MDR-TB in Mumbai. The alarming rates of MDR-TB, heterogeneous drug-resistance patterns and disproportionate number of cases that are found to be pre-XDR and XDR, mandate the early and comprehensive drug-susceptibility testing (DST) of all TB patients, to allow for individualized, DST-guided drug regimens. The rationale to impose a standardized drug regimen for MDR-TB and harmonize medical practice is not unfounded. As in many other high-burden settings, upfront, universal DST is uncommon in India and hinders opportunities to devise optimal regimens for patients with MDR-TB.[2] Nearly half of all Indian TB patients are treated in the private sector[3] where the quality of care is highly variable,[4] and use of irrational drug regimens[5] urges stricter antibiotic stewardship, and makes a case for standardized protocols. However, there is an undeniable risk of amplifying drug resistance by promoting one regimen for all, without taking the local epidemiology into account. In the cohort of 1539 MDR-TB cases identified by the TB laboratory at Hinduja Hospital in Mumbai (which included samples from both public and private patients), only 30% were truly MDR-TB; remaining cases represented pre-XDR, XDR, and resistance beyond XDR-TB. In other words, nearly two-thirds of this sample would have received a suboptimal drug regimen and have been at risk for developing secondary resistance, had they been empirically treated under the recommended standardized regimen for MDR-TB.[1] This is not unlike resistance profiles of cases reported by Dalal et al. and Dholakia and Shah, who have similarly suggested the need for individualized, DST-guided drug regimens in Mumbai.[67] Other experts have argued that all TB patients in India deserve up-front DST,[8] and there are large-scale, programmatic data to support this.[9] The importance of factoring in the local epidemiology of TB into empiric treatment regimens thus cannot be overemphasized. In India, a standardized empiric regimen for MDR-TB may perpetuate delays in DST and inadvertently feed into provider complacency in managing difficult cases. While patients' previous treatment histories have guided empiric drug regimens in other settings, this option would be futile in Mumbai, where primary drug resistance is common, and previously treated patients lack a complete medical history due to their high rate of switching between providers.[310] The alternative recommendation suggested by Udwadia and colleagues[1] to use a tailored empiric drug regimen at their site based on data from their laboratory (and given their existing capacity to perform timely, comprehensive DST) is in line with the World Health Organization's most recent treatment guideline which emphasizes, "TB programs may need to adjust the (drug-resistant TB treatment) strategy to meet special circumstances and the local context."[11] The argument against a one-size-fits-all approach to MDR-TB management is also congruent with recent trends to shift away from rigid, inflexible TB management in general toward more individualized, patient-centered approaches to care.[1213] Patient retention is a major challenge to MDR-TB treatment completion and cure, and the administration of suboptimal drug regimens may be considered an important determinant of nonadherence and loss to follow-up.[1415] A patient-centered approach to MDR-TB management begins with early diagnosis and DST, but equally incorporates stable access to the second-line drugs, treatment literacy, individualized education and counseling, and efforts to meet the medical, economic, and social needs of patients throughout their treatment course.[16] Alongside the push to tailor treatment regimens via DST, we cannot ignore the commensurate opportunity to refine current mechanisms of ensuring adherence and move beyond directly observed therapy-centered models of patient support. The imposition of a standardized empiric drug regimen for MDR-TB, while feasible in lower burden settings with a more homogenous epidemic, may compound the existing challenges to MDR-TB management in settings with diverse patterns of TB drug resistance. It is time we shed universal dogmas when confronting strains of TB that are unpredictable, inconsistent, and increasingly untreatable. Given the profile of MDR-TB cases in Mumbai, DST-guided, individualized therapy is the safest option for patients and better aligned with the new End TB Strategy that calls for all countries to offer universal DST to all TB patients, at the time of diagnosis.[17] To meet the End TB Strategy goals, however, India must invest more in TB care and control, increase the budget of the Revised National TB Control Program, roll-out improved molecular diagnostics and daily drug regimens and scale-up successful models of private sector engagement.[18]

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,008
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,043
Score d'incertitude au seuil0,952

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,036
Tête enseignante GPT0,359
Écart entre enseignants0,322 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2016
Routes d'admission1
Résumé présentoui

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