Outcomes of patients with alcohol‐associated hepatitis and acute kidney injury – Results from the HRS Harmony Consortium
Bibliographic record
Abstract
BACKGROUND & AIMS: The development of acute kidney injury (AKI) in the setting of alcohol-associated hepatitis (AH) portends a poor prognosis. Whether the presence of AH itself drives worse outcomes in patients with cirrhosis and AKI is unknown. METHODS: Retrospective cohort study of 11 hospital networks of consecutive adult patients admitted in 2019 with cirrhosis and AKI. AKI phenotypes, clinical course, and outcomes were compared between AH and non-AH groups. RESULTS: A total of 2062 patients were included, of which 303 (15%) had AH, as defined by National Institute on Alcohol Abuse and Alcoholism (NIAAA) criteria. Patients with AH, compared to those without, were younger and had higher Model for End-stage Liver Disease-Sodium (MELD-Na) scores on admission. AKI phenotypes significantly differed between groups (p < 0.001) with acute tubular necrosis occurring more frequently in patients with AH. Patients with AH reached more severe peak AKI stage, required more renal replacement therapy, and had higher 90-day cumulative incidence of death (45% [95% CI: 39%-51%] vs. 38% [95% CI: 35%-40%], p = 0.026). Using no AH as reference, the unadjusted sHR for 90-day mortality was higher for AH (sHR: 1.24 [95% CI: 1.03-1.50], p = 0.024), but was not significant when adjusting for MELD-Na, age and sex. However, in patients with hepatorenal syndrome, AH was an independent predictor of 90-day mortality (sHR: 1.82 [95% CI: 1.16-2.86], p = 0.009). CONCLUSIONS: Hospitalised patients with cirrhosis and AKI presenting with AH had higher 90-day mortality than those without AH, but this may have been driven by higher MELD-Na rather than AH itself. However, in patients with hepatorenal syndrome, AH was an independent predictor of mortality.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".