Loss in Body Weight is an Independent Prognostic Factor for Mortality in Chronic Heart Failure: Insights from the GISSI-HF and Val-HeFT Trials
Bibliographic record
Abstract
AIMS: Uncertainties remain on the biological and prognostic significance and therapeutic implications of loss in body weight (W-LOSS) in chronic heart failure (HF) patients. We assessed whether W-LOSS added additional prognostic value to classical clinical risk factors in two separate and large cohorts of patients with chronic HF. The factors associated with W-LOSS were studied. METHODS AND RESULTS: W-LOSS and estimated plasma volume changes were measured serially in the GISSI-HF (n = 6820) and Val-HeFT trials (n = 4892). In both studies, experiencing at least one episode of ≥5% W-LOSS during the first year of follow-up was considered a sign of wasting. In GISSI-HF, self-reported unintentional W-LOSS ≥2 kg between two consecutive clinical visits within 1 year was also considered a sign of wasting. W-LOSS occurred in 16.4% and 15.7% of the patients enrolled in GISSI-HF and Val-HeFT, respectively (unintentional ≥2 kg W-LOSS occurred in 18.9% in GISSI-HF). In multivariable analyses adjusting for a number of baseline covariates as well as for plasma volume changes, W-LOSS was found to be independently associated with mortality and adverse cardiovascular and non-cardiovascular outcomes, with a significant net reclassification improvement (cfNRI) and an increase in integrated discrimination improvement (IDI). W-LOSS was independently associated with several features representing the severity of HF, including baseline NT-proBNP and high sensitivity C-reactive protein (hsCRP) in Val-HeFT. CONCLUSIONS: W-LOSS was a frequent finding in the GISSI-HF and Val-HeFT trials, associated with multiple patient features, and added additional prognostic information beyond clinical variables of HF severity, including estimated plasma volume changes.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".