Kidney Complications of COVID-19: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Some patients with coronavirus disease 2019 (COVID-19) have been reported to have developed mild to severe kidney injuries. The current systematic review and meta-analysis was carried out to estimate the prevalence and incidence of acute kidney injury (AKI) among COVID-19 patients. STUDY DESIGN: A systematic review and meta-analysis . METHODS: PubMed, Embase, Scopus, Web of Science, and MedRxiv databases were searched from December 1, 2019, up to July 27, 2020. Two independent co-authors completed the screening process, data extraction, and quality assessment of the retrieved records. Random-effects meta-analyses were used to determine the pooled prevalence and 95% confidence interval (CI) of AKI among COVID-19 patients. RESULTS: Out of 2,332 unique identified records, 51 studies were included in the review. Overall, the studies were carried out on 25,600 patients. A total of 6,505 patients (in 18 cross-sectional studies) were included to estimate the pooled prevalence of AKI, and 18,934 patients (in 27 cohort studies) were included to determine the pooled incidence of AKI. The pooled prevalence of AKI was estimated as 10.08% (95% CI: 4.59, 17.32; I2=98.56%; P<0.001). Furthermore, the pooled incidence of AKI was 12.78% (95% CI: 7.38, 19.36; I2=99.27%; P<0.001). The mean (95% CI) values of serum creatinine (SCr), blood urea nitrogen (BUN), potassium, and sodium were 76.10 (69.36, 82.84), 4.60 (4.04, 5.30), 3.94 (3.78, 4.11), and 139.30 (138.26, 140.36) mmol/L, respectively. CONCLUSION: The AKI is a considerable complication among COVID-19 patients and should be screened for on clinical examinations. The BUN, SCr, potassium, and sodium levels were within the normal ranges.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.007 | 0.008 |
| 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".