Acute kidney injury outcomes in covid-19 patients: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is a frequent complication of coronavirus-19 disease (COVID-19). Therefore, we decided to perform a systematic review and meta-analysis with data from the literature to relate the development of COVID-19 associated-AKI with comorbidities, medications, and the impact of mechanical ventilation. METHODS: We performed a systematic review using the Newcastle-Ottawa scale and a meta-analysis using the R program. Relevant studies were searched in the PubMed, Medline, and SciELO electronic databases. Search filters were used to include reports after 2020 and cohort studies. RESULTS: In total, 1166 articles were identified and 55 English-written articles were included based on the risk of bias. Of all COVID-19-hospitalized patients presenting with AKI (n = 18029) classified as Kidney Disease Improving Global Outcomes stage 1 to 3, approximately 18% required mechanical ventilation and 39.2 % died. Around 11.3% of the patients required kidney replacement therapy (KRT) and of these, 1093 died and 321 required continuous KRT. Death is more frequent in individuals with AKI [OR 6.03, 95%CI: 5.73-6.74; p<0.01]. Finally, mechanical ventilation is an aggravating factor in the clinical conditions studied [OR 11.01, 95%CI: 10.29-11.77; p<0.01]. CONCLUSION: Current literature indicates AKI as an important complication in COVID-19. In this context, we observed that comorbidities, such as chronic kidney disease and heart failure, were more related to the development of AKI. In addition, mechanical ventilation was seen as an aggravating factor in this scenario.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".