Impact of Non-Alcoholic Fatty Liver Disease on COVID-19 Severity and Healthcare Outcomes: A Systematic Review
Bibliographic record
Abstract
The systematic review investigated the association between non-alcoholic fatty liver disease (NAFLD) and COVID-19, focusing on pathophysiology, clinical outcomes, and public health implications. A comprehensive search of EBSCO, Scopus, and PubMed from January 2020 to December 2022 was conducted, following PRISMA guidelines. Included studies involved patients diagnosed with NAFLD or metabolic-associated fatty liver disease (MAFLD) and reported relevant comorbidities and COVID-19 outcomes. Quality was assessed using tools like the Newcastle-Ottawa Scale and AMSTAR-2. The review found that COVID-19 patients with NAFLD often had multiple comorbidities, especially diabetes and cardiovascular disease, which worsened outcomes. NAFLD was linked to higher hospitalization rates (odds ratio ~3.25), longer hospital stays by about two days, increased oxygen supplementation, higher ICU admissions, and a trend toward increased mortality, though mortality significance varied. Liver injury, indicated by elevated ALT and AST levels and hepatic steatosis on imaging, correlated with severe COVID-19. NAFLD patients showed systemic inflammation, immune dysregulation, and coagulation abnormalities contributing to disease severity. Ethnic disparities were noted, with certain groups having higher NAFLD prevalence and worse COVID-19 outcomes. These findings reveal challenges for healthcare systems due to increased resource demands and the need for integrated liver function monitoring during COVID-19 care. Overall, NAFLD significantly impacts COVID-19 severity through complex metabolic and immunological pathways, emphasizing the importance of clinical vigilance and multidisciplinary management for this high-risk population.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".