The Diagnosis and Management of Hepatorenal Syndrome: A Comprehensive Update for the Intensivist
Bibliographic record
Abstract
Intensivists are being increasingly tasked with caring for critically ill patients with cirrhosis (ie, acute-on-chronic liver failure), many of whom develop acute kidney injury (AKI). Among the most morbid and complex causes of AKI in patients with cirrhosis is hepatorenal syndrome (HRS-AKI). Though HRS-AKI accounts for a fraction of AKI cases in the setting of cirrhosis, recent data suggest that effective pharmacologic treatment of HRS-AKI requires rapid diagnosis to allow for prompt intervention. Consequently, a firm understanding of the diagnosis and treatment of HRS-AKI is vital for all intensivists. In this review, we summarize recent developments in the diagnosis and treatment of HRS-AKI. Chief among these is the recent realization that HRS-AKI is not a diagnosis of exclusion, but instead may coexist with other forms of AKI, such as acute tubular injury, or may develop in the context of pre-existing chronic kidney disease. Moreover, with multiple recent trials suggesting that administration of fixed doses of intravenous albumin to unselected patients with cirrhosis and AKI may cause harm via volume overload and pulmonary edema, no longer is a 48-h trial of intravenous albumin recommended for all patients with AKI and cirrhosis. Instead, the newest guidelines recommend thoughtful assessment of volume status in all patients with AKI and cirrhosis and determination of an HRS-AKI diagnosis within 24 h to allow for prompt initiation of effective therapy. Short of liver transplantation, treatment of HRS-AKI is with vasoconstrictive agents. Though commonly used, midodrine/octreotide should largely be abandoned due to lack of efficacy. While recent trials have confirmed the effectiveness of terlipressin, its use is associated with a risk of potentially fatal respiratory failure and therefore requires careful patient selection and monitoring. As such, treatment of HRS-AKI with norepinephrine in the intensive care unit will remain the primary treatment option for many patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".