Kidney biomarkers and differential diagnosis of patients with cirrhosis and acute kidney injury
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Bibliographic record
Abstract
UNLABELLED: Acute kidney injury (AKI) is common in patients with cirrhosis and associated with significant mortality. The most common etiologies of AKI in this setting are prerenal azotemia (PRA), acute tubular necrosis (ATN), and hepatorenal syndrome (HRS). Accurately distinguishing the etiology of AKI is critical, as treatments differ markedly. However, establishing an accurate differential diagnosis is extremely challenging. Urinary biomarkers of kidney injury distinguish structural from functional causes of AKI and may facilitate more accurate and rapid diagnoses. We conducted a multicenter, prospective cohort study of patients with cirrhosis and AKI assessing multiple biomarkers for differential diagnosis of clinically adjudicated AKI. Patients (n = 36) whose creatinine returned to within 25% of their baseline within 48 hours were diagnosed with PRA. In addition, 76 patients with progressive AKI were diagnosed by way of blinded retrospective adjudication. Of these progressors, 39 (53%) patients were diagnosed with ATN, 19 (26%) with PRA, and 16 (22%) with HRS. Median values for neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18), kidney injury molecule-1 (KIM-1), liver-type fatty acid binding protein (L-FABP), and albumin differed between etiologies and were significantly higher in patients adjudicated with ATN. The fractional excretion of sodium (FENa) was lowest in patients with HRS, 0.10%, but did not differ between those with PRA, 0.27%, or ATN, 0.31%, P = 0.54. The likelihood of being diagnosed with ATN increased step-wise with the number of biomarkers above optimal diagnostic cutoffs. CONCLUSION: Urinary biomarkers of kidney injury are elevated in patients with cirrhosis and AKI due to ATN. Incorporating biomarkers into clinical decision making has the potential to more accurately guide treatment by establishing which patients have structural injury underlying their AKI. Further research is required to document biomarkers specific to HRS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it