A systematic review and meta-analysis of the risk of hepatitis B virus (HBV) resistance in people treated with entecavir or tenofovir
Bibliographic record
Abstract
BACKGROUND: As nucleos/tide analogue (NA) therapy (e.g. entecavir and tenofovir) for chronic Hepatitis B virus (HBV) infection becomes more widely indicated and available, understanding drug resistance is essential. A systematic review to quantify resistance to these agents has not previously been undertaken. METHODS: We performed a systematic review and random-effects meta-analysis to estimate the risk of HBV resistance to entecavir and tenofovir. We searched nine databases up to 29-Aug-23. We included studies of HBV infection featuring >10 individuals, written in English, reporting treatment ≥48 weeks, with assessment of HBV resistance based on viral sequence data. Data were analysed according to prior exposure history to NA, and choice of NA agent. Analyses were performed in R. FINDINGS: 62 studies involving a total of 12,358 participants were included. For entecavir, in treatment-naive individuals (22 studies; 4326 individuals), resistance increased over time to 0.9 % at ≥5 years (95 %CI 0.1-2.3 %), and resistance was increased in NA-experienced individuals (18 studies; 1112 individuals), to 20.1 % (95 %CI 1.6-50.1 %) at ≥5 years. For tenofovir, pooled resistance risk was 0.0 % at all time points, whether previously NA naive (11 studies; 3778 individuals) or experienced (19 studies; 2059 individuals). There was a lack of consistent definitions, poor global representation and insufficient metadata to support subgroup analysis. INTERPRETATION: We have generated the first pooled estimates of HBV entecavir and tenofovir resistance over time. HBV resistance to entecavir in treatment-experienced groups in particular may represent a clinical and public health challenge. To date, tenofovir appears to have an excellent resistance profile, but due to data gaps, we caution that existing studies under-estimate the true real-world risk of resistance. Robust prospective data collection is crucial to reduce health inequities and reduce blind-spots in surveillance as treatment is rolled out more widely.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.028 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".