COVID-19-Induced Hepatic Injury: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background The current pandemic of the novel coronavirus disease (COVID-19) is a global health challenge. Pulmonary dysfunction is the main outcome of COVID‐19 infection. In critically ill patients, however, liver complications have also been reported. Thus, we conducted a systematic review and meta-analysis to draw generalized conclusions regarding impaired liver biochemistry and its potential relationship with COVID-19 disease severity. Materials and Methods We searched the PubMed, Scopus, and Web of Science databases for all the related literature published up to June 20, 2020. The data were analyzed using R statistical software. A random‐effects model was employed for pooling the data. The risk of bias and quality of included studies was assessed using the modified Newcastle-Ottawa Scale (NOS) for cohort studies. Results The present meta-analysis comprises 10 retrospective and two prospective studies (6,976 COVID-19 patients). The serum analysis revealed significantly higher levels of alanine aminotransferases and aspartate aminotransferases and significantly lower albumin levels. Moreover, insignificant increases in serum levels of total bilirubin were observed. Upon subgroup analysis of six studies (severe cases, n=131; non-severe cases, n=334) stratified on the basis of disease severity, we found that these abnormalities were relatively higher in severe cases of COVID-19 (albumin [weighted mean difference (WMD), 34.03 g/L; 95% CI, 27.42 to 40.63; p<0.0001; I2=96.83%); alanine transaminase (ALT) [WMD, 31.66 U/L; 95% CI, 25.07 to 38.25; p<0.0001; I2=55.64%]; aspartate aminotransferase (AST) [WMD, 41.79 U/L; 95% CI, 32.85 to 50.72; p<0.0001; I2=51.43%]; total bilirubin [WMD, 9.97 μmol/L; 95% CI, 8.46 to 11.48; p<0.0001; I2=98%]) than in non-severe cases. Conclusion Deranged liver enzymes serve as prognostic factors to assess the severity of COVID-19. Liver markers should, therefore, be observed and monitored continuously.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".