Association of predicted lean body mass and fat mass with prognosis in patients with heart failure preserved ejection fraction
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown that body composition influences the prognosis of heart failure patients; however, the prognostic value of lean body mass and fat mass in patients with heart failure with preserved ejection fraction (HFpEF) remains unclear. This study aimed to investigate the association of lean body mass index and fat mass index with the prognostic outcomes in patients with HFpEF. METHODS: We performed a post hoc analysis of data from the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial to assess the relationship between Lean BMI and FMI with the adverse events. The primary endpoint was defined as a composite of cardiovascular death, aborted cardiac arrest, or heart failure hospitalization. RESULTS: A total of 3,320 patients were included, with a median follow-up of 3.3 years. Among them, 624 occurred a primary endpoint event, and 503 died. Lean BMI was not associated with the primary endpoint (HR 0.98, 95% CI 0.82-1.16) but was found to reduce the risk of all-cause mortality (HR 0.74, 95% CI 0.61-0.91). In contrast, FMI was associated with an increased risk of both the primary endpoint (HR 1.29, 95% CI 1.09-1.55) and all-cause mortality (HR 1.46, 95% CI 1.19-1.79). CONCLUSION: In patients with HFpEF, a higher FMI was strongly associated with increased risks of both the primary endpoint and all-cause mortality, while an elevated LBMI was associated with a reduced risk of mortality.
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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) | 0.001 | 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 | 0.001 |
| 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".