Acute Kidney Injury in Adults due to COVID-19 infection: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: In COVID-19 infection, Acute Kidney Injury (AKI) poses a severe complication. To help clinicians to implement effective clinical therapy, we systematically documented evidence on AKI incidence and associated death in COVID-19 infection among adult populations. Methods: For the systematic review and meta-analysis Guidelines laid by Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) were followed. Studies were searched from Pubmed and MedRxiv databases published between December 2019 - June 19, 2021. Results: 55 out of 3175 total articles were identified as eligible for the qualitative review with 32 reports on adults (n=169560); mean age 65.34 years. Studies on AKI incidence, mortality, and AKI-related mortality in COVID-19-infected adults p were included in the meta-analysis. Estimated AKI incidence in adults was 16.4% ([95%CI, 12.8 – 20.0%], I2 =99.39%, P<0.001). Moreover, overall death 34.8% ([95%CI, 21.3- 48.3%], I2 = 99.88%, P<0.001) and AKI-related death 68.4% ([95%CI, 55.9–81.0%], I2=99.2%, P<0.001) among adults revealed high statistical heterogeneity. Conclusion: AKI- related mortality was significantly high in COVID-19-infected adults. Therefore, AKI is common in COVID-19 infection and clinical management must be accordingly formulated.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.047 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".