Key result
Big data approaches, including omics and machine learning on clinical data, are increasingly used to elucidate heart failure biology and improve phenotyping, though translational challenges remain.
Why the study?
To summarize the state of big data analyses in the study of heart failure, focusing on omics and clinical data, including their limitations and future potential.
Big data and machine learning offer new insights into heart failure biology and phenotyping, but require further work to translate into clinical care improvements.
Answers are grounded in this paper and cite their sources.
Requires prospective validation before clinical adoption in heart failure; leaves open effects on patient management and outcomes.
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. 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.
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Lanzer et al. (2020) conducted a review in Heart failure. Big data approaches (omics and clinical data) was evaluated. Big data approaches, including omics and machine learning on clinical data, are increasingly used to elucidate heart failure biology and improve phenotyping, though translational challenges remain.
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