Comparison of B-type natriuretic peptide and NT proBNP as predictors of survival in patients on high-flux hemodialysis and hemodiafiltration
Bibliographic record
Abstract
End stage renal failure is associated with very high risk of cardiovascular disease. Serum levels of B-type natriuretic peptide (BNP) and NT proBNP reflect cardiovascular risk but it is unknown which of these peptides is a better predictor of survival in this population. BNP and NT proBNP levels and other relevant parameters were measured in 103 patients on high-flux hemodialysis (HD) and hemodiafiltration. Patients were followed for 4 years or until transplantation or death. Median BNP level was 262 pg/mL while the corresponding NT proBNP level was 362 pg/mL. Levels of these peptides were significantly lower in patients receiving hemodiafiltration than in those on high-flux HD. Only 1 of the 26 patients with normal NT proBNP died during follow-up while 3 of the 33 patients with normal BNP levels died in the same period. Both median BNP and NT proBNP levels were higher in those who died during follow-up than in those who survived 4 years. Cox Proportional Hazard models showed that both logBNP and log NT proBNP were independent predictors of survival. The area under the receiver operating characteristic curve was very similar for BNP and NT proBNP (0.779 vs. 0.781) for predicting 4-year survival. Net reclassification improvement analysis showed that adding NT proBNP to the baseline model lead to improved prediction of 4-year survival. BNP and NT proBNP levels were markedly elevated in HD patients and were highly predictive of survival. NT proBNP may have marginal advantage over BNP in predicting survival in this population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".