Improving Early Detection and Risk Prediction in Heart Failure

About this Research Topic

Background

Heart failure is a leading cause of hospitalization in adults over 65 years, with high mortality despite major advances in clinical management: approximately 10% at 1 month, 20-30% at 12 months and 45-60% at 5 years.

Genetics, lifestyle, chronic conditions (including high blood pressure and diabetes) and acute cardiac injury influence the risk of developing clinical HF. There is considerable heterogeneity in the development and manifestation of the HF syndrome, beyond the common functional categories of HF with reduced, preserved or mid-range ejection fraction. Therefore there is the need to apply systems medicine in order to better classify HF subtypes and develop methods leading to more personalized medical monitoring and treatment.

Despite moderate heritability, genomic approaches have struggled to identify genetic risk loci for HF. Of the few loci identified to date, the majority are pleiotropic (i.e. they are associated with risk for other conditions, including coronary heart disease and hypertension), and their association with HF may be indirect. Moreover, these studies have typically included patients with diverse underlying aetiologies. Each aetiology may be associated with a distinct genetic profile and lack of appropriate sub-phenotyping may have masked their discovery.

Newer technologies in imaging and biomarkers allow for better characterization of clinical factors such as infarct size or severity, and are increasingly combined with genomics for improvement in diagnosis and follow-up on patients. The goal of this research topic is to highlight the utility of systems medicine and ‘omics approaches to refine HF sub-phenotyping and improve early detection and risk prediction in HF.

Given the complexity of the HF syndrome, we propose that systems medicine approaches in well-phenotyped cohorts may provide an opportunity to improve early detection and risk prediction. We are seeking articles describing original research (including methodological approaches), reviews or commentaries that address the early detection of HF, and prediction of risk pre- or post- development of HF.

Topics include but are not limited to:
1) Clinical models for diagnosis and prediction of HF and subtypes, with and without -omics.
2) Electronic health record (EHR) mining and triage of HF subtypes.
3) Machine learning and AI on multisource health data for HF subtype prediction.
4) Sex and ethnic differences in aetiology and progression of HF.
5) Multi-modal modelling.

Keywords: Heart failure, clinical models, subphenotyping, machine learning, omics

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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