Machine Learning Tool Predicts Heart Failure Patients' Response to Diuretics, Enabling Personalized Treatment
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Researchers developed a machine learning-based tool to predict diuretic response in patients with acute decompensated heart failure.
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The tool, called the BAN-ADHF score, accurately identified subgroups of patients based on their response to diuretics.
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BAN-ADHF also predicted which patients would have the least response to diuretics, allowing personalized treatment.
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The study won an NHLBI data challenge for novel heart failure phenotyping and an AHA award for early career research.
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Validating BAN-ADHF in other populations could enable personalized strategies for managing fluid overload in hospitalized heart failure patients.