Heart Failure-Chip: a miniaturized technology for self-monitoring of biomarkers in minimally or non-invasive body fluids
Heart failure (HF) affects at least 26 million people worldwide (1). While improvements have been made for treating HF with reduced ejection fraction (HFrEF), this has not been the case for patients with HF and preserved ejection fraction (HFpEF), who represent 40-70% of all HF patients (2). HFpEF diagnostics is based on assessing HF signs and symptoms, clinical demographics, echocardiography criteria and analytical evaluation of plasma B-type natriuretic peptide (BNP) (3). Natriuretic peptides are biomarkers of ventricular overload used to monitor HF disease progression and guide recurrent therapy optimizations (4).This process requires frequent visits to the hospital for venous blood sample collection. Measuring clinically-relevant biomarkers in readily available body fluids represents an unmet need for improving early diagnosis and monitoring of HF patients. In this project, we aim at validating a portable multiplex biosensing platform (“HF-Chip”) for simultaneous high-sensitivity and selective quantification of HF-related biomarkers, including BNP, inflammatory cytokines and matrix remodeling proteins, which might also predict HF progression, in blood and in fluids collected by non-invasive means (e.g. tears). The HF-Chip will help predicting newonset HF, HF exacerbations and disease progression, contributing for improved biomarker-leveraged disease phenotyping, risk stratification and timely therapeutic interventions. Relevance to HF’s management: In healthcare systems where an ever-increasing pressure to cut costs requires cheaper and better tests at the point of care to reduce clinical burden by using minimally invasive body fluids. Here lies the enormous potential of the HF-Chip: it is cheaper and more sensitive than conventional technologies; can be incorporated in clinical trials for high-throughput HF screening and monitoring, and in novel disease management strategies, including physician-supervised and/or algorithm-based self-management at distance.
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La Caixa Foundation