P3-S3.12 Transmitted HIV drug resistance mutations in Ontario, Canada, 2002–2009
Bibliographic record
Abstract
Background We estimated the prevalence of transmitted HIV drug resistance (TDR) among HIV-positive outpatients in Ontario, Canada who were diagnosed since 2002 when the provincial laboratories started testing for drug resistance among treatment-naïve patients. Methods We analysed data from the Ontario HIV Treatment Network Cohort Study, a multi-site open dynamic cohort of people living with HIV. Participants were recruited from specialised HIV clinics and primary care practices. Data were obtained from medical chart extractions, interviews and linked with data from the Ontario Public Health Laboratories, which performs almost all viral load and genotypic resistance testing (GRT). We analysed data from participants who received GRT testing while treatment-naïve, defined as (1) no record of antiretroviral use in their clinical chart and (2) detectable viral load. We used the Stanford University HIV Drug Resistance Database to identify TDR mutations in the viral sequences of the protease and reverse transcriptase genes. We used descriptive statistics to characterise the prevalence of TDR mutations and report results with 95% CI. Results Among 623 persons diagnosed in 2002–2009, 330 received GRT while treatment naïve. Among those tested, the mean age was 39 (SD 9.9); 12% were female, 65% men who have sex with men (MSM), and 66% white. The median baseline viral load count was 4.5 log10 copies/ml (IQR 3.9–5.0) and the median baseline CD4 count was 399 cells/mm3(IQR 240–540). Overall, 13.6% (CI 9.9 to 17.3%) had one or more drug resistance mutations, and 8.8% (CI 5.7 to 11.8%), 4.8% (CI 2.5 to 7.2%) and 2.7% (CI 1.0 to 4.5%) had mutations conferring resistance against nucleoside/tide reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), or protease inhibitors (PIs), respectively. TDR against two or more drug classes was observed in 2.7% (CI 1.0 to 4.5%). The most common mutations were T215 revertants, M41L, and K103N in the RT gene. The proportion with TDR was highest among IDU (30.4%), intermediate among MSM and heterosexuals (12.0% and 14.3%, respectively) and lowest among persons from HIV endemic regions (6.9%) (p=0.06). Participants diagnosed in 2008–2009 had a higher proportion of NRTI mutations (18.2% vs 5.9%, p=0.0009) and NNRTI mutations (11.7% vs 2.8%, p=0.004) than those diagnosed earlier; such increases were observed among MSM, heterosexuals and IDU. There was no evidence of a change in PI mutation frequency over time. Conclusion Our finding of a recent increase in NRTI and NNRTI mutations is concerning but requires confirmation, ideally in a random sample of specimens from newly diagnosed individuals. Although the actual dates of infection were unknown, the results suggest that drug-resistant strains are commonly circulating within the established HIV epidemics in Ontario.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".