Prognostic Utility of N-terminal Pro-B-type Natriuretic Peptide (NT-proBNP) for Predicting Hospital Readmissions in Patients With Heart Failure: A Systematic Review of Clinical Evidence
Bibliographic record
Abstract
This systematic review examines the prognostic utility of N-terminal pro-B-type natriuretic peptide (NT-proBNP) for predicting hospital readmissions in patients with heart failure (HF). HF remains a leading cause of recurrent hospitalizations, contributing to increased morbidity and healthcare burden. While NT-proBNP is widely established for diagnosis and mortality prediction in HF, its role in forecasting hospital readmission risk-particularly at varying time frames-remains unclear. A comprehensive search of PubMed, Scopus, and the Cochrane Central Register of Controlled Trials (CENTRAL) yielded 452 records, of which six studies met the inclusion criteria. These included one randomized controlled trial, one post hoc RCT analysis, and four observational or registry-based studies. The methodological quality was assessed using RoB 2.0, the NIH Quality Assessment Tool, and the Newcastle-Ottawa Scale, revealing low to moderate risk of bias. The review found that isolated NT-proBNP values at admission showed limited predictive value, while serial measurements during hospitalization or within early post-discharge periods (e.g., 30-180 days) demonstrated stronger associations with readmission risk. Predictive variability was influenced by factors such as timing of measurement, renal function, age, and sex. Comparators, where present, varied across studies and included standard care or alternative biomarkers. Notably, current clinical guidelines lack standardized protocols for using NT-proBNP in readmission risk prediction, leading to inconsistent applications in practice. This review underscores the need for individualized interpretation and standardized measurement strategies to enhance NT-proBNP's utility in discharge planning and post-acute care of patients with HF.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".