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Record W2989937673 · doi:10.9734/jpri/2019/v31i630328

Efficacy of DAA-based Antiviral Therapies for HCV Patients with Chronic Kidney Disease: A Meta-analysis

2019· article· en· W2989937673 on OpenAlexaboutno aff
Peyman Sanjari Pirayvatlou, Seyyed Moayed Alavian, Sasan Sanjari Pirayvatlou, Pouyan Sanjari Pirayvatlou, Mina Mahboodi, Madjad Einollahi

Bibliographic record

VenueJournal of Pharmaceutical Research International · 2019
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineKidney diseaseInternal medicineHepatitis C virusHepatitis CRegimenContext (archaeology)Chronic liver diseaseLiver diseaseAdverse effectImmunologyVirusCirrhosis

Abstract

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Context: HCV infection in patients with chronic kidney disease (CKD) is important to be treated because it's associated with increased healthcare costs, utilization and is pertained with decrease in survival rate of HCV-infected patients who also have chronic kidney disease. Direct acting agents (DAAs) are novel form of treatment of HCV infection in patients with CKD. The aim of this study is meta-analysis and comparison of the efficacy of different regimen of DAAs used in the treatment of HCV in such patients.
 Objective: Hepatitis C is a liver disease caused by the hepatitis C virus, the virus can cause both acute and chronic hepatitis. Hepatitis C virus (HCV) is a known risk factor for chronic kidney disease (CKD) and end-stage renal disease (ESRD). HCV infection in CKD patients is also associated with increased healthcare costs and utilization, with further increases in those with ESRD. It should be also noted that survival among HCV-infected patients with chronic kidney disease without undertaking any treatment is low, various mechanisms such as increased liver-related mortality, low quality of life and high cardiovascular risk can explain this finding. The benefits of treatment may extend beyond the liver, with improvements in both cardiovascular and renal outcomes in patient with chronic kidney disease. Previously PEG-INTERFRON Based regimens have been used for treatment of CKD or ESRD Patients with chronic Hepatitis C but this treatment plan was associated with higher adverse effects and less efficacy. Nowadays new researches have shown the efficacy of the Direct Anti-Viral Agents (DAAs) In such patients.
 Data Sources: A systematic literature searches in PubMed, EMBASE, Web of Science, and Scopus motor searches was done. Virologic response at 12 weeks after the end of treatment (SVR12) was extract from the included studies. Finally, SVR12 rate with 95% confidence intervals (CI) were pool analyzed with random-effects model.
 Study Selection: Studies were included if they satisfied the following criteria: Participants being adult HCV patients with stage 3–5 CKD (age≥18 years), Interventions being DAA-based antiviral therapies, Outcomes being sustained virologic response at 12 weeks after the end of treatment (SVR12). Studies were excluded if having incomplete outcome data and had no sufficient data to calculate SVR12.
 Data Extraction: The methodological quality of included observational studies was assessed by three reviewers independently by using the Newcastle–Ottawa scale (NOS), which is usually used for observational studies in meta-analyses.
 Results: 20 studies comprising a total of 628 patients (from 20 studies) were included for our meta-analysis. The pooled analysis for SVR12 rate was 0.95 (95% Cl 0.92-0.96, I2= 0.00%), 0.92 (95% Cl 0.82-0.96 I2= 0.00%) and 0.95 (95% Cl 0.93-0.97, I2= 0.0%) for total population, sofosbuvir base treatment group and non sofosbuvir base treatment group. 
 Conclusion: DDAs have high efficacy in treatment of HCV in patient with CKD and it seems that there is no different between sofosbuvir versus non sofosbuvir based regimens for treatment of HCV infection in this 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.204
GPT teacher head0.513
Teacher spread0.309 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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