Acute Kidney Injury and Renal Replacement Therapy in COVID-19 Versus Other Respiratory Viruses: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) is a potentially fatal complication of Coronavirus Disease-2019 (COVID-19). Binding of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, to its viral receptor, angiotensin converting enzyme 2 (ACE2), results in viral entry and may cause AKI. Objectives: We performed a systematic review and meta-analysis of the frequencies of AKI and renal replacement therapy (RRT) in critically ill COVID-19 patients and compared those frequencies with patients who were infected by respiratory viruses that bind or downregulate ACE2 (ACE2-associated viruses) and viruses that do not bind nor downregulate ACE2 (non-ACE2-associated viruses). Design: Systematic review and meta-analysis. Setting: Observational studies on COVID-19 and other respiratory viral infections reporting AKI and RRT were included. The exclusion criteria were non-English articles, non-peer-reviewed articles, review articles, studies that included patients under the age of 18, studies including fewer than 10 patients, and studies not reporting AKI and RRT rates. Patients: Adult COVID-19, Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and influenza patients. Measurements: We extracted the following data from the included studies: author, year, study location, age, sex, race, diabetes mellitus, hypertension, chronic kidney disease, shock, vasopressor use, mortality, intensive care unit (ICU) admission, ICU mortality, AKI, and RRT. Methods: We systematically searched PubMed and EMBASE for articles reporting AKI or RRT. AKI was defined by authors of included studies. Critical illness was defined by ICU admission. We performed a random effects meta-analysis to calculate pooled estimates for the AKI and RRT rate within each virus group using a random intercept logistic regression model. Results: Of 23 655 hospitalized, critically ill COVID-19 patients, AKI frequencies were not significantly different between COVID-19 patients (51%, 95% confidence interval [CI]: 44%-57%) and critically ill patients infected with ACE2-associated (56%, 95% CI: 37%-74%, P = .610) or non-ACE2-associated viruses (63%, 95% CI: 43%-79%, P = .255). Pooled RRT rates were also not significantly different between critically ill, hospitalized patients with COVID-19 (20%, 95% CI: 16%-24%) and ACE2-associated viruses (18%, 95% CI: 8%-33%, P = .747). RRT rates for both COVID-19 and ACE2-associated viruses were significantly different ( P < .001 for both) from non-ACE2-associated viruses (49%, 95% CI: 44%-54%). After adjusting for shock or vasopressor use, AKI and RRT rates were not significantly different between groups. Limitations: Limitations of this study include the heterogeneity of definitions of AKI that were used across different virus studies. We could not match severity of infection or do propensity matching across studies. Most of the included studies were conducted in retrospective fashion. Last, we did not include non-English publications. Conclusions: Our findings suggest that viral ACE2 association does not significantly alter the rates of AKI and RRT among critically ill patients admitted to the ICU. However, the rate of RRT is lower in patients with COVID-19 or ACE2-associated viruses when compared with patients infected with non-ACE2-binding viruses, which might partly be due to the lower frequencies of shock and use of vasopressors in these two virus groups. Prospective studies are necessary to demonstrate whether modulation of the ACE2 axis with Renin-Angiotensin System inhibitors impacts the rates of AKI and whether they are beneficial or harmful 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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| 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".