Prospective validation of the EASL management algorithm for acute kidney injury in cirrhosis
Bibliographic record
Abstract
BACKGROUND & AIMS: The management of acute kidney injury (AKI) in cirrhosis is challenging. The EASL guidelines proposed an algorithm for the management of AKI, but this has never been validated. We aimed to prospectively evaluate this algorithm in clinical practice. METHODS: We performed a prospective cohort study in consecutive hospitalized patients with cirrhosis and AKI. The EASL management algorithm includes identification/treatment of precipitating factors, 2-day albumin infusion in patients with AKI ≥stage 1B, and treatment with terlipressin in patients with hepatorenal syndrome (HRS-AKI). The primary outcome was treatment response, which included both full and partial response. Secondary outcomes were survival and adverse events associated with terlipressin therapy. RESULTS: A total of 202 AKI episodes in 139 patients were included. Overall treatment response was 80%, while renal replacement therapy was required in only 8%. Response to albumin infusion was achieved in one-third of episodes. Of patients not responding to albumin, most (74%) did not meet the diagnostic criteria of HRS-AKI, with acute tubular necrosis (ATN) being the most common phenotype. The response rate in patients not meeting the criteria for HRS-AKI was 70%. Only 30 patients met the diagnostic criteria for HRS-AKI, and their response rate to terlipressin was 61%. Median time from AKI diagnosis to terlipressin initiation was only 2.5 days. While uNGAL (urinary neutrophil gelatinase-associated lipocalin) could differentiate ATN from other phenotypes (AUROC 0.78), it did not predict response to therapy in HRS-AKI. Ninety-day transplant-free survival was negatively associated with MELD-Na, ATN and HRS-AKI as well as uNGAL. Three patients treated with terlipressin developed pulmonary edema. CONCLUSIONS: The application of the EASL AKI algorithm is associated with very good response rates and does not significantly delay initiation of terlipressin therapy. IMPACT AND IMPLICATIONS: The occurrence of acute kidney injury (AKI) in patients with cirrhosis is associated with poor short-term mortality. Improving its rapid identification and prompt management was the focus of the recently proposed EASL AKI algorithm. This is the first prospective study demonstrating that high AKI response rates are achieved with the use of this algorithm, which includes identification of AKI, treatment of precipitating factors, a 2-day albumin challenge in patients with AKI ≥1B, and supportive therapy in patients with persistent AKI not meeting HRS-AKI criteria or terlipressin with albumin in those with HRS-AKI. These findings support the use of this algorithm in clinical practice.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".