Management of hepatorenal syndrome in liver cirrhosis: a recent update
Bibliographic record
Abstract
Hepatorenal syndrome (HRS) is a serious form of renal dysfunction in patients with cirrhosis and ascites. It is an important component of the acute-on-chronic liver failure (ACLF) syndrome. Significant recent changes in the understanding of the pathophysiology of renal dysfunction in cirrhosis include the role of inflammation in addition to hemodynamic changes. The term acute kidney injury (AKI) is now adopted to include all functional and structural forms of acute renal dysfunction in cirrhosis, with various stages describing the severity of the condition. Type 1 hepatorenal syndrome (HRS1) is renamed HRS-AKI, which is stage 2 AKI [doubling of baseline serum creatinine (sCr)] while fulfilling all other criteria of HRS1. Albumin is used for its volume expanding and anti-inflammatory properties to confirm the diagnosis of HRS-AKI. Vasoconstrictors are added to albumin as pharmacotherapy to improve the hemodynamics. Terlipressin, although not yet available in North America, is the most common vasoconstrictor used worldwide. Patients with high grade of ACLF treated with terlipressin are at risk for respiratory failure if there is pretreatment respiratory compromise. Norepinephrine is equally effective as terlipressin in reversing HRS1. Recent data show that norepinephrine may be administered outside the intensive care setting, but close monitoring is still required. There has been no improvement in overall or transplant-free survival shown with vasoconstrictor use, but response to vasoconstrictors with reduction in sCr is associated with improvement in survival. Non-responders to vasoconstrictor plus albumin will need liver transplantation as definite treatment with renal replacement therapy as a bridge therapy. Combined liver and kidney transplantation is recommended for patients with prolonged history of AKI, underlying chronic kidney disease or with hereditary renal conditions. Future developments, such as the use of biomarkers and metabolomics, may help to identify at risk patients with earlier diagnosis to allow for earlier treatment with improved outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".