Prevalence of Human Immunodeficiency Virus-1 Integrase Strand Transfer Inhibitor Resistance in British Columbia, Canada Between 2009 and 2016: A Longitudinal Analysis
Bibliographic record
Abstract
Abstract Background Integrase strand transfer inhibitors (INSTIs) are highly efficacious and well tolerated antiretrovirals with fewer adverse side-effects relative to other classes of antiretrovirals. The use of INSTIs raltegravir, elvitegravir, and dolutegravir has increased dramatically over recent years. However, there is limited information about the evolution and prevalence of INSTI resistance mutations in clinical human immunodeficiency virus populations. Methods Human immunodeficiency virus-1-positive individuals ≥19 years were included if they received ≥1 dispensed prescription of antiretroviral therapy (ART) in British Columbia between 2009 and 2016 (N = 9358). Physician-ordered drug resistance tests were analyzed and protease inhibitor (PI), reverse-transcriptase inhibitor (RT), and INSTI resistance were defined as having ≥1 sample with a combined, cumulative score ≥30 by Stanford HIV Drug Resistance Algorithm version 7.0.1. Results Although most ART-treated individuals were tested for PI and RT resistance, INSTI resistance testing lagged behind the uptake of INSTIs among INSTI-treated individuals (11% in 2009; 34% in 2016). The prevalence of INSTI resistance was relatively low, but it increased from 1 to 7 per 1000 ART-treated individuals between 2009 and 2016 (P < .0001, R2 = 0.98). Integrase strand transfer inhibitor resistance mutations increased at integrase codons 66, 97, 140, 148, 155, and 263. Conclusions The prevalence of INSTI resistance remains low compared with PI and RT resistance in ART-treated populations but is expanding with increased INSTI use.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".