Terlipressin May Decrease In-Hospital Mortality of Cirrhotic Patients with Acute Gastrointestinal Bleeding and Renal Dysfunction: A Retrospective Multicenter Observational Study
Bibliographic record
Abstract
BACKGROUND: Acute gastrointestinal bleeding (GIB) rapidly reduces effective blood volume, thereby precipitating acute kidney injury (AKI). Terlipressin, which can induce splanchnic vasoconstriction and increase renal perfusion, has been recommended for acute GIB and hepatorenal syndrome in liver cirrhosis. Thus, we hypothesized that terlipressin might be beneficial for cirrhotic patients with acute GIB and renal impairment. METHODS: In this Chinese multi-center study, 1644 cirrhotic patients with acute GIB were retrospectively enrolled. AKI was defined according to the International Club of Ascites (ICA) criteria. Renal dysfunction was defined as serum creatinine (sCr) > 133 μmol/L at admission and/or any time point during hospitalization. Incidence of renal impairment and in-hospital mortality were the primary end-points. RESULTS: The incidence of any stage ICA-AKI, ICA-AKI stages 1B, 2, and 3, and renal dysfunction in cirrhotic patients with acute GIB was 7.1%, 1.8%, and 5.0%, respectively. The in-hospital mortality was significantly increased by renal dysfunction (14.5% vs. 2.2%, P < 0.001) and ICA-AKI stages 1B, 2, and 3 (11.1% vs. 2.8%, P = 0.011), but not any stage ICA-AKI (5.7% vs. 2.7%, P = 0.083). The in-hospital mortality was significantly decreased by terlipressin in patients with renal dysfunction (3.6% vs. 20.0%, P = 0.044), but not in those with any stage ICA-AKI (4.5% vs. 6.0%, P = 0.799) or ICA-AKI stages 1B, 2, and 3 (0.0% vs. 14.3%, P = 0.326). CONCLUSION: Renal dysfunction increased the in-hospital mortality of cirrhotic patients with acute GIB. Terlipressin might decrease the in-hospital mortality of cirrhotic patients with acute GIB and renal dysfunction. TRIAL REGISTRATION: NCT03846180 ( https://clinicaltrials.gov ).
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".