Urinary viral shedding of COVID-19 and its clinical associations: A Systematic Review and Meta-analysis of Observational Studies
Bibliographic record
Abstract
ABSTRACT Objectives To review the current literature on the presence of COVID-19 virus in the urine of infected patients and to explore the clinical features that can predict the presence of COVID-19 in urine. Materials and Methods A systematic review of published literature between 30 th December 2019 and 21 st June 2020 was conducted on Pubmed, Google Scholar, Ovid, Scopus, and ISI web of science. Studies investigating urinary viral shedding of COVID-19 in infected patients were included. Two reviewers selected relative studies and performed quality assessment of individual studies. Meta-analysis was performed on the pooled case reports and cohort with a sample size of 9. Results Thirty-nine studies were finally included in the systematic review; 12 case reports, 26 case series, and one cohort study. Urinary samples from 533 patients were investigated. Fourteen studies reported the presence of COVID-19 in the urinary samples from 24 patients. The crude overall rate of COVID-19 detection in urinary samples was 4.5%. Considering case series and cohorts with a sample size of ≥ 9, the estimated viral shedding frequency was 1.18 % (CI 95%: 0.14 – 2.87) in the meta-analysis. In adult patients, urinary shedding of COVID-19 was commonly detected in patients with moderate to severe disease (16 adult patients with moderate or severe disease versus two adult patients with mild disease). In children, urinary viral shedding of COVID-19 was reported in 4 children who all suffered from mild disease. Urinary viral shedding of COVID-19 was detected from day 1 to day 52 after disease onset. The pathogenicity of virus isolated from urine has been demonstrated in cell culture media in one study while another study failed to reveal replication of isolated viral RNA in cell cultures. Urinary symptoms were not attributed to urinary viral shedding. Conclusions While COVID-19 is rarely detected in urine of infected individuals, infection transmission through urine still remains possible. In adult patients, infected urine is more likely in the presence of moderate or severe disease. Therefore, caution should be exerted when dealing with COVID-19 infected patients during medical interventions like endoscopy and urethral catheterization.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".