New strategies and therapies for the prevention of heart failure in high‐risk patients. (5th July 2022)

• Record Type: Journal Article
• Authors: Hammond, Michael M.; Everitt, Ian K.; Khan, Sadiya S.
• Other Names: Fonarow Gregg C. guestEditor.
• Is Part Of: Clinical cardiology. Volume 45(2022)Supplement 1
• Page Start: S13
• Page End: S25
• Publication Date: 2022-07-05
• Subjects: heart failure -- machine learning -- primary prevention -- risk prediction -- social determinants; Cardiology -- Periodicals; 616.12005
• DOI: 10.1002/clc.23839
• Languages: English
• ISSNs: 0160-9289

Abstract

Abstract: Despite declines in total cardiovascular mortality rates in the United States, heart failure (HF) mortality rates as well as hospitalizations and readmissions have increased in the past decade. Increases have been relatively higher among young and middle‐aged adults (<65 years). Therefore, identification of individuals HF at‐risk (Stage A) or with pre‐HF (Stage B) before the onset of overt clinical signs and symptoms (Stage C) is urgently needed. Multivariate risk models (e.g., Pooled Cohort Equations to Prevent Heart Failure [PCP‐HF]) have been externally validated in diverse populations and endorsed by the 2022 HF Guidelines to apply a risk‐based framework for the prevention of HF. However, traditional risk factors included in the PCP‐HF model only account for half of an individual's lifetime risk of HF; novel risk factors (e.g., adverse pregnancy outcomes, impaired lung health, COVID‐19) are emerging as important risk‐enhancing factors that need to be accounted for in personalized approaches to prevention. In addition to determining the role of novel risk‐enhancing factors, integration of social determinants of health (SDoH) in identifying and addressing HF risk is needed to transform the current clinical paradigm for the prevention of HF. Comprehensive strategies to prevent the progression of HF must incorporate pharmacotherapies (e.g., sodium glucose co‐transporter‐2 inhibitors that have also been termed the "statins" of HF prevention), intensive blood pressure lowering, and heart‐healthy behaviors. Future directions include investigation of novel prediction models leveraging machine learning, integration of risk‐enhancing factors and SDoH, and equitable approaches to interventions for risk‐based prevention of HF.