Leucocyte ratios are biomarkers of mortality in patients with acute decompensation of cirrhosis and acute‐on‐chronic liver failure
Bibliographic record
Abstract
BACKGROUND: In patients with cirrhosis, progression to acute decompensation (AD) and acute-on-chronic liver failure (ACLF) has been associated with poor prognosis. Differential leucocyte ratios might predict mortality in systemic inflammatory conditions. AIM: To evaluate differential leucocyte ratios as prognostic biomarkers in patients with cirrhosis. METHODS: Patients with AD and ACLF were recruited from four centres in three countries. Peripheral blood differential leucocytes were measured (three centres using flow cytometry) on hospital admission and at 48 hours. Ratios were correlated to model for end-stage liver disease (MELD), chronic liver failure-sequential organ failure (CLIF-SOFA), suspected/culture-positive bacterial infection and survival. RESULTS: Nine hundred twenty-six patients (562 (61%) male, median age 55 (25-94) years) were studied. Overall, 350 (37%) did not survive to hospital discharge. Neutrophil-lymphocyte ratio (NLR) and monocyte-lymphocyte ratio (MLR) were elevated in patients with AD and ACLF who died during their hospital stay. On multivariate analysis NLR retained statistical significance independently of CLIF-SOFA or MELD. NLR >30 was associated with an 80% 90-day mortality in patients with ACLF but not AD. On sensitivity analysis for subgroups (alcohol-related liver disease and suspected sepsis), NLR and MLR retained statistically robust accuracy for the prediction of mortality. Significant predictive accuracy was only observed in centres using flow cytometry. CONCLUSION: Leucocyte ratios are simple and robust biomarkers of outcome in ACLF, which are comparable to CLIF-SOFA score but dependent on leucocyte quantification method. NLR and MLR may be used as screening tools for mortality prediction in patients with acutely deteriorating cirrhosis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".