Bedaquiline Adherence Measured by Electronic Dose Monitoring Predicts Clinical Outcomes in the Treatment of Patients With Multidrug-Resistant Tuberculosis and HIV/AIDS
Bibliographic record
Abstract
BACKGROUND: Novel regimens have revolutionized multidrug-resistant tuberculosis (MDR-TB) treatment; however, medication adherence remains challenging and poorly characterized. We hypothesized that bedaquiline adherence, measured using electronic dose monitoring, would predict MDR-TB treatment outcomes. SETTING: This is a prospective cohort study conducted in KwaZulu-Natal, South Africa. METHODS: Adults with MDR-TB and HIV initiating bedaquiline and on antiretroviral therapy (ART) were eligible. Separate electronic dose monitoring devices measured bedaquiline and ART adherence through 6 months, calculated as observed versus expected doses. Whole-genome sequencing was performed to identify bedaquiline resistance-associated variants. RESULTS: From November 2016 through February 2018, 199 participants with MDR-TB and HIV were enrolled and followed up through treatment completion (median 17.2 months interquartile range 12.2-19.6). The median bedaquiline adherence was higher than ART adherence (97 vs. 89%, P < 0.001) but correlated (r2 = 0.68, P < 0.001). High bedaquiline adherence (≥90%) compared with lower adherence was associated with improved end of treatment successful outcome (83.4% vs. 46.3%, P < 0.001), decreased mortality (11.0% vs. 29.6% P = 0.004), and improved retention in care through end of treatment (94.5% vs. 79.6% P = 0.002). Modeling identified a highly significant but linear association between bedaquiline adherence and outcome. On multivariable analysis, bedaquiline adherence was independently associated with mortality and outcome. Bedaquiline resistance-associated variants were seen in 12% (7/57) of sequenced isolates (7% baseline, 5% emergent) with only 28.6% experiencing successful treatment outcome. CONCLUSIONS: Bedaquiline adherence through 6 months independently predicted end of MDR-TB treatment outcome, but a specific bedaquiline adherence threshold was not identified. Interventions to optimize bedaquiline adherence are urgently needed to improve MDR-TB HIV treatment outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".