Liver Function Abnormalities and Outcome in Patients with Chronic Heart Failure: Data from the Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity (CHARM) Program
Bibliographic record
Abstract
AIMS: The prevalence and importance of liver function test (LFT) abnormalities in a large contemporary cohort of heart failure patients have not been systematically evaluated. METHODS AND RESULTS: We characterized the LFTs of 2679 patients with symptomatic chronic heart failure from the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity program (CHARM). We used multivariable modelling to assess the relationships between baseline LFT values and long-term outcomes. Liver function test abnormalities were common in patients with chronic heart failure, ranging from alanine aminotransferase elevation in 3.1% of patients to low albumin in 18.3% of patients; total bilirubin was elevated in 13.0% of patients. In multivariable analysis, elevated total bilirubin was the strongest LFT predictor of adverse outcome for both the composite outcome of cardiovascular death or heart failure hospitalization (HR 1.21 per 1 SD increase, P<0.0001) and all-cause mortality (HR 1.19 per 1 SD increase, P<0.0001). Even after adjustment for other variables, elevated total bilirubin was one of the strongest independent predictors of poor prognosis (by global chi-square). CONCLUSION: Bilirubin is independently associated with morbidity and mortality. Changes in total bilirubin may offer insight into the underlying pathophysiology of chronic heart failure.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".