AST/ALT ratio predicts the functional severity of chronic heart failure with reduced left ventricular ejection fraction
Bibliographic record
Abstract
OBJECTIVE: Despite previous research that focused on liver transaminases as predictors of cardiovascular disease, there has been limited research evaluating the predictive value of AST/ALT ratio in patients with heart failure. We aimed to investigate AST/ALT ratio as an indicator of the functional severity in chronic heart failure with reduced left ventricular ejection fraction. RESULTS: Overall, 105 patients previously diagnosed with HFrEF from Buraidah-Al Qassim province, Saudi Arabia were included in this retrospective cross-sectional study. Data on study variables, including demographic data, left ventricular ejection fraction, NYHA class, and AST/ALT ratio, were collected from patients' records. The patients were divided into two groups, namely group-1 (AST/ALT ratio < 1) and group-2 (AST/ALT ratio ≥ 1), to identify any differences in their cardiac function profiles. NYHA class and NT-proBNP were higher and LVEF was lower in group-2 than in group-1. We found a mild significant correlation between AST/ALT ratio and APRI, FIB-4 score, NYHA-class, and LVEF (r = 0.2, 0.25, 0.26, and - 0.24, respectively; P < 0.05). Multivariate linear regression analysis model and ROC curve showed that AST/ALT ratio could independently predict HFrEF functional severity with a best cut-off value of 0.9, sensitivity of 43.6%, and specificity of 81.4%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".