Managing low-level HIV viraemia in antiretroviral therapy: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: HIV-1 management has advanced significantly with antiretroviral therapy (ART), yet challenges persist, including low-level HIV-1 viraemia (LLV). LLV presents a complex scenario, with varied definitions in the literature, reflecting uncertainties in its clinical interpretation. Questions arise regarding the underlying mechanisms of LLV, whether it signifies ongoing viral replication or stems from other factors. This study aimed to systematically review strategies for LLV management, providing insights into optimal clinical approaches. METHODS: MEDLINE, EMBASE, Cochrane Library, Web of Science and Canadian Agency for Drugs and Technologies in Health were searched for relevant literature on LLV management. We included studies published between 2004 and 2024, assessing interventions such as ART modification, genotypic resistance testing, adherence assessment, performing therapeutic drug monitoring, testing for chronic coinfections and assessing the viral reservoir via HIV DNA quantification. Meta-analyses were conducted where feasible. RESULTS: The systematic review identified 48 eligible records. Findings indicated limited evidence supporting the effectiveness of ART regimen modification in achieving virological suppression among individuals with LLV. However, studies assessing genotypic resistance testing revealed a significant association between resistance-associated mutations and virological suppression during LLV. Adherence to ART emerged as a critical determinant of treatment efficacy, with interventions showing promise in achieving viral suppression. The clinical utility of therapeutic drug monitoring in managing LLV remained inconclusive. Gaps in the literature were identified regarding follow-up scheduling, managing concurrent chronic infections and assessing inflammatory markers in LLV management. CONCLUSIONS: While ART modification may not consistently achieve virological suppression, genotypic resistance testing may offer insights into treatment outcomes. Adherence to ART emerged as a crucial factor, necessitating tailored interventions. However, further research is needed to elucidate the clinical utility of therapeutic drug monitoring and other management strategies. The study highlights the importance of ongoing research to refine therapeutic approaches and improve patient outcomes in LLV management. PROSPERO REGISTRATION NUMBER: CRD42024511492.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.030 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".