Mortality of Acute Kidney Injury in Cirrhosis: A Systematic Review and Meta‐Analysis of Over 5 Million Patients Across Different Clinical Settings
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) represents a commonly seen condition in the natural course of cirrhosis associated with unfavourable outcomes. AIMS: To evaluate and compare the pooled mortality rates of patients with cirrhosis, with versus without AKI, across different clinical settings and diagnostic criteria. METHODS: A systematic search of several databases was performed up to Oct 2023. Meta-analysis was performed using a generalised linear mixed model with a random effects model for all calculations. RESULTS: A total of 59 studies comparing patients with cirrhosis, with and without AKI, were included in the meta-analysis, encompassing 1,153,193 individuals with AKI and 4,630,814 without AKI. AKI development predisposed to significantly higher short (in-hospital and 30-days)-, intermediate (90-days)- and long (1-year)-term mortality rates in both inpatients and outpatients. Remarkably, patients with AKI admitted to intensive care unit (ICU) or diagnosed with acute-on-chronic liver failure (ACLF) experienced the higher short-term mortality rates, reaching 76% [95% confidence interval (CI): 73%-79%] and 54% (95%CI: 33%-73%), respectively. AKI staging correlated with mortality risk, with higher stages indicating higher mortality rates, while the timing of AKI development, whether community-acquired or hospital-acquired, plays a crucial role in patient prognosis, with distinct mortality patterns observed in each group. The selection of diagnostic criteria for AKI may also impact its association with the short-term mortality risk. CONCLUSIONS: AKI substantially affects the prognosis of patients with cirrhosis, especially those in ICU and/or with ACLF. Prognosis is also greatly influenced by the AKI stage, timing of onset and diagnostic criteria.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.034 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".