Incorporating Novel Biomarkers to Predict 6-Month Mortality in Patients Hospitalized for Acute Heart Failure Open Access

Abstract

Background: Predicting heart failure is important as patients with a high short-term mortality may benefit from early referral for advanced therapies such as heart transplantation or durable mechanical circulatory support. While multiple models exist to predict mortality in patients admitted for acute heart failure, none incorporate novel biomarkers of urine sodium and urine microalbumin.

Methods: We used the Renal Optimization Strategies Evaluation Acute Heart Failure (ROSE-AHF) trial database which enrolled adults (≥18 years) hospitalized for acute heart failure with chronic kidney disease (estimated glomerula filtration rate of 15 – 30 ml/min/1.73m^2). Complete case analysis was performed for each biomarker. Biomarkers were Box-Cox transformed if skewed distribution. We evaluated the ability of each individual biomarker, N-terminus B-type natriuretic peptide (NT-proBNP), urine sodium, and urine albumin to predict 6-month mortality. Additional covariates were added using backward selection with a p-value threshold <0.05. Evaluation of multivariable logistic regression models was performed using K-fold (k=5) cross validation. Area under the receiver operating curves (AUC) with 95% confidence intervals was evaluated for model.

Results: The ROSE-AHF database has 360 patients enrolled, after removing missing values for each biomarker, 345 patients were included in the NT-proBNP cohort, 335 patients were included in the microalbumin cohort, 316 patients were included in the urine sodium cohort, and 297 patients were included in the all-biomarker cohort. Evaluation of the multivariable logistic regression models for NT-proBNP was 0.78 (95% Confidence Interval [CI]: 0.69 – 0.78), urine microalbumin (AUC: 0.77, 95% CI: 0.67 – 0.78), urine sodium (AUC 0.77, 95% CI: 0.68 – 0.78).

Conclusion: Urine microalbumin and urine sodium perform similarly well in fully adjusted logistic regression models when compared to serum NT-proBNP in predicting 6-month mortality in patients hospitalized for acute heart failure.

Table of Contents

Introduction---------------------------------------------------------------------------------------------------1

Methods-------------------------------------------------------------------------------------------------------3

Results---------------------------------------------------------------------------------------------------------5

Discussion-----------------------------------------------------------------------------------------------------7

References----------------------------------------------------------------------------------------------------11

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About this Master's Thesis

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School	Laney Graduate School
Department	Clinical Research
Degree	M.S.
Submission	Master's Thesis
Language	English
Research Field	Health Sciences, Osteopathic Medicine
Keyword	Biomarkers Heart Failure Prediction Models
Committee Chair / Thesis Advisor	Jordan Kempker, Emory University
Committee Members	Neal Dickert, Emory University Alanna Morris, Emory University

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