Plasma<scp>BNP</scp>, a useful marker of fluid overload in hospitalized hemodialysis patients
Bibliographic record
Abstract
Hospitalization for intercurrent illness frequently disrupts the nutritional status of hemodialysis (HD) patients and jeopardizes the dry weight prescription. We report in this study the evolution of brain natriuretic peptide (BNP), blood pressure and body weight in hospitalized patients and the relationship between BNP plasma level and nutritional and inflammation parameters. We have studied 42 patients requiring hospitalization (F/M: 18/24; 72.5 ± 12.5 years old; 19/42 with diabetes). The plasma BNP levels at baseline, during hospitalization (BNP-Hosp), and in the recovery phase were compared. Predialysis and postdialysis blood pressure and postdialysis body weight were recorded and compared. BNP-Hosp increased significantly when compared with BNP levels at baseline, from 421 ± 647.2 pg/mL to 1584 ± 1584.4 pg/mL (P < 0.0001). Brain natriuretic peptide decreased from 1223 ± 1342.1 pg/mL during hospitalization to 616 ± 892.1 pg/mL after discharge (P = 0.005). The BNP-Hosp was positively correlated with C-reactive protein (P = 0.003) and negatively correlated with serum prealbumin (P = 0.0001) and albumin (P = 0.0001). The postdialysis body weight prescription decreased from 71.0 ± 15.7 kg at baseline to 70.5 ± 15.4 kg during hospitalization and to 67.8 ± 14.4 kg 4 months after discharge (P = 0.0032). Our study displays clearly the significant changes of plasma BNP levels occurring during intercurrent events. Fluid overload triggered by inflammation-associated catabolism and the lag time for dry weight adjustment is the cause of this finding. Hence, plasma BNP level may be used as a marker of fluid overload in patients with intercurrent events and may allow efficient dry weight adjustment. We cannot rule out an effect of inflammation on BNP synthesis.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".