Open Access
Open access
International Journal of Tuberculosis and Lung Disease, volume 27, issue 1, pages 34-40

Concordance of three approaches for operationalizing outcome definitions for multidrug-resistant TB

C. Zeng 1
C D Mitnick 2
C Hewison 3
M Bastard 4
P. KHAN 5
K. J. Seung 6
M. L. Rich 6
S Atwood 7
N Melikyan 4
N. Morchiladze 8
N. Khachatryan 9
M. Khmyz 10
C. G. Restrepo 11
N Salahuddin 12
E. Kazmi 13
A. A. Dahri 13
S. Ahmed 14
F. Varaine 3
S C Vilbrun 15
L Oyewusi 16
A. Gelin 17
K Tintaya 18
L. T. Yeraliyeva 19
S HAMID 20
U. Khan 21
H Huerga 4
M. F. Franke 1
Show full list: 27 authors
3
 
Medical Department, Médecins Sans Frontières (MSF), Paris, France
4
 
Field Epidemiology Department, Epicentre, Paris, France
8
 
MSF, Sokhumi, Georgia
9
 
MSF, Yerevan, Armenia
10
 
MSF, Minsk, Belarus
11
 
MSF, Yangon, Myanmar
12
 
Indus Hospital & Health Network (IHHN), Karachi, Pakistan
13
 
Center for Disease Control and Prevention, Directorate General Health Services, Sindh, Pakistan
14
 
Interactive Research and Development, Karachi, Pakistan
15
 
Haitian Group for the Study of Kaposi´s Sarcoma and Opportunistic Infections (GHESKIO), Port-au-Prince, Haiti
16
 
PIH, Maseru, Lesotho
17
 
Zanmi LaSante, Port-au-Prince, Haiti
18
 
PIH/Socios En Salud Sucursal Peru, Lima, Peru
19
 
National Scientific Center of Phthisiopulmonology of the Ministry of Health of the Republic of Kazakhstan, Kazakhstan
20
 
Bishoftu General Hospital, Bishoftu, Ethiopia
21
 
Interactive Research and Development Global, Singapore
Publication typeJournal Article
Publication date2023-01-01
scimago Q1
SJR0.952
CiteScore4.9
Impact factor3.4
ISSN10273719, 18157920
Infectious Diseases
Pulmonary and Respiratory Medicine
Abstract

BACKGROUND: The WHO provides standardized outcome definitions for rifampicin-resistant (RR) and multidrug-resistant (MDR) TB. However, operationalizing these definitions can be challenging in some clinical settings, and incorrect classification may generate bias in reporting and research. Outcomes calculated by algorithms can increase standardization and be adapted to suit the research question. We evaluated concordance between clinician-assigned treatment outcomes and outcomes calculated based on one of two standardized algorithms, one which identified failure at its earliest possible recurrence (i.e., failure-dominant algorithm), and one which calculated the outcome based on culture results at the end of treatment, regardless of early occurrence of failure (i.e., success-dominant algorithm).METHODS: Among 2,525 patients enrolled in the multi-country endTB observational study, we calculated the frequencies of concordance using cross-tabulations of clinician-assigned and algorithm-assigned outcomes. We summarized the common discrepancies.RESULTS: Treatment success calculated by algorithms had high concordance with treatment success assigned by clinicians (95.8 and 97.7% for failure-dominant and success-dominant algorithms, respectively). The frequency and pattern of the most common discrepancies varied by country.CONCLUSION: High concordance was found between clinician-assigned and algorithm-assigned outcomes. Heterogeneity in discrepancies across settings suggests that using algorithms to calculate outcomes may minimize bias.

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