Abstract 18608: Low Appendicular Skeletal Muscle Mass Predicts the Future Cadiovascular Events in Patients With Acute Decompensated Heart Failure
Bibliographic record
Abstract
Introduction: Heart failure (HF) is a clinical syndrome associated with diverse metabolic disturbances. Recent studies suggest that failing heart through secretion of soluble myostatin may induce skeletal muscle wasting in HF patients and skeletal muscle plays an important role in pathogenesis of exercise intolerance in patients with chronic HF. However, the clinical significance of skeletal muscle mass in patients with acute decompensated HF (ADHF) remains unclear. Hypothesis: We hypothesized that low appendicular skeletal muscle mass could predict the occurrence of future cardiovascular (CV) events in patients with ADHF. Methods: We assessed lean body mass by dual energy X-ray absorptiometry in 96 patients with ADHF (age 72±11, left ventricular ejection fraction (LVEF) 38±15%, B-type natriuretic peptide (BNP) levels on admission 752 [377-1398] pg/ml). Low appendicular skeletal muscle mass index (ASMI, appendicular skeletal muscle mass/height 2 ) was defined according to the Asia Working Group for Sarcopenia criteria (<7.0kg/m 2 in male, <5.4kg/m 2 in female). ADHF patients were followed until occurring CV events (CV death, nonfatal myocardial infarction, ischemic stroke, or HF re-hospitalization). Results: ASMI significantly correlated with age (r=-0.51, P<0.001), male sex (r=0.53, P<0.001), body mass index (r=0.63, P<0.001), systolic blood pressure on admission (r=0.21, P=0.04), and BNP levels on admission (r=-0.39, P=0.04). ADHF patients with low ASMI (n=54, 56%) had higher BNP levels (968 [552-1773] versus 498 [273-943], p=0.001) and higher rate of clinical scenario 2-3 (48% versus 12%, p=0.001) than those with normal ASMI. 42 patients developed CV events (median follow-up, 16months). Kaplan-Meier analysis demonstrated a significantly higher probability of CV events in the low ASMI group than those in the normal ASMI group (54% vs. 29%, log-rank test, P=0.02). Multivariate Cox hazard analysis identified low ASMI as an independent predictor of the CV events in patients with ADHF (hazard ratio 2.1, 95%-confidence interval 1.1-4.2, P=0.03). Conclusions: Low ASMI could predict the future CV events in patients with ADHF, irrespective of LV systolic function and other clinical profile.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".