Emergent drug resistance with integrase strand transfer inhibitor-based regimens
Bibliographic record
Abstract
OBJECTIVES: To estimate the incidence of and risk factors for emergent resistance to integrase strand transfer inhibitor (INSTI) and nucleoside(-tide) reverse transcriptase inhibitors (NRTI) in HIV-1-infected adults receiving an INSTI and two NRTIs. DESIGN: Retrospective cohort study. METHODS: Persons aged at least 19 years were included if they received their first prescription for raltegravir, elvitegravir or dolutegravir in British Columbia, Canada in 2012-2014 and were followed to 31 December 2015. Emergent resistance was defined as new mutations conferring intermediate-high level NRTI or INSTI resistance (score ≥30, Stanford HIV Drug Resistance Algorithm v.7.0.1). First-year resistance rates and 95% confidence intervals (95% CI) were estimated for 'any' (INSTI or NRTI) resistance using Poisson regression. The relationship between any emergent resistance and explanatory variables was modeled by Cox proportional hazards. RESULTS: There were 270 raltegravir, 323 elvitegravir and 392 dolutegravir-treated persons who were predominantly male (77%), antiretroviral therapy (ART)-experienced (81%), with low prevalence of preexisting drug resistance (16%). INSTI and NRTI resistance emerged in both ART-experienced and ART-naive persons (including dolutegravir-treated ART-naive), with no statistically significant differences in 'any' resistance rates (95% CI) between INSTIs: raltegravir 3.80 (1.90, 7.60), elvitegravir 2.37 (1.06, 5.27) and dolutegravir 1.48 (0.62, 3.55)/100 person-years. The strongest factors associated with emergent resistance were CD4 less than 200 cells/μl, adjusted hazard ratio (95% CI) 10.46 (4.67, 23.41) and less than 80% adherence to the INSTI regimen hazard ratio 2.52 (1.11, 5.71). CONCLUSION: Incident drug resistance rates were low with 'real-world' use of INSTI-based regimens. However, incomplete ART adherence and low CD4 cell count were associated with increased resistance rates regardless of which INSTI was prescribed. Provide adherence support and monitor for drug resistance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".