Association between acute liver injury & severity and mortality of COVID-19 patients: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Acute liver injury (ALI), a complication often seen in COVID-19 patients, can lead to severe liver damage, multi-organ failure, acute vascular events, and can potentially escalate to patient mortality. Given this, we initiated a meta-analysis to investigate the correlation between ALI and adverse outcomes in COVID-19 patients. Methods: We conducted an exhaustive search of databases, including Medline, Embase, PubMed Central, ScienceDirect, Google Scholar, and the Cochrane Library, from the November 2019 until January 2022. The quality of the included studies was evaluated using the Newcastle Ottawa (NO) scale. Our meta-analysis was carried out using a random-effects model and results were presented as pooled odds ratios (ORs) with their corresponding 95% confidence intervals (CIs). Results: Our analysis incorporated 20 studies involving a total of 13,850 participants, predominantly from China and the United States. According to the NO scale, the majority of these studies were categorized as low-quality. Patients with ALI faced approximately 7 times higher odds of severe COVID-19 symptoms (pooled OR = 7.09; 95%CI: 4.97 to 10.12) and over 5 times higher odds of mortality (pooled OR = 5.50; 95%CI: 3.37 to 8.99) when compared to those without ALI. Conclusion: Our findings affirm that ALI is a potent predictor of adverse outcomes, including severity and mortality, among COVID-19 patients. Recognizing and promptly addressing ALI in COVID-19 patients could be pivotal in improving prognosis and tailoring individualized patient management strategies. This underscores the need for clinicians to be vigilant about liver complications in the COVID-19 patients and integrate appropriate interventions in the treatment paradigm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".