Severity and Outcome of Acute-on-Chronic Liver Failure is Dependent on the Etiology of Acute Hepatic Insults
Bibliographic record
Abstract
BACKGROUND: Acute-on-chronic liver failure (ACLF) may be precipitated by various hepatic insults. The present study evaluated the outcomes of ACLF with different acute insults. PATIENTS AND METHODS: A total of 368 ACLF patients were included. Data collected included etiologies of acute hepatic insult and underlying chronic liver disease, and organ failure. Model for end-stage liver disease (MELD), chronic liver failure consortium (CLIF)-C ACLF, and acute physiology and chronic health evaluation (APACHE) II scores were calculated. Predictors of survival were assessed by the Cox proportional hazard model. RESULTS: The most frequent acute insult was active alcohol consumption [150 (40.8%) patients], followed by hepatitis B virus (HBV) [71 (19.3%) patients], hepatitis E virus (HEV) superinfection [45 (12.2%) patients], autoimmune hepatitis flare [17 (4.6%) patients], antituberculosis drugs [16 (4.3%) patients], and hepatitis A virus superinfection [2 (0.5%) patients]; 67 (18.2%) cases were cryptogenic. Alcohol-ACLF and cryptogenic-ACLF were more severe. Median CLIF-C, MELD, and APACHE II scores in alcohol-ACLF and cryptogenic-ACLF were significantly higher than those in HBV-ACLF and HEV-ACLF (CLIF-C: 47.1, 47.4 vs. 42.9, 42.0, P=0.002; MELD: 29, 29.9 vs. 28.9, 25.2, P=0.02; APACHE II: 16.5, 18.0 vs. 12, 14, P<0.001, respectively). Frequencies of kidney and brain failures were also higher in alcohol/cryptogenic-ACLF than in HBV/HEV-ACLF (kidney failure: 35.3%/34.3% vs. 23.9%/11.1%, P=0.009; brain failure: 26.0%/22.4% vs. 15.5%/4.4%, P=0.01, respectively). Mortality in the alcohol-ACLF group was the highest (64.0%), followed by that in the cryptogenic-ACLF (62.7%), HBV-ACLF (45.1%), and HEV-ACLF (17.8%) groups (P<0.001). In multivariable analysis, alcohol-ACLF had significantly higher mortality compared with HEV-ACLF (hazard ratio, 3.06; 95% confidence interval, 1.10-8.49, P=0.03). CONCLUSIONS: Alcohol/cryptogenic-ACLF had more severe phenotypic presentation, more incidence of organ failures, and higher mortality compared with HEV/HBV-ACLF. Alcohol-ACLF had the highest mortality, whereas HEV-ACLF had the best survival.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".