Comparative Efficacy of Terlipressin and Norepinephrine for Treatment of Hepatorenal Syndrome-Acute Kidney Injury (HRS-AKI): A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract The treatment of choice for hepatorenal syndrome-acute kidney injury (HRSAKI) is vasoconstrictor therapy in combination with albumin, preferably norepinephrine or terlipressin as recommended by recent guidelines. However, larger head-to-head trials comparing the efficacy of terlipressin and norepinephrine have not been completed. Evaluation of smaller studies can provide insights needed to understand the comparative effects of these 2 medications. In this meta-analysis, we aimed to assess HRS reversal and 1-month mortality in subjects receiving terlipressin or norepinephrine for the management of HRSAKI. We searched literature databases, including PubMed, Cochrane EMBASE, and ResearchGate, for randomized controlled trials (RCTs) published in the last 15 years (2007–2022) that compare terlipressin plus albumin to norepinephrine plus albumin for the treatment of HRS-AKI in adults. We identified 7 RCTs that included a total of 376 subjects with HRSAKI or HRS type 1 and performed pairwise meta-analysis and network meta-analysis with the random effects model to estimate odds ratios (OR) for HRS reversal and 1-month mortality. We also examined additional outcomes of HRS recurrence, predictors of response, and incidence of adverse events (AEs). Network meta-analysis favored terlipressin for HRS reversal (OR 1.33, 95% confidence interval [CI]; [0.81–2.18] P = 0.2532) and short-term survival (OR 1.43, 95% CI [0.68–3.02]; P = 0.3450) though this benefit did not reach statistical significance. Terlipressin was associated with AEs such as abdominal pain and diarrhea, whereas norepinephrine was associated with cardiovascular AEs such as chest pain and ischemia. Most of the AEs were reversible with reduction in dose or discontinuation of therapy across both arms. Of the terlipressin-treated subjects, 5.3% discontinued therapy due to serious AEs compared to 2.7% of the norepinephrine-treated subjects. Although this analysis favors terlipressin, future studies can provide additional insight into the comparative efficacy of norepinephrine and terlipressin in the treatment of HRS-AKI, especially in the setting of acute-on-chronic liver failure.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".