Novel prognostic biomarkers in decompensated cirrhosis: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Patients with decompensated cirrhosis experience high mortality rates. Current prognostic scores, including the model for end-stage liver disease (MELD), may underperform in settings other than in those they were initially developed. Novel biomarkers have been proposed to improve prognostication accuracy and even to predict development of complications. METHODS: We performed a systematic review and meta-analysis on novel urine and blood biomarkers and their ability to predict 90-day mortality in patients with decompensated cirrhosis. Secondary outcomes included 28-day and 1-year mortality, and development of acute-on-chronic liver failure, acute kidney injury and other complications. To overcome differences in units, temporal changes in assays and reporting heterogeneity, we used the ratio of means (RoM) as measure of association for assessing strength in predicting outcomes. An RoM>1 implies that the mean biomarker level is higher in those that develop the outcome than in those that do not. RESULTS: Of 6629 unique references, 103 were included, reporting on 29 different biomarkers, with a total of 31 362 biomarker patients. Most studies were prospective cohorts of hospitalised patients (median Child-Pugh-Turcotte score of 9 and MELD score of 18). The pooled 90-day mortality rate was 0.27 (95% CI 0.24 to 0.29). The RoM for predicting 90-day mortality was highest for interleukin 6 (IL-6) (2.56, 95% CI 2.39 to 2.74), followed by urinary neutrophil gelatinase-associated lipocalin (uNGAL) (2.42, 95% CI 2.20 to 2.66) and copeptin (2.33, 95% CI 2.17 to 2.50). These RoMs were all higher than for MELD (1.44, 95% CI 1.42 to 1.46). CONCLUSION: Novel biomarkers, including IL-6, uNGAL and copeptin, can probably improve prognostication of patients with decompensated cirrhosis compared with MELD alone.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".