Abnormal Liver Function Tests in Acute Heart Failure: Relationship with Clinical Characteristics and Outcome in the PROTECT Study
Bibliographic record
Abstract
AIMS: Episodes of acute heart failure (AHF) unfavourably affect multiple organs, which may have an adverse impact on the outcomes. We investigated the prevalence and clinical consequences of abnormal liver function tests (LFTs) in AHF patients enrolled in the PROTECT study. METHODS AND RESULTS: The LFTs comprised serial assessment of aspartate aminotransferase (AST), alanine aminotransferase (ALT) and albumin at baseline and during follow-up (daily until discharge, on days 7 and 14). The prevalence of abnormal LFTs (above upper limit of normal for AST and ALT or below lower limit of normal for albumin) was: at baseline AST 20%, ALT 12%, albumin 40%; and at day 14: AST 15%, ALT 9%, albumin 26%. Abnormal LFTs at baseline were associated with a higher risk of in-hospital death with odds ratios [95% confidence interval (CI)] of 3.5 (1.7-7.3) for AST, 3.9 (1.8-8.4) for ALT, and 2.8 (1.3-5.9) for albumin (all P < 0.01). Abnormal baseline and discharge LFTs had an unfavourable impact on 180-day mortality with hazard ratios (95% CI) for baseline AST, ALT, and albumin of 1.3 (1.0-1.7), 1.1 (1.0-1.2), 1.4 (1.1-1.8), respectively, and 1.5 (1.1-2.0), 1.5 (1.0-2.2), and 1.6 (1.2-2.1), for discharge AST, ALT, albumin, respectively (all P < 0.05). Analysis of LFTs trajectories (calculated as changes in LFTs over time) revealed that increasing AST and ALT on day 3 as well as decreasing albumin on day 4 were independent prognosticators of 180-day outcome (all P < 0.05). CONCLUSIONS: Abnormal LFTs are frequent in AHF at baseline and during hospital stay and predict worse outcomes. Whether this association is causal and what are the underlying mechanisms involved require further study.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".