Evidence map›Paper›PMID 40447344›Full record

ArticleJACC. Advances2025

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

Javad Razjouyan, Saeed Tofighi, Ariela R Orkaby, Biykem Bozkurt, Amir Sharafkhaneh, Molly J Horstman, Parag Goyal, Christopher I Amos, Orna Intrator, Aanand D Naik

Abstract read
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In one paragraph

Article in JACC. Advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Javad RazjouyanVA HSR&D, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, Texas, USA; Department of Medicine, Baylor College of Medicine, Houston, Texas, USA; Big Data Scientist Training Enhancement Program, VA Office of Research and Development, Washington, District of Columbia, USA. Electronic address: javad.razjouyan@bcm.edu.
Saeed TofighiTehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
Ariela R OrkabyNew England Geriatrics Research, Education, and Clinical Center, VA Boston Health Care System, Boston, Massachusetts, USA; Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Biykem BozkurtVA HSR&D, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, Texas, USA.
Amir SharafkhanehVA HSR&D, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, Texas, USA.
Molly J HorstmanVA HSR&D, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, Texas, USA; Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.
Parag GoyalDivision of General Internal Medicine, Department of Medicine, Weill Medical College of Cornell University, New York, New York, USA; Division of Cardiology, Department of Medicine, Weill Medical College of Cornell University, New York, New York, USA.
Christopher I AmosDepartment of Internal Medicine, UNM Comprehensive Cancer Center, University of New Mexico, Albuquerque, New Mexico, USA.
Orna IntratorBrookdale Department of Geriatrics & Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Geriatrics & Extended Care Data, Analysis Center, James J. Peters VA Medical Center, Bronx, New York, USA.
Aanand D NaikVA HSR&D, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, Texas, USA; Department of Medicine, Baylor College of Medicine, Houston, Texas, USA; Big Data Scientist Training Enhancement Program, VA Office of Research and Development, Washington, District of Columbia, USA; Department of Management, Policy, and Community Health, The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA; UTHealth Institute on Aging, University of Texas Health Science Center, Houston, Texas, USA.

Funding

Using Large Electronic Health Records and Advanced Analytics to Develop Predictive Frailty Trajectories in Patients with Heart FailureK25HL152006 · NHLBI · BAYLOR COLLEGE OF MEDICINE · PI RAZJOUYAN, JAVAD · 2020 to 2024
$719k
HSRD VA IK2 HX003163NHLBI NIH HHS K25 HL152006
6 · The paper itself

Abstract

backgroundFrailty 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.

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

methodsThis 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.

resultsThe 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.

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

Indexed as

cluster analysisfrailtyheart failurepatient outcomerisk assessment

Identifiers

PMID40447344
PMCPMC12235477

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.