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Record W4399134399 · doi:10.1101/2024.05.28.24307936

Antivirals for treatment of non-severe influenza: a systematic review and network meta-analysis of randomized controlled trials

2024· review· en· W4399134399 on OpenAlexaff
Ya Gao, Yunli Zhao, Ming Liu, Shuyue Luo, Yamin Chen, Xiaoyan Chen, Qingyong Zheng, Jianguo Xu, Yanjiao Shen, Wanyu Zhao, Zhifan Li, Sha Huang, Jie Huang, Jinhui Tian, Gordon Guyatt, Qiukui Hao

Bibliographic record

VenuemedRxiv · 2024
Typereview
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsMeta-analysisRandomized controlled trialMedicineIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Summary Background The optimal antiviral drug for treatment of non-severe influenza remains unclear. To support an update of WHO guidelines on antiviral treatment for influenza, this systematic review compared effects of antiviral drugs for treating non-severe influenza. Methods We systematically searched Medline, Embase, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature, Global Health, Epistemonikos, and ClinicalTrials.gov for randomized controlled trials published between database inception and 20 September 2023, comparing direct-acting influenza antiviral drugs, including but not limited to baloxavir, favipiravir, laninamivir, oseltamivir, peramivir, umifenovir, and zanamivir, to placebo, standard care, or another antiviral drug for treating people with non-severe influenza. We performed frequentist network meta-analyses to summarize the evidence and evaluated the certainty of evidence using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. We registered the protocol with PROSPERO, CRD42023456650. Findings We identified 11878 records, of which 73 trials with 34332 participants proved eligible. Compared with standard care or placebo, all antiviral drugs have little or no effect on mortality for low-risk patients (risk difference (RD) varied from 0.12 fewer to 0.02 fewer per 1000) and high-risk patients (RD varied from 1.22 fewer to 0.24 fewer per 1000) (all high certainty). All antivirals (no data for peramivir and amantadine) have little or no effect on admission to hospital (RD varied from 2 fewer to 1 more per 1000) for low-risk patients (high certainty). With respect to hospital admission, for high-risk patients, oseltamivir (RD 4 fewer per 1000, 95% CI 10 fewer to 4 more; high certainty) and zanamivir (RD 4 more per 1000, 95% CI 4 fewer to 15 more; high certainty) have little or no effect; baloxavir may reduce risk (RD 16 fewer per 1000, 95% CI 20 fewer to 4 more; low certainty); all other drugs may have little or uncertain effect. For time to alleviation of symptoms, baloxavir probably reduces symptom duration (mean difference (MD) 1.02 days lower, 95% CI 1.41 lower to 0.63 lower; moderate certainty); umifenovir may reduce symptom duration (MD 1.10 days lower, 95% CI 1.57 lower to 0.63 lower; low certainty); oseltamivir probably has no important effect (MD 0.75 days lower, 95% CI 0.93 lower to 0.57 lower, moderate certainty) and other drugs may have no important or little effect. For adverse events related to treatment, baloxavir (RD 32 fewer per 1000, 95% CI 52 fewer to 6 fewer; high certainty) has few or no such events; oseltamivir (RD 28 more per 1000, 95% CI 12 more to 48 more; moderate certainty) probably increases such events; other drugs may have little or no effect, or uncertain effect. Interpretation Baloxavir may reduce the risk of hospital admission for high-risk patients and probably reduces time to alleviation of symptoms, without increasing adverse events related to treatment in patients with non-severe influenza. All other antivirals either probably have little or no effect, or uncertain effects on patient-important outcomes. Funding WHO. Research in context Evidence before this study Antiviral drugs may play a role in reducing illness duration, preventing serious complications, and lowering morbidity, particularly in high-risk populations. Previous systematic reviews and network meta-analyses have assessed the effects of antiviral drugs for treating influenza, but none assessed all approved antivirals for influenza or addressed patient-important outcomes of mortality and admission to hospital. The effect of many antiviral drugs for treating patients with non-severe influenza remains uncertain. Added value of this study This systematic review and network meta-analysis represents the most comprehensive assessment of the benefits and harms of antivirals in treating patients with non-severe influenza and demonstrates that baloxavir may reduce the risk of admission to hospital for high-risk patients and probably reduces time to alleviation of symptoms, does not increase adverse events related to treatment, but may increase emergence of resistance. Oseltamivir has little or no effect on mortality and admission to hospital, probably has no important effect on time to alleviation of symptoms, and probably increases adverse events related to treatments. Other antivirals probably have little or no effect on mortality and admission to hospital and may have no important effect on time to alleviation of symptoms. Implications of all the available evidence Our study provides evidence that baloxavir may be superior to standard care or placebo in reducing the risk of admission to hospital for high-risk patients and probably decreases time to alleviation of symptoms with few or no adverse effects. These findings support the use of baloxavir for treatment of high-risk non-severe influenza patients.

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)0.037
metaresearch head score (Gemma)0.085
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: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.037
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.085
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0270.040
Bibliometrics0.0120.010
Science and technology studies0.0010.001
Scholarly communication0.0040.003
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 head0.364
GPT teacher head0.509
Teacher spread0.145 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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