The Relationship Between Kidney Biomarkers, Inflammation, Severity, and Mortality Due to COVID-19—A Two-Timepoint Study
Bibliographic record
Abstract
About a quarter of COVID-19 patients develop acute kidney injury (AKI), worsening prognosis and increasing mortality. Severe COVID-19 often triggers a hyperactive immune response, influencing disease outcomes. This study examined the correlation between kidney injury biomarkers, inflammatory mediators, and mortality in COVID-19 patients. Blood samples from 390 COVID-19 patients were collected at admission and before the outcome. Serum Cystatin C (CysC), albumin, and plasma NGAL were measured via nephelometry, while inflammatory mediators (IL-4, IL-6, IL-10, IL-15, IFN-γ, TNF-α, and IL-1β) were assessed by ELISA. Most patients were male, with hypertension and diabetes as common comorbidities, and a high ICU admission rate. Lower albumin and elevated CysC and NGAL were linked to mortality. Increased inflammatory mediators correlated with lower albumin and higher CysC and NGAL, reinforcing the connection between systemic inflammation and kidney dysfunction. Elevated cytokines and kidney injury biomarkers, including NGAL, CysC, and low albumin, are strongly associated with higher mortality in COVID-19 patients. These findings highlight the role of inflammation and kidney function markers in identifying high-risk individuals, improving patient management, and mitigating complications. Monitoring these biomarkers remains crucial for managing long-term health impacts and future outbreaks.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".