Purpose of Review: The goal of this review is to summarize the state of big data analyses in the study of heart failure (HF). We discuss the use of big data in the HF space, focusing on “omics” and clinical data. We address some limitations of this data, as well as their future potential. Recent Findings: Omics are providing insight into plasmal and myocardial molecular profiles in HF patients. The introduction of single cell and spatial technologies is a major advance that will reshape our understanding of cell heterogeneity and function as well as tissue architecture. Clinical data analysis focuses on HF phenotyping and prognostic modeling. Summary: Big data approaches are increasingly common in HF research. The use of methods designed for big data, such as machine learning, may help elucidate the biology underlying HF. However, important challenges remain in the translation of this knowledge into improvements in clinical care.

Additional Metadata
Keywords Big data, Heart failure, Machine learning, Omics, Single cell
Persistent URL dx.doi.org/10.1007/s11897-020-00469-9, hdl.handle.net/1765/129567
Journal Current Heart Failure Reports
Citation
Lanzer, J.D. (Jan D.), Leuschner, F. (Florian), Kramann, R.J.T, Levinson, R.T. (Rebecca T.), & Saez-Rodriguez, J. (Julio). (2020). Big Data Approaches in Heart Failure Research. Current Heart Failure Reports. doi:10.1007/s11897-020-00469-9