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Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
Published on: June 10, 2025
Admission BNP as a Dominant Prognostic Signal in Acute Decompensated Heart Failure with Frequent Renal Dysfunction:
Muneera O AlTaweel1,2, Elbadri I Abdelgadir2,3, Shahinaz Mohamed2
1Department of Cardiac Sciences, King Abdulaziz Hospital (KAH), Ministry of National Guard-Health Affairs (MNGHA), Al-Ahsa 36428, Saudi Arabia.
Admission B-type natriuretic peptide (BNP) is the strongest predictor of in-hospital mortality in acute decompensated heart failure patients with kidney dysfunction. Further validation is needed before clinical use.
Area of Science:
- Cardiology
- Nephrology
- Biomarkers
Background:
- Risk stratification for acute decompensated heart failure (ADHF) is challenging, especially with concurrent renal dysfunction.
- B-type natriuretic peptide (BNP) is a key biomarker, but its prognostic role in cardiorenal dysfunction needs further characterization.
Purpose of the Study:
- To evaluate the prognostic significance of admission biomarkers, including BNP, for in-hospital mortality in ADHF patients with renal dysfunction.
- To assess the performance of a multivariable model incorporating BNP for predicting outcomes in this high-risk population.
Main Methods:
- Retrospective cohort study of 220 ADHF admissions.
- Developed a multivariable logistic regression model using admission data (age, sex, BP, eGFR, albumin, log-BNP).
- Evaluated model performance using discrimination (AUC) and calibration, adhering to TRIPOD guidelines.
Main Results:
- In-hospital mortality was 7.7%.
- Log-transformed BNP was the sole significant independent predictor of in-hospital mortality (OR 2.39, p=0.009).
- Exploratory model showed an AUC of 0.81 (0.73 after correction); simplified model (albumin, log-BNP) had an AUC of 0.77.
Conclusions:
- Admission BNP is the primary prognostic factor for in-hospital mortality in ADHF patients with renal dysfunction.
- The study's findings are hypothesis-generating due to the limited number of events.
- External validation in larger cohorts is essential for clinical application.
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