Impact of Malnutrition in Patients with Severe Heart Failure
Bibliographic record
Abstract
AIM: The role of malnutrition among patients with severe heart failure (HF) is not well established. We evaluated the incidence, predictors, and prognostic impact of malnutrition in patients with severe HF. METHODS AND RESULTS: Nutritional status was measured using the geriatric nutritional risk index (GNRI), based on body weight, height and serum albumin concentration, with malnutrition defined as GNRI ≤98. It was assessed in consecutive patients with severe HF, defined by at least one high-risk 'I NEED HELP' marker, enrolled at four Italian centres between January 2020 and November 2021. The primary endpoint was all-cause mortality. A total of 510 patients with data regarding nutritional status were included in the study (mean age 74 ± 12 years, 66.5% male). Among them, 179 (35.1%) had GNRI ≤98 (malnutrition). At multivariable logistic regression, lower body mass index (BMI) and higher levels of natriuretic peptides (B-type natriuretic peptide [BNP] > median value [685 pg/ml] or N-terminal proBNP > median value [5775 pg/ml]) were independently associated with a higher likelihood of malnutrition. Estimated rates of all-cause death at 1 year were 22.4% and 41.1% in patients without and with malnutrition, respectively (log-rank p < 0.001). The impact of malnutrition on all-cause mortality was confirmed after multivariable adjustment for relevant covariates (adjusted hazard ratio 2.03, 95% confidence interval 1.43-2.89, p < 0.001). CONCLUSION: In a contemporary, real-world, multicentre cohort of patients with severe HF, malnutrition (defined as GNRI ≤98) was common and independently associated with an increased risk of mortality. Lower BMI and higher natriuretic peptides were identified as predictors of malnutrition in these patients.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".