Abstract
INTRODUCTION: To date, acquired resistance to second-line antituberculosis drugs (SLDs) during multi-drug resistant tuberculosis (MDR-TB) treatment is becoming a public health concern. Different studies have assessed the incidence of acquired resistance to SLDs. However, the findings are inconsistent and there is limited global evidence. Thus, we are going to assess the incidence and predictors of acquired resistance to SLDs during MDR-TB treatment. METHODS AND ANALYSIS: We designed this protocol following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Electronic databases and grey literature sources will be searched systematically for articles published up to 25 March 2023. Studies reporting the incidence and predictors of acquired resistance to SLDs in MDR-TB patients will be explored. The studies will be managed using Endnote X8 citation manager and a stepwise approach will be followed to select studies. Data will be summarised using Microsoft Excel 2016 spreadsheet. A Newcastle-Ottawa Scale quality assessment and cochrane risk-of-bias tools will be used to assess the study's quality. The authors will independently search databases, select studies, assess the study's quality and extract data. Data will be analysed using STATA V.17 software. We will estimate the pooled incidence of acquired resistance with 95% CI. In addition, the pooled effect measures (OR, HR, risk ratio) with their 95% CI will be estimated. Heterogeneity will be assessed using the I(2) statistics. Publication bias will be assessed using funnel plot and Egger's test. A subgroup analysis will be conducted for the primary outcome (acquired resistance) per each study characteristics such as WHO regional category, country's TB/MDR-TB burden, data collection period and per the specific second-line anti-TB drug. ETHICS AND DISSEMINATION: Since this study will be based on data extraction from published studies, ethical approval is not mandatory. The study will be published in peer-reviewed scientific journals and the findings will be presented at different scientific conferences. PROSPERO REGISTRATION NUMBER: CRD42022371014.