COVID–19 and chronic kidney disease: an updated overview of reviews
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease (COVID-19) has resulted in the death of more than 3.5 million people worldwide. While COVID-19 mostly affects the lungs, different comorbidities can have an impact on its outcomes. We performed an overview of reviews to assess the effect of Chronic Kidney Disease (CKD) on contracting COVID-19, hospitalization, mortality, and disease severity. METHODS: We searched published and preprint databases. We updated the reviews by searching for primary studies published after August 2020, and prioritized reviews that are most updated and of higher quality using the AMSTAR tool. RESULTS: We included 69 systematic reviews and 66 primary studies. Twenty-eight reviews reported on the prevalence of CKD among patients with COVID-19, which ranged from 0.4 to 49.0%. One systematic review showed an increased risk of hospitalization in patients with CKD and COVID-19 (RR = 1.63, 95% CI 1.03-2.58) (Moderate certainty). Primary studies also showed a statistically significant increase of hospitalization in such patients. Thirty-seven systematic reviews assessed mortality risk in patients with CKD and COVID-19. The pooled estimates from primary studies for mortality in patients with CKD and COVID-19 showed a HR of 1.48 (95% CI 1.33-1.65) (Moderate certainty), an OR of 1.77 (95% CI 1.54-2.02) (Moderate certainty) and a RR of 1.6 (95% CI 0.88-2.92) (Low certainty). CONCLUSIONS: Our review highlights the impact of CKD on the poor outcomes of COVID-19, underscoring the importance of identifying strategies to prevent COVID-19 infection among patients with CKD.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".