Efficacy and Safety of Sofosbuvir with Simeprevir in Hepatitis C Infected Patients with Severe Chronic Kidney Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background & Aims: Conventional treatment (interferon or ribavirin) for Hepatitis C viral (HCV) infection in patients with severe chronic kidney disease (CKD) has limitations of high dropout and less response rate. Directly acting antivirals raise hopes for HCV treatment in these patients. This meta-analysis was performed to evaluate the evidence for efficacy and safety of sofosbuvir and simeprevir, with or without ribavirin, in HCV-infected patients with severe CKD. Methods: Data was collected from Medline database, clinical-trial registry sites, and conference proceedings. This meta-analysis screened 78 studies. Quality of studies was assessed by New-Castle Ottawa scale. Heterogeneity and publication bias was checked by chi-square Q test and Eggers' test, respectively. Summary estimate of SVR12 and dropout rate was calculated at 95% confidence interval (CI). Results: Seven relevant clinical studies were identified. Two case-series and one case-report were excluded; only four studies were eligible for analysis. Three studies were cohort and one was retrospective-cohort in nature. Data was analyzed for 56 subjects. 33/56 subjects had cirrhosis. 39/56 subjects were on hemodialysis. 37/56 subjects were male. 30/56 subjects were treatment-naive. Pooled estimate of SVR12 was found 0.897 (CI 95%=0.957-0.772; p<0.01) and dropout estimate was 0.040 (CI 95%=0.011-0.137; p<0.01). Only one subject discontinued the treatment due to worsening Renal function regardless of ribavirin. Conclusion: This study concluded that combination of sofosbuvir and simeprevir, with or without ribavirin, was significantly effective and safe in HCV patients with severe CKD. For conclusive results, more data is required as this study involved only limited number of subjects.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".