Concordance of three approaches for operationalizing outcome definitions for multidrug-resistant TB
Zeng C. Mitnick C.D. Hewison C. Bastard M. Khan P. Seung K.J. Rich M.L. Atwood S. Melikyan N. Morchiladze N. Khachatryan N. Khmyz M. Restrepo C.G. Salahuddin N. Kazmi E. Dahri A.A. Ahmed S. Varaine F. Vilbrun S.C. Oyewusi L. Gelin A. Tintaya K. Yeraliyeva L.T. Hamid S. Khan U. Huerga H. Franke M.F.
1 January 2023International Union Against Tuberculosis and Lung Disease
International Journal of Tuberculosis and Lung Disease
2023#27Issue 134 - 40 pp.
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 multicountry 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.
definition , drug-resistant tuberculosis , rifampin-resistant tuberculosis , treatment outcome
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Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, United States
Partners In Health (PIH), Boston, MA, United States
Division of Global Health Equity, Brigham and Women’s Hospital, Boston, MA, United States
Medical Department, Médecins Sans Frontières (MSF), Paris, France
Field Epidemiology Department, Epicentre, Paris, France
Interactive Research and Development Global, Singapore
Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
MSF, Sokhumi, Georgia
MSF, Yerevan, Armenia
MSF, Minsk, Belarus
MSF, Yangon, Myanmar
Indus Hospital & Health Network (IHHN), Karachi, Pakistan
Center for Disease Control and Prevention, Directorate General Health Services, Sindh, Pakistan
Interactive Research and Development, Karachi, Pakistan
Haitian Group for the Study of Kaposi’s Sarcoma and Opportunistic Infections (GHESKIO), Port-au-Prince, Haiti
PIH, Maseru, Lesotho
Zanmi Lasante, Port-au-Prince, Haiti
PIH/Socios En Salud Sucursal Peru, Lima, Peru
National Scientific Center of Phthisiopulmonology, The Ministry of Health of the Republic of Kazakhstan, Kazakhstan
Bishoftu General Hospital, Bishoftu, Ethiopia
Department of Global Health and Social Medicine
Partners In Health (PIH)
Division of Global Health Equity
Medical Department
Field Epidemiology Department
Interactive Research and Development Global
Clinical Research Department
MSF
MSF
MSF
MSF
Indus Hospital & Health Network (IHHN)
Center for Disease Control and Prevention
Interactive Research and Development
Haitian Group for the Study of Kaposi’s Sarcoma and Opportunistic Infections (GHESKIO)
PIH
Zanmi Lasante
PIH/Socios En Salud Sucursal Peru
National Scientific Center of Phthisiopulmonology
Bishoftu General Hospital
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