The Change in Body Weight During Hospitalization Predicts Mortality in Patients With Acute Decompensated Heart Failure
Bibliographic record
Abstract
BACKGROUND: In our experience, the change in body weight (BW) during hospitalization varies greatly in patients with acute decompensated heart failure (HF). Since the clinical significance of a change in BW is not clear, we investigated whether a change in BW could predict mortality. METHODS: We retrospectively enrolled 130 patients (72 males; aged 68 ± 10 years) who were hospitalized due to acute decompensated HF and followed for 2 years after discharge. The change in the BW index during hospitalization (ΔBWI) was calculated as (BW at hospital admission minus BW at hospital discharge)/body surface area at hospital discharge. RESULTS: The patients were divided into quartiles according to ΔBWI, and the 2-year mortality rates in the quartiles with the lowest, second, third and highest ΔBWI were 18.8%, 12.1%, 3.1% and 9.1%, respectively. In a multivariate Cox proportional hazards analysis after adjusting for variables with a P value less than 0.05, ΔBWI was independently associated with 2-year mortality (P = 0.0002), and the quartile with the lowest ΔBWI had a higher relative risk (RR) for 2-year mortality than the quartile with the highest ΔBWI (RR: 7.46, 95% confidence interval: 1.03 - 53.99, P = 0.04). CONCLUSION: In conclusion, ΔBWI was significantly associated with 2-year mortality after discharge, which indicates that ΔBWI might be a simple predictor of prognosis in acute decompensated HF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".