S'abonner

Radiographic severity and treatment outcome of Mycobacterium abscessus complex pulmonary disease - 19/10/21

Doi : 10.1016/j.rmed.2021.106549 
Jimyung Park a, b, Soon Ho Yoon c, d, Joong-Yub Kim a, b, Kang-Mo Gu e, Nakwon Kwak a, b, Jae-Joon Yim a, b,
a Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea 
b Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea 
c Department of Radiology, Seoul National University Hospital, Seoul, South Korea 
d Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea 
e Department of Internal Medicine, Chung-Ang University College of Medicine, Seoul, South Korea 

Corresponding author. Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, 101, Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.Division of Pulmonary and Critical Care MedicineDepartment of Internal MedicineSeoul National University College of Medicine101, Daehak-roJongno-guSeoul03080South Korea

Abstract

Introduction

The lack of reliable predictors for the treatment response complicates decisions to initiate treatment in patients with Mycobacterium abscessus complex pulmonary disease (MABC-PD). We aimed to investigate whether baseline radiographic disease severity is associated with treatment outcome in MABC-PD.

Method

We retrospectively analyzed 101 patients with MABC-PD (54 with M. abscessus-PD and 47 with M. massiliense-PD) treated in a tertiary referral hospital between January 2006 and December 2019. Using chest computed tomography images, baseline radiographic disease severity was quantitatively scored according to five categories of radiographic lesions (bronchiectasis, bronchiolitis, cavities, nodules, and consolidation).

Results

Treatment success was achieved in 53.7% of patients with M. abscessus-PD and 85.1% of patients with M. massiliense-PD. Higher overall scores for baseline radiographic disease severity were associated with treatment failure in patients with M. massiliense-PD (aOR 1.35, 95% CI 1.02–1.79 for each 1-point increase in severity score), as well as in patients with M. abscessus-PD (aOR 1.15, 95% CI 1.00–1.33). This was particularly prominent in patients with overall severity score of ≥14 (aOR 31.16, 95% CI 1.12–868.95 for M. massiliense-PD and aOR 3.55, 95% CI 1.01–12.45 for M. abscessus-PD). Among variable radiographic abnormalities, the score for cavitary lesion severity was associated with treatment failure in patients with M. abscessus-PD (aOR 1.26, 95% CI 1.01–1.56), but not in patients with M. massiliense-PD.

Conclusions

Given the association between baseline radiographic disease severity and treatment outcome, initiating treatment should be actively considered before significant progression of radiographic lesions in patients with MABC-PD.

Le texte complet de cet article est disponible en PDF.

Highlights

There are currently no reliable predictors for treatment response in MABC-PD.
Baseline radiographic severity was associated with treatment outcome in MABC-PD.
Severity of cavitary lesion was important particularly for M. abscessus-PD.
Earlier initiation of antibiotic treatment should be considered for MABC-PD.

Le texte complet de cet article est disponible en PDF.

Keywords : Nontuberculous mycobacteria, Mycobacterium abscessus, Mycobacterium massiliense, Radiography, Computed tomography

Abbreviations : Adjusted odds ratio, Computed tomography, Confidence interval, Interquartile range, Mycobacterium abscessus complex, Mycobacterium abscessus complex pulmonary disease, Mycobacterium abscessus pulmonary disease, Mycobacterium avium complex, Mycobacterium avium complex pulmonary disease, Mycobacterium massiliense pulmonary disease, Nontuberculous mycobacteria, Nontuberculous mycobacterial pulmonary disease, Peripherally inserted central catheters, Receiver operating characteristics


Plan


© 2021  Elsevier Ltd. Tous droits réservés.
Ajouter à ma bibliothèque Retirer de ma bibliothèque Imprimer
Export

    Export citations

  • Fichier

  • Contenu

Vol 187

Article 106549- octobre 2021 Retour au numéro
Article précédent Article précédent
  • Genomic biomarkers in chronic beryllium disease and sarcoidosis
  • Nancy W. Lin, Lisa A. Maier, Margaret M. Mroz, Sean Jacobson, Kristyn MacPhail, Sucai Liu, Zhe Lei, Briana Q. Barkes, Tasha E. Fingerlin, Nabeel Hamzeh, Annyce S. Mayer, Clara I. Restrepo, Divya Chhabra, Ivana V. Yang, Li Li
| Article suivant Article suivant
  • Galectin-3 as prognostic biomarker in patients with COVID-19 acute respiratory failure
  • Andrea Portacci, Fabrizio Diaferia, Carla Santomasi, Silvano Dragonieri, Esterina Boniello, Francesca Di Serio, Giovanna Elisiana Carpagnano

Bienvenue sur EM-consulte, la référence des professionnels de santé.
L’accès au texte intégral de cet article nécessite un abonnement.

Déjà abonné à cette revue ?

Mon compte


Plateformes Elsevier Masson

Déclaration CNIL

EM-CONSULTE.COM est déclaré à la CNIL, déclaration n° 1286925.

En application de la loi nº78-17 du 6 janvier 1978 relative à l'informatique, aux fichiers et aux libertés, vous disposez des droits d'opposition (art.26 de la loi), d'accès (art.34 à 38 de la loi), et de rectification (art.36 de la loi) des données vous concernant. Ainsi, vous pouvez exiger que soient rectifiées, complétées, clarifiées, mises à jour ou effacées les informations vous concernant qui sont inexactes, incomplètes, équivoques, périmées ou dont la collecte ou l'utilisation ou la conservation est interdite.
Les informations personnelles concernant les visiteurs de notre site, y compris leur identité, sont confidentielles.
Le responsable du site s'engage sur l'honneur à respecter les conditions légales de confidentialité applicables en France et à ne pas divulguer ces informations à des tiers.


Tout le contenu de ce site: Copyright © 2024 Elsevier, ses concédants de licence et ses contributeurs. Tout les droits sont réservés, y compris ceux relatifs à l'exploration de textes et de données, a la formation en IA et aux technologies similaires. Pour tout contenu en libre accès, les conditions de licence Creative Commons s'appliquent.