Mortality in chronic kidney disease patients with COVID-19: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract At the beginning of 2020, the outbreak of coronavirus disease 2019 (COVID-19) led to a worldwide pandemic and mass panic. The number of infected people has been increasing exponentially since, and the mortality rate has also been concomitantly increasing. At present, no study has summarized the mortality risk of COVID-19 in patients with chronic kidney disease (CKD). Therefore, the aim of the present study was to conduct a literature review and meta-analysis to understand the frequency of mortality among CKD patients infected with COVID-19. A comprehensive systematic search was conducted on the PubMed, Embase, and Cochrane databases to find articles published until May 15, 2020. Study quality was assessed using a modified version of the Newcastle–Ottawa Scale. After careful screening based on the inclusion and exclusion criteria, 3,867,367 patients from 12 studies were included. The mortality rate was significantly higher among CKD patients with COVID-19 infection than among CKD patients without COVID-19 infection, as indicated by a pooled OR of 5.81 (95% CI 3.78–8.94, P < 0.00001, I 2 = 30%). The patients were then stratified into ≥ 70 and < 70 years, and subgroup analysis revealed that among CKD patients with COVID-19 infection, the mortality rate was higher in the < 70 years group (OR 8.69, 95% CI 7.56–9.97, P < 0.0001) than in the ≥ 70 years group ( OR 2.44, 95% CI 0.75–6.63, P = 0.15). Thus, COVID-19 patients with CKD have a high mortality risk and require a comprehensive multidisciplinary management strategy.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| 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".