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Identification of Frailty Clusters Using Cross-Sectional Frailty and Frailty Trajectory: Cohort of Heart Failure Veterans Publisher



Razjouyan J1, 2, 3 ; Tofighi S4 ; Orkaby AR5, 6 ; Bozkurt B1 ; Sharafkhaneh A1 ; Horstman MJ1, 2 ; Goyal P7, 8 ; Amos CI9 ; Intrator O10, 11 ; Naik AD1, 2, 3, 12, 13
Authors
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Authors Affiliations
  1. 1. VA HSR&D, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, United States
  2. 2. Department of Medicine, Baylor College of Medicine, Houston, TX, United States
  3. 3. Big Data Scientist Training Enhancement Program, VA Office of Research and Development, Washington, DC, United States
  4. 4. Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
  5. 5. New England Geriatrics Research, Education, and Clinical Center, VA Boston Health Care System, Boston, MA, United States
  6. 6. Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States
  7. 7. Division of General Internal Medicine, Department of Medicine, Weill Medical College of Cornell University, New York, NY, United States
  8. 8. Division of Cardiology, Department of Medicine, Weill Medical College of Cornell University, New York, NY, United States
  9. 9. Department of Internal Medicine, UNM Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, United States
  10. 10. Brookdale Department of Geriatrics & Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
  11. 11. Geriatrics & Extended Care Data, Analysis Center, James J. Peters VA Medical Center, Bronx, NY, United States
  12. 12. Department of Management, Policy, and Community Health, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, United States
  13. 13. UTHealth Institute on Aging, University of Texas Health Science Center, Houston, TX, United States

Source: JACC: Advances Published:2025


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. © 2025
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