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Record W4380485347 · doi:10.2196/46767

Safety and Efficacy of Long-Acting Injectable Agents for HIV-1: Systematic Review and Meta-Analysis

2023· review· en· W4380485347 on OpenAlexvenueno aff
Wenjing Wang, Shengnan Zhao, Yaxin Wu, Wenshan Duan, Sibo Li, Zhen Li, Caiping Guo, Wen Wang, Tong Zhang, Hao Wu, Xiaojie Huang

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsnot available
FundersNational Science and Technology Major ProjectBeijing Municipal Health Commission
KeywordsMeta-analysisHuman immunodeficiency virus (HIV)MedicineSystematic reviewRisk analysis (engineering)MEDLINEIntensive care medicineBiologyVirologyInternal medicine

Abstract

fetched live from OpenAlex

Background HIV-1 infection continues to affect global health. Although antiretrovirals can reduce the viral load or prevent HIV-1 infection, current drugs require daily oral use with a high adherence level. Long-acting antiretrovirals (LA-ARVs) significantly improve medication adherence and are essential for HIV-1 prophylaxis and therapy. Objective This study aimed to investigate the safety and efficacy of long-acting cabotegravir (CAB-LA) and long-acting rilpivirine (RPV-LA) in the prevention and treatment of HIV-1 infection. Methods PubMed, Embase, and the Cochrane Library were searched for studies from database inception to November 12, 2022. We included studies that reported efficacy and safety data on LA-ARV intervention in people living with HIV and excluded reviews, animal studies, and articles with missing or duplicate data. Virological suppression was defined as plasma viral load <50 copies/mL 6 months after antiviral therapy initiation. We extracted outcomes for analysis and expressed dichotomous data as risk ratios (RRs) and continuous data as mean differences. Depending on the heterogeneity assessment, a fixed- or random-effects model was used for data synthesis. We performed subgroup analyses of the partial safety and efficacy outcomes of CAB-LA+RPV-LA. The protocol was registered with the Open Science Framework. Results We included 12 trials comprising 10,957 individuals, of which 7 were prevention trials and 5 were treatment trials. CAB-LA and RPV-LA demonstrated safety profiles comparable with those of the placebo in terms of adverse event–related withdrawal. Moreover, the efficacy data showed that CAB-LA had a better effect on HIV-1 prevention than tenofovir disoproxil fumarate–emtricitabine (17/5161, 0.33% vs 75/5129, 1.46%; RR 0.21, 95% CI 0.07-0.61; I2=70%). Although CAB-LA+RPV-LA had more drug-related adverse events (556/681, 81.6% vs 37/598, 6.2%; RR 12.50, 95% CI 3.98-39.23; I2=85%), a mild or moderate injection site reaction was the most common reaction, and its frequency decreased over time. The efficacy of CAB-LA+RPV-LA was comparable with that of daily oral drugs at 48 and 96 weeks (1302/1424, 91.43% vs 915/993, 92.2%; RR 0.99, 95% CI 0.97-1.02; I2=0%), and a high level of virological suppression of 80.9% (186/230) was maintained even after 5 years of LA-ARV use. Similar efficacy outcomes were observed in both treatment-naive and treatment-experienced patients (849/911, 93.2% vs 615/654, 94%; RR 0.99, 95% CI 0.96-1.02; I2=0%). According to the questionnaires, more than 85% of people living with HIV favored LA-ARVs. Conclusions LA-ARVs showed favorable safety profiles for both the prevention and treatment of HIV-1 infection and were well tolerated. CAB-LA has more satisfactory efficacy than tenofovir disoproxil fumarate–emtricitabine, significantly reducing the rate of HIV-1 infection. CAB-LA+RPV-LA maintains virological suppression for a long time and may be a viable switching strategy with enhanced public health benefits by reducing transmission. However, further trials are required to confirm the efficacy of these drugs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.025
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.041
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0250.038
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.156
GPT teacher head0.412
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations32
Published2023
Admission routes1
Has abstractyes

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