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

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.

Keywords: definition; drug-resistant tuberculosis; rifampin-resistant tuberculosis; treatment outcome

Document Type: Research Article

Affiliations: 1: Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA 2: Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA, Partners In Health (PIH), Boston, MA, USA, Division of Global Health Equity, Brigham and Women´s Hospital, Boston, MA, USA 3: Medical Department, Médecins Sans Frontières (MSF), Paris, France 4: Field Epidemiology Department, Epicentre, Paris, France 5: Interactive Research and Development Global, Singapore, Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK 6: Partners In Health (PIH), Boston, MA, USA, Division of Global Health Equity, Brigham and Women´s Hospital, Boston, MA, USA 7: Division of Global Health Equity, Brigham and Women´s Hospital, Boston, MA, USA 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 date: January 1, 2023

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