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Record W4386092141 · doi:10.1016/j.cgh.2023.06.006

Current Pharmacologic Therapies for Hepatorenal Syndrome-Acute Kidney Injury

2023· review· en· W4386092141 on OpenAlexfundno aff
Nikki Duong, Payal Kakadiya, Jasmohan S. Bajaj

Bibliographic record

VenueClinical Gastroenterology and Hepatology · 2023
Typereview
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsnot available
FundersMallinckrodt Pharmaceuticals
KeywordsTerlipressinHepatorenal syndromeMedicineMidodrineCirrhosisAcute kidney injuryInternal medicineAscitesRandomized controlled trialIntensive care medicineBlood pressure

Abstract

fetched live from OpenAlex

Background & AimsHepatorenal syndrome (HRS) can occur in patients with cirrhosis and ascites due to splanchnic vasodilation, renal hypoperfusion, and vasoconstriction. HRS is a diagnosis of exclusion and portends a poor prognosis, with upward of 80% mortality at 2 weeks without treatment. This review will highlight randomized controlled trials for HRS pharmacotherapy.MethodsA PubMed review of randomized controlled trials conducted over the past 25 years was undertaken; 18 studies were included.ResultsInitial studies showed that norepinephrine is as effective as terlipressin for HRS reversal. Midodrine with octreotide and albumin is less effective than terlipressin but better than albumin alone at improving 30-day mortality. Recently, terlipressin with albumin led to significantly higher rates of HRS reversal compared to albumin alone. Non-response to terlipressin can predict 90-day mortality in acute-on-chronic-liver failure.ConclusionsOur current understanding of HRS treatment is improved by recent randomized clinical trials. Previous studies using varying medication doses along with the “old” definition of hepatorenal syndrome (HRS type 1) rather than HRS-AKI means that there is still a need for future multicenter prospective studies further refining the risk-benefit ratio of vasoconstrictors for HRS-AKI patients. The Food and Drug Administration has approved terlipressin for use in September 2022. Because it will take time to adapt into clinical practice, less cost-prohibitive vasoconstrictors should still be considered. Opportunities also exist to clarify the safety, timing of initiation, as well as possible discontinuation of terlipressin. Hepatorenal syndrome (HRS) can occur in patients with cirrhosis and ascites due to splanchnic vasodilation, renal hypoperfusion, and vasoconstriction. HRS is a diagnosis of exclusion and portends a poor prognosis, with upward of 80% mortality at 2 weeks without treatment. This review will highlight randomized controlled trials for HRS pharmacotherapy. A PubMed review of randomized controlled trials conducted over the past 25 years was undertaken; 18 studies were included. Initial studies showed that norepinephrine is as effective as terlipressin for HRS reversal. Midodrine with octreotide and albumin is less effective than terlipressin but better than albumin alone at improving 30-day mortality. Recently, terlipressin with albumin led to significantly higher rates of HRS reversal compared to albumin alone. Non-response to terlipressin can predict 90-day mortality in acute-on-chronic-liver failure. Our current understanding of HRS treatment is improved by recent randomized clinical trials. Previous studies using varying medication doses along with the “old” definition of hepatorenal syndrome (HRS type 1) rather than HRS-AKI means that there is still a need for future multicenter prospective studies further refining the risk-benefit ratio of vasoconstrictors for HRS-AKI patients. The Food and Drug Administration has approved terlipressin for use in September 2022. Because it will take time to adapt into clinical practice, less cost-prohibitive vasoconstrictors should still be considered. Opportunities also exist to clarify the safety, timing of initiation, as well as possible discontinuation of terlipressin.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.150
GPT teacher head0.483
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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