Why the study?
Contemporary guidelines do not incorporate AI-based multidimensional phenotyping into daily practice, despite HFpEF being a heterogeneous clinical syndrome needing characterized phenotypes for personalized management.
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Design
Review
AI phenotyping may refine HFpEF management; hypothesis-generating and requires prospective validation before practice change.
Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome with multiple underlying mechanisms and comorbidities that leads to a variety of clinical phenotypes. The identification and characterization of these phenotypes are essential for better understanding the precise pathophysiology of HFpEF, identifying appropriate treatment strategies, and improving patient outcomes. Despite accumulating data showing the potentiality of artificial intelligence (AI)-based phenotyping using clinical, biomarker, and imaging information from multiple dimensions in HFpEF management, contemporary guidelines and consensus do not incorporate these in daily practice. In the future, further studies are required to authenticate and substantiate these findings in order to establish a more standardized approach for clinical implementation.
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Lin et al. (2023) studied this question.
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