Association of NT-proBNP and BNP With Future Clinical Outcomes in Patients With ESKD: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
RATIONALE & OBJECTIVE: Use of brain natriuretic peptide (BNP) and N-terminal pro-BNP (NT-proBNP) for cardiovascular (CV) risk assessment in patients with end-stage kidney disease (ESKD) remains unclear. We examined the associations between different threshold elevations of these peptide levels and clinical outcomes in patients with ESKD. STUDY DESIGN: Systematic review and meta-analysis. SETTING & STUDY POPULATIONS: or receiving maintenance dialysis). SELECTION CRITERIA FOR STUDIES: Studies that reported NT-proBNP or BNP levels and future CV events, CV mortality, or all-cause mortality. DATA EXTRACTION: Cohort characteristics and measures of risk associated with study-specified peptide thresholds. ANALYTICAL APPROACH: Hazard ratios (HRs) for clinical outcomes associated with different NT-proBNP and BNP ranges were categorized into common thresholds and pooled using random-effects meta-analysis. RESULTS: We identified 61 studies for inclusion in our review (19,688 people). 49 provided sufficient detail for inclusion in meta-analysis. Pooled unadjusted HRs for CV mortality were progressively greater for greater thresholds of NT-proBNP, from 1.45 (95% CI, 0.91-2.32) for levels>2,000pg/mL to 5.95 (95% CI, 4.23-8.37) for levels>15,000pg/mL. Risk for all-cause mortality was significantly higher at all NT-proBNP thresholds ranging from> 1,000 to> 20,000pg/mL (HR range, 1.53-4.00). BNP levels>550pg/mL were associated with increased risk for CV mortality (HR, 2.54; 95% CI, 1.49-4.33), while the risks for all-cause mortality were 2.04 (95% CI, 0.82-5.12) at BNP levels>100pg/mL and 2.97 (95% CI, 2.21-3.98) at BNP levels>550pg/mL. Adjusted analyses demonstrated similarly greater risks for CV and all-cause mortality with greater NT-proBNP concentrations. LIMITATIONS: Incomplete outcome reporting and risk for outcome reporting bias. Estimation of risk for CV events for specific thresholds of both peptides were limited by poor precision. CONCLUSIONS: ESKD-specific NT-proBNP and BNP level thresholds of elevation are associated with increased risk for CV and all-cause mortality. This information may help guide interpretation of NT-proBNP and BNP levels in patients with ESKD.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".