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Second-line drug resistance markers as proxy indicators of sputum culture conversion for samples tested in Uganda

Cite this dataset

Mujuni, Dennis et al. (2023). Second-line drug resistance markers as proxy indicators of sputum culture conversion for samples tested in Uganda [Dataset]. Dryad.


Increased TB disease burden arises as a result of low treatment success rates stemming from the emergence of second-line drug (SLD) resistance. We aimed at determining the usefulness of SLD resistance markers as proxy indicators of time to sputum culture conversion; a renowned predictor of Tuberculosis (TB) treatment outcome, among SLD-resistant TB patients tested at the Uganda National TB Reference Laboratory (NTRL). A cross-sectional study was conducted on 72 bacteriologically confirmed SLDR TB patients with datasets including culture conversion time and second-line probe assay mutation profiles between 01/06/2017 and 31/12/2019. The data were then imported into STATA v15 for descriptive statistical analysis, Univariate cox proportional hazard model analysis and Kaplan-Meier survival curves at a 5% level of significance; p-value ≤ 0.05. Results indicate the median time was achieved at 3 (0–12) months across the studied patients. The rrs G1484T mutation associated with conferring drug resistance to aminoglycosides was observed to have the shortest median conversion time of 1.5 months and the longest by the gryB E540D at 5 months. A single mutation in the gryA gene locus showed higher converted proportions 70.8% (58.9–81.0) than those that had two 8.3% (3.1–17.3) or three 2.7% (0.3–10.0) mutations. We conclude that the studied second-line drug resistance markers had no statistically significant association with the time to sputum culture conversion, although increased drug resistance levels reduced the converted proportions and stressed the need to utilize molecular diagnostics data to better comprehend proxy indicators of SLD-resistant tuberculosis management.


The TB patients whose drug resistance status was previously analyzed following the national diagnostic algorithm were re-evaluated using their respective line probe assay DNA strips as previously discussed in our paper (Mujuni et al., 2022). This was followed by the addition of resistance marker data which were manually curated respectively in a protected Microsoft Excel sheet [Research Resource Identifiers (RRIDs) RRID:SCR 016137]. This sheet contained the patient datasets with corresponding monthly culture conversion time prior to cleaning to standardize mutation curations and subsequently import these results into STATA v15 (RRID:SCR_012763) for analysis. The analysis included descriptive, Univariate cox proportional hazard model analyses with the use of the Kaplan-Meier survival curves. The level of significance was set at 5% and therefore a p-value ≤ 0.05 was considered statistically significant. The data were presented in the form of summary statistic tables and figures.

Usage notes

Microsoft Excel 2016 (RRID:SCR 016137)

STATA v15 (RRID:SCR_012763)


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