Impact of Pre-antiretroviral Therapy CD4 Counts on Drug Resistance and Treatment Failure: A Systematic Review
Bibliographic record
Abstract
The continuous rising of HIV drug resistance in low- and middle-income countries and its impact on treatment failure is a growing threat for the HIV treatment response. This review aimed to document pre-antiretroviral therapy (ART) CD4 counts, emerging drug resistance, and treatment failure in HIV-infected individuals initiating ART. We performed an online search in PubMed, Embase, Web of Science, African Index Medicus, Cochrane library, and The National Institute for Health Clinical Trials Registry of relevant articles published from January 1996 to June 2019. Of 1755 original studies retrieved, 28 were retained for final analysis. Treatment failure varied between 5% (95% confidence interval [CI]: 2.7-7.4) and 72% (95% CI: 55-89.6), while resistance varied between 1% (95% CI: 0.47-1.5) and 48% (95% CI: 28.4-67.6). Participants with a pre-ART CD4 count below 200 cell/μl and low adherence showed higher percentages of resistance and failure, while those with CD4 count above 200 showed lower resistance and failure regardless adherence levels. Most frequent resistance mutations included the M184I/V for the nucleoside reverse-transcriptase inhibitors (NRTIs), K103N, and Y181 for the non-NRTIs (NNRTIs), and L90M for the Protease inhibitors. Pre-ART CD4 count and adherence to treatment could play a key role in reducing drug resistance and treatment failure. The increased access to ART in resources limited settings should be accompanied by regular CD4 count testing, drug resistance monitoring, and continuous promotion of adherence. In addition, the rising of resistance mutations associated with NRTIs and NNRTIs, suggest that alternative ART regimens should be considered. (AIDS Rev. 2020;22: -0).
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".