Challenging the Two Concepts in Determining the Appropriate Pre-Discharge N-Terminal Pro-Brain Natriuretic Peptide Treatment Target in Acute Decompensated Heart Failure Patients: Absolute or Relative Discharge Levels?
Bibliographic record
Abstract
AIMS: NT-proBNP is a strong predictor for readmissions and mortality in acute decompensated heart failure (ADHF) patients. We assessed whether absolute or relative NT-proBNP levels should be used as pre discharge treatment target. METHODS AND RESULTS: Our study population was assembled from seven ADHF cohorts. We defined absolute (<1500, <3000, <5000, and <15 000 ng/L) and relative NT-proBNP targets (>30, >50, and >70%). Population attributable risk fraction (PARF) is the proportion of all-cause 6-month mortality in the population that would be reduced if all patients attain the NT-proBNP target. PARF was determined for each target as well as the percentage of patients attaining the NT-proBNP target. Attainability was investigated by logistic regression analysis. A total of 1266 patients [age 74 (64-80), 60% male] was studied. For every absolute NT-proBNP level, a corresponding percentage reduction was found that resulted in similar PARFs. The highest PARF (∼60-70%) was observed for <1500 or >70%, but attainability was low (27% and 22%, respectively). The strongest predictor for not attaining these targets was admission NT-proBNP. In admission NT-proBNP tertiles, PARFs were significantly different for absolute, but not for relative targets. CONCLUSION: In an ADHF population, pre-discharge absolute or relative NT-proBNP targets may both be useful as they have similar effects on PARF. However, depending on admission NT-proBNP, absolute targets show varying PARFs, while PARFs for relative targets were similar. A relative target is predicted to reduce mortality consistently across the whole spectrum of ADHF patients, while this is not the case using a single absolute target.
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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 | 0.031 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".