The Influence of Comorbidities on Achieving an N-Terminal Pro-B-Type Natriuretic Peptide Target: A Secondary Analysis of the GUIDE-IT Trial
Bibliographic record
Abstract
Abstract Aims N-terminal pro-b-type natriuretic peptide (NT-proBNP) values may be influenced by patient factors beyond the severity of illness, including atrial fibrillation (AF), renal dysfunction, or increased body mass index (BMI). We hypothesized that these factors may influence the achievement of NT-proBNP targets and clinical outcomes. Methods A total of 894 patients with heart failure with reduced ejection fraction were enrolled in The Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment trial. NT-proBNP was analysed every 3 months. Results Forty per cent of patients had AF, the median estimated glomerular filtration rate (eGFR) was 59 mL/min/1.73 m2 [interquartile range (IQR) 43–76], and median BMI was 29 kg/m2 (IQR 25–34). Patients with AF, eGFR < 60 mL/min/1.73 m2, or a BMI < 29 kg/m2 had a higher level of NT-proBNP at randomization and over all study visits (all P values < 0.001). Over 18 months, the rate of change of NT-proBNP was less for patients with AF (compared with those without AF, P = 0.037) and patients with an eGFR < 60 mL/min/1.73 m2 (compared with eGFR > 60 mL/min/1.73 m2, P < 0.001). The rate of change of NT-proBNP was similar for patients with a BMI above or below the median value. Using the 90 day NT-proBNP, patients with AF, lower eGFR, or lower BMI were less likely to achieve the target NT-proBNP < 1000 pg/mL than patients without AF, higher eGFR, or higher BMI, respectively. None of these differed between the Usual Care or Guided Care arm for AF, eGFR, or BMI (Pinteractions all NS). Conclusions Patients with AF, a lower BMI, or worse renal function are less likely to achieve a lower or target NT-proBNP. Clinicians should be aware of these factors both when interpreting NT-proBNP levels and making therapeutic decisions about heart failure therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".