Acute kidney injury in COVID-19 patients receiving remdesivir: A systematic review and meta-analysis of randomized clinical trials
Bibliographic record
Abstract
OBJECTIVES: Remdesivir is an antiviral agent with positive effects on the prognosis of Coronavirus Disease (COVID-19). However, there are concerns about the detrimental effects of remdesivir on kidney function which might consequently lead to Acute Kidney Injury (AKI). In this study, we aim to determine whether remdesivir use in COVID-19 patients increases the risk of AKI. METHODS: PubMed, Scopus, Web of Science, the Cochrane Central Register of Controlled Trials, medRxiv, and bioRxiv were systematically searched until July 2022, to find Randomized Clinical Trials (RCT) that evaluated remdesivir for its effect on COVID-19 and provided information on AKI events. A random-effects model meta-analysis was conducted and the certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation. The primary outcomes were AKI as a Serious Adverse Event (SAE) and combined serious and non-serious Adverse Events (AE) due to AKI. RESULTS: This study included 5 RCTs involving 3095 patients. Remdesivir treatment was not associated with a significant change in the risk of AKI classified as SAE (Risk Ratio [RR]: 0.71, 95% Confidence Interval [95% CI] 0.43‒1.18, p = 0.19, low-certainty evidence) and AKI classified as any grade AEs (RR = 0.83, 95% CI 0.52‒1.33, p = 0.44, low-certainty evidence), compared to the control group. CONCLUSION: Our study suggested that remdesivir treatment probably has little or no effect on the risk of AKI in COVID-19 patients.
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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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".