COVID-19 and CKD: An Overview of Reviews to Inform the World Health Organization Scientific Brief
Bibliographic record
Abstract
Background: The World Health organization (WHO) declared COVID-19 as a global pandemic in March of 2020. Many studies have assessed the association between different comorbidities and COVID-19 outcomes. In this overview of reviews, we aim to summarize the association between CKD and different COVID-19 outcomes. Methods: We performed a systematic search through Embase, PubMed, Epistemonikos, and Cochrane as well as preprint databases from January 1, 2020 to January 5, 2021. After searching systematic reviews, we updated the search by identifying primary studies published after August 2020, which was the date of last search in the reviews. We focused on systematic reviews and large primary studies. We followed the GRADE methodology to assess the certainty in effect estimates. Data was pooled based on random effects model. Results: We included a total of 69 systematic reviews and 66 primary studies in our overview. We did not identify any systematic reviews that directly reports on CKD and the risk of contracting COVID-19. There was no convincing difference on the risk of acquiring COVID-19 infection in patients with and without CKD in primary studies (OR = 1.00, 95% CI 0.76-1.33). CKD is associated with higher risk of COVID-19 related mortality pooled hazard ratio (HR) 1.48 (95% CI 1.33-1.65) and pooled odds ratio (OR) 1.77 (95% CI 1.54-2.02)(moderate certainty), hospitalization pooled risk ratio (RR) 1.63 (95% CI 1.03-2.58) (moderate certainty) and disease severity pooled RR 1.56 (95% CI 1.3-1.86) (moderate certainty). Notably, the risk of COVID-19 attributed hospitalization and mortality were higher in patients with more advanced CKD stage. Conclusions: Evidence consistently demonstrated an increased risk of mortality, hospitalization and disease severity in patients with CKD and COVID-19 infection. The results highlight the importance of recognizing patients with CKD as a high-risk group and of prioritizing these patients for COVID-19 prevention strategies including vaccination.
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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 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.033 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".