Nutraceutical support in heart failure: a position paper of the International Lipid Expert Panel (ILEP)
Bibliographic record
Abstract
Heart failure (HF) is a complex clinical syndrome that represents a major cause of morbidity and mortality in Western countries. Several nutraceuticals have shown interesting clinical results in HF prevention as well as in the treatment of the early stages of the disease, alone or in combination with pharmacological therapy. The aim of the present expert opinion position paper is to summarise the available clinical evidence on the role of phytochemicals in HF prevention and/or treatment that might be considered in those patients not treated optimally as well as in those with low therapy adherence. The level of evidence and the strength of recommendation of particular HF treatment options were weighed up and graded according to predefined scales. A systematic search strategy was developed to identify trials in PubMed (January 1970 to June 2019). The terms 'nutraceuticals', 'dietary supplements', 'herbal drug' and 'heart failure' or 'left verntricular dysfunction' were used in the literature search. The experts discussed and agreed on the recommendation levels. Available clinical trials reported that the intake of some nutraceuticals (hawthorn, coenzyme Q10, l-carnitine, d-ribose, carnosine, vitamin D, probiotics, n-3 PUFA and beet nitrates) might be associated with improvements in self-perceived quality of life and/or functional parameters such as left ventricular ejection fraction, stroke volume and cardiac output in HF patients, with minimal or no side effects. Those benefits tended to be greater in earlier HF stages. Available clinical evidence supports the usefulness of supplementation with some nutraceuticals to improve HF management in addition to evidence-based pharmacological therapy.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".