Abstract 4141217: Lipid Profiles and Prognosis in Heart Failure: A Participant-Level Pooled Analysis of the PARADIGM-HF and PARAGON-HF Trials
Bibliographic record
Abstract
BACKGROUND: Higher total cholesterol (TC) and LDL-C levels may improve outcomes in heart failure (HF) patients. Elevated remnant cholesterol (RC) is a novel potential atherosclerosis risk factor, but its role in HF is underexplored. AIM: To investigate classical (TC, LDL-C, triglycerides (TG), and HDL-C) and novel (RC) lipid parameters (LP) as risk determinants for CV events in HF patients across the LVEF spectrum. METHODS: We pooled participant-level data from the PARADIGM-HF (LVEF ≤40%) and PARAGON-HF (LVEF ≥45%) trials to assess the association of LP with CV outcomes. RC was calculated as TC minus the sum of HDL-C and LDL-C. Lipid levels were classified per the National Lipid Association and RC by quartiles. The associations with time to first HF hospitalization and myocardial infarction (MI) were evaluated by adjusted Cox proportional hazards models stratified by trial. Restricted cubic splines accounted for potentially non-linear relationships. RESULTS: Of 12,819 participants with available lipid data, 1,967 (15.3%) experienced HF hospitalization and 442 (3.4%) had an MI. Over a median follow-up of 2.6 years, various LP were not consistently or significantly associated with risk of HF hospitalization, while higher RC and TC were linked to increased risk of MI (Table 1) . When evaluating LP continuously, there was a significant non-linear association between RC and risk of HF hospitalization, with a nadir in risk at ~ 20-30 mg/dL (Figure 1) . In contrast, higher levels of RC, LDL-C, and TC were linearly and significantly associated with risk of MI. None of the results were modified by LVEF. CONCLUSION: In a global HF population, higher RC levels were consistently associated with increased risk of MI, while the relationship between RC and the more frequent endpoint of HF hospitalization appeared to be more complex and inconsistent. These data might explain why previous interventional lipid lowering trials have not modified disease course in HF.
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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 | 0.026 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.019 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".