Faculty, Staff and Student Publications

Language

English

Publication Date

5-1-2025

Journal

JACC: Advances

DOI

10.1016/j.jacadv.2025.101751

PMID

40447344

PMCID

PMC12235477

PubMedCentral® Posted Date

5-28-2025

PubMedCentral® Full Text Version

Post-print

Abstract

BACKGROUND: Frailty is a syndrome associated with increased vulnerability and diminished physiological reserves. Three-quarters (78%) of heart failure (HF) patients are frail. Traditional frailty indices (FIs) assess cross-sectional deficits, while frailty trajectories (FTs) measure changes over time.

OBJECTIVES: This study aims to examine the interaction between FI and FT to enhance risk stratification in hospitalized adults with HF.

METHODS: This retrospective cohort study utilized data from the Veterans Health Administration, including 143,687 veterans aged >50 admitted for HF from 2005 to 2019. FT measurements were derived from FI calculations for each of the 3 years before index hospitalization. Unsupervised clustering identified 4 clusters based on FI and FT interactions: low-low, low-high, high-low, and high-high. Associations between these clusters and clinical outcomes (ie, 1-year mortality, prolonged hospital stays, emergency department visits, and readmissions) were analyzed.

RESULTS: The study cohort was mostly older (mean age 74 ± 10 years), male (98%), and diverse (55% non-Hispanic White). Survival analysis showed distinct mortality risks across clusters; while the 2 clusters with low FI had the longest survival, the high-high group had the lowest survival probability. Adjusted logistic regression indicated that the high-high cluster had over twice the odds of 1-year mortality compared to the low-low cluster (OR: 2.29; 95% CI: 2.15-2.44). The high-high cluster also had significantly higher rates of prolonged hospital stays, emergency department visits, and readmissions at 30 and 90 days postdischarge.

CONCLUSIONS: Integrating cross-sectional FI and longitudinal FT offers a comprehensive assessment of frailty in HF patients, improving risk stratification and disease management.

Keywords

cluster analysis, frailty, heart failure, patient outcome, risk assessment

Published Open-Access

yes

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