Prevalence and determinants of resistance mutations in <scp>HIV</scp>‐1‐infected patients exposed to integrase inhibitors in a large Italian cohort
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to analyse the prevalence of integrase resistance mutations in integrase strand transfer inhibitor (INSTI)-experienced HIV-1-infected patients and its predictors. METHODS: test and predictors of resistance were analysed by logistic regression. RESULTS: We included 462 genotypes from INSTI-exposed individuals: 356 'INSTI-failing' patients and 106 'previously INSTI-exposed' patients (obtained a median of 42 weeks after INSTI discontinuation [interquartile range (IQR) 17-110 weeks]). Overall, at least low-level resistance (LLR) to any INSTI (Stanford 8.5 algorithm) was detected in 198 (42.9%) cases. The most frequent INSTI resistance mutation was N155H, followed by Q148H/K/R, G140A/C/S, E138A/K/T and Y143C/H/R. Y143R and E138A were more prevalent in viral subtype B versus non-B [5.2 versus 1.5%, respectively (P = 0.04), and 3.1 versus 0%, respectively (P = 0.02)]. Overall, the Q148H/K/R plus G140A/C/S and/or E138A/K/T pattern, defining an intermediate level of resistance to DTG, was detected in 70 (15%) cases. Independent predictors of at least LLR to any INSTI were current use versus past use of INSTIs, a lower genotypic sensitivity score (GSS) for contemporary antiretroviral drugs used, and having an integrase sequence obtained in calendar year 2016 as compared to 2008-2009. CONCLUSIONS: The results support integrase resistance testing in INSTI-experienced patients. Emergence of INSTI resistance is facilitated by the reduced genetic barrier of the regimen as a consequence of resistance to companion drugs. However, INSTI resistance may become undetectable by standard population sequencing upon INSTI discontinuation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".