Acute kidney injury incidence and clinical features: Refractory versus non-refractory ascites
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Outpatients with cirrhosis, ascites and minor acute serum creatinine (sCr) changes could have been missed as having acute kidney injury (AKI). This study aims to assess the incidence, clinical features of all AKI stages amongst patients with cirrhosis and various ascites severities. MATERIALS AND METHODS: Retrospective study of patients with cirrhosis and ascites from April 2020 to March 2021. Data collected included demographics, clinical features, medications, AKI development, and 6-month follow-up outcomes. Multivariate analysis for factors predicting AKI development and resolution was done. RESULTS: 115 (38 % of 306) with refractory ascites (RA) were compared to 191 with non-refractory ascites (n-RA), 86 % were outpatients. RA patients had higher baseline MELD-Na (18.1 ± 4.7 vs. 17.2 ± 6.8 in n-RA, p = 0.01) but had similar cirrhosis complications. 98 % RA patients required regular large volume paracenteses (LVP) (p < 0.001 vs. n-RA). AKI occurred in 39 % of RA and 19 % of n-RA patients (p<0.001). Most were stage 1 AKI, treated with albumin ± vasoconstrictor with similar response. 27 % of AKI in n-RA were classified as type 1 HRS (vs.20 % in RA, p < 0.001). Baseline MELD-Na (p = 0.01) predicted AKI development; lower peak sCr predicted AKI resolution (p = 0008). 11 (3.6 %) n-RA and 22 (19 %) RA patients developed acute-on-chronic liver failure (ACLF), with 86 % RA patients having renal failure as part of the ACLF syndrome (p < 0.001 vs. n-RA patients). Both groups had similar 6-month survival. CONCLUSIONS: AKI occurs not infrequently in n-RA patients who are mostly treated as outpatients. Therefore, patients with n-RA need to be monitored closely so to allow prompt diagnosis and treatment of AKI.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".