Host Response Protein Biomarkers Indicative of Persistent Acute Kidney Injury in Critically Ill COVID-19 Patients
Bibliographic record
Abstract
IMPORTANCE: Sepsis-related host-response anomalies contribute to acute kidney injury (AKI) duration. Data on the host-response specific to COVID-19-associated AKI (COVID-AKI) in critically ill patients is limited. OBJECTIVES: We postulated that persistent COVID-AKI (> 48 hr) differs in host response from transient (< 48 hr) or no COVID-AKI. DESIGN, SETTING, AND PARTICIPANTS: This prospective biomarker study observed patients with severe acute respiratory syndrome coronavirus 2 infection, without chronic kidney disease, in three ICUs from March 2020 to July 2020. AKI was assessed by hourly urine output and daily plasma creatinine. MAIN OUTCOMES AND MEASURES: Luminex and enzyme-linked immunosorbent assay were used to analyze 48 plasma protein biomarkers across six pathophysiological domains, which were tested with mixed-effects models. RESULTS: Of 177 included patients, 106 (59.9%) had AKI within the first 48 hours of admission, of whom 76 (71.7%) had persistent AKI and 30 (28.3%) transient AKI. Those with persistent AKI often had obesity, hypertension, and a higher Sequential Organ Failure Assessment score due to the renal component. Longitudinal analyses revealed that seven proteins were elevated in persistent AKI compared with no AKI. These were related to inflammation (triggering receptor expressed on myeloid cells 1, p < 0.001; tumor necrosis factor receptor 1, p < 0.001; procalcitonin, p = 0.001), complement activation (mannan-binding lectin serine protease-2, p = 0.001), kidney dysfunction (cystatin C, p < 0.001; neutrophil gelatinase-associated lipocalin, p < 0.001), and lung dysfunction (Clara cell secretory protein 16, p < 0.001). AKI (duration) was not associated with differences in the cytokine signaling, endothelial cell activation, or coagulation domains. CONCLUSIONS AND RELEVANCE: In contrast with sepsis-associated AKI, primarily inflammation-related biomarker levels correlated with COVID-AKI persistence. This study offers insights into COVID-AKI and may guide approaches to mitigate its persistence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".