JMIR AGING

Razjouyan et alResearch Letter

The Frailty Trajectory’s Additional Edge Over the Frailty
Index: Retrospective Cohort Study of Veterans WithHeart Failure
Javad Razjouyan1,2,3, PhD; Ariela R Orkaby4,5, MD; Molly J Horstman1,2, MD; Parag Goyal6, MD; Orna Intrator7,8,
MD; Aanand D Naik2,9,10, MD
1Baylor College of Medicine, Houston, TX, United States
VA Health Services Research & Development, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA MedicalCenter, Houston, TX, United States
3Big Data Scientist Training Enhancement Program, VA Office of Research and Development, Washington, DC, United States
4
New England Geriatrics Research, Education, and Clinical Center, VA Boston Health Care System, Boston, MA, United States
5
Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States
6Division of General Internal Medicine, Department of Medicine, Weill Medical College of Cornell University, New York, NY, United States
7
Geriatrics and Extended Care Data Analysis Center, Canandaigua VA Medical Center, Canandaigua, NY, United States
8
Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY, United States
9Department of Management, Policy, and Community Health, School of Public Health, University of Texas Health Science Center, Houston, TX,
United States
10Institute on Aging, University of Texas Health Science Center, Houston, TX, United States
2Corresponding Author:
Javad Razjouyan, PhD
Baylor College of Medicine
1 Baylor Plaza
Houston, TX, 77030
United States
Phone: 1 5203424525
Email: javad.razjouyan@bcm.edu
JMIR Aging 2024;7:e56345; doi: 10.2196/56345
Keywords: gerontology; geriatric; geriatrics; older adult; older adults; elder; elderly; older person; older people; ageing; aging;frailty; frailty index; frailty trajectory; frail; weak; weakness; heart failure; HF; cardiovascular disease; CVD; congestive
heart failure; CHF; myocardial infarction; MI; unstable angina; angina; cardiac arrest; atherosclerosis; cardiology; cardiac;
cardiologist; cardiologists

Introduction

Methods

Individuals with heart failure (HF) have a high burden ofhealth care utilization, costs, and morbidity in the year
following hospitalization for an acute HF exacerbation.
Frailty, which has been described as increased vulnerability to adverse events, is common among those with HF
and increases with age [1]. Health systems worldwide areintegrating automated tools within electronic health records tomeasure frailty. However, using longitudinal data to measurefrailty and better predict outcomes among those with HF
has rarely been considered [2-5]. We sought to evaluate thepredictive value of adding longitudinal data to a standard
frailty index (FI) and evaluate predictions of 1-year outcomesin patients with HF.

Study Design

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This was a retrospective cohort study that used nationalVeterans Health Administration (VA) data. Veterans aged
≥50 years with an index hospital admission for HF from2016 to 2019 were included. We excluded veterans with<2 primary care visits in the 3 years before their date ofadmission to indicate regular use of VA care. We included
those with documentation of ejection fraction. We used thevalidated VA FI, which captures 31 deficits in health basedon International Classification of Diseases, Tenth Revision,and Current Procedural Terminology codes [6]. We estimated the FI for each preceding year, without overlap. Wefit a linear line to 3 calculated FIs for each year prior
to the index date of admission and reported the slope
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and intercept individually. This method provided a 3-year
longitudinal estimate of frailty at admission. We used 1-year
all-cause mortality following the index date of admission as
the primary outcome. We reported the area under the curve(AUC) for predicting outcomes, using logistic regression. We
estimated two AUCs: (1) FI at the time of admission (AUCFI)and (2) FI at time of admission plus slope and intercept(AUCfrailty trajectory (FT)+FI). Changes in the AUCs werereported as the percentage of improvement (ΔAUC = 100%× [AUCFT+FI – AUCFI]/AUCFI). We recursively calculated
the AUCs and ΔAUC by including patients whose FIs atadmission were <0.1 and, at each step, increased the FI levelby 0.01 to 0.4.

Ethical Considerations
The study protocol was approved by the Research &Development Committee of the Michael E. DeBakey
VA Medical Center and Baylor College of MedicineRazjouyan et al
Institutional Review Board (institutional review board
number: H-464220).

Results
In total, 54,774 veterans were included (age: mean 73.3, SD10.1 y; BMI: mean 30.1, SD 7.5 kg/m2; male: n=53,899,98.4%; White: n=30,406, 55.5%; Table 1). Figure 1 shows
the AUCFI and AUCFT+FI across the distribution of frailty
ranges, from prefrail (FI: 0.1-0.2) to frail; an FI of 0.2
is equivalent to an accumulation of 7 deficits among 31
variables, and the ΔAUC is also displayed. For all veteransacross all FI thresholds, the AUC improved by at least 4.1%
when adding the FT to the FI. The highest ΔAUC (24%) was
observed for FIs of 0.13 to 0.16, and it decreased to ≤10% for
FIs of ≥0.2.

Table 1. Characteristics of patients (N=54,774) with an index admission to the Veterans Health Administration for heart failure from January 1,2016, to January 1, 2020.
Characteristics
Admit year 2016, n (%)
Admit year 2017, n (%)

Patients
12,875 (23.5)
13,585 (24.8)

Admit year 2019, n (%)

14,232 (26)Admit year 2018, n (%)
Age (y), mean (SD)<65, n (%)

65‐75, n (%)≥85, n (%)

Sex, n (%)

MaleFemale

Race, n (%)

14,082 (25.7)
73.3 (10.1)

9776 (17.8)

22,772 (41.6)
22,226 (40.6)
53,899 (98.4)
875 (1.6)

White30,406 (55.5)

Othera15,028 (27.4)

Black

Hispanic ethnicity, n (%)

9340 (17.1)

2093 (3.8)

BMI (kg/m2), mean (SD)30.1 (7.5)

Frailty status (frailty index), mean (SD)0.35 (0.11)≥30, n (%)

Robust (<0.1), n (%)b

Prefrail (0.1‐0.2), n (%)b
Frail (>0.2), n (%)bAll-cause mortality, n (%)

24,352 (44.5)
297 (0.5)5715 (10.5)
48,762 (89)30-day mortality

2848 (5.2)

All-time mortality

37,027 (67.6)

1-year mortality

Time to death (mo), median (IQR)

14,460 (26.4)
18.2 (5.6-36.4)

HFrEFc, n (%)

27,223 (49.7)

HFpEFe, n (%)

23,005 (42.0)

HFmEFd, n (%)

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4546 (8.3)

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Characteristics
Living in a CLCf, n (%)

Razjouyan et alPatients
1808 (3.3)

a“Other” includes Asian, American Indian or Alaska Native, Native Hawaiian or other Pacific Islander, and unknown.
bStandardized frailty status cut points drawn from validated studies [6].
cHFrEF: heart failure with reduced ejection fraction of <40%.
dHFmEF: heart failure with modified reduced ejection fraction of 40%-50%.
eHFpEF: heart failure with preserved ejection fraction of >50%.
fCLC: community living center.

Figure 1. AUCs for patients who were admitted, for the first time, to the Veterans Health Administration for heart failure from January 1, 2016, toJanuary 1, 2020, and had an FI of 0.1-0.4 (as shown on the x-axis in increments of 0.01). We compared the AUCs of FIs (in blue; AUCFI) versus theAUCs of FIs and FTs combined (in orange; AUCFI+FT). The percentage of improvement in AUCs resulting from the addition of the FT to the FI wasAUCFI + FT − AUCFI× 100. AUC: area under the curve; FI:
reported in black (ΔAUC) and calculated by using the following formula: Δ AUC =AUCFI
frailty index; FT: frailty trajectory.

Discussion
In a national cohort of veterans who were admitted tothe VA for HF, the addition of longitudinal FT data resulted in a clinically significant (up to 24%) improvement in
1-year mortality prediction when compared to a standard FI
alone among patients in the prefrail range. In contrast, weobserved a modest (at least 4.1%) improvement in 1-year
mortality prediction in the overall population. Enhancing
AUC prediction for patients in the prefrail range is clinicallyimportant, as interventions that mitigate frailty may be mostimpactful in this population [7]. Patients with prefrailty may
benefit from interventions (eg, cardiac rehabilitation) that
improve frailty status and cardiovascular outcomes [1]. These
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findings enrich our understanding of the importance of FT inpatients at lower FI levels, and a previous study compared
the importance of FIs to that of FTs alone [5]. These results
may not generalize to nonveteran populations. The sample
was predominately male but did include a diverse population in terms of race, ethnicity, and geographic distribution.
In summary, methods for calculating frailty provide usefulpredictions of adverse outcomes among adults with HF. The
addition of longitudinal frailty data improves predictions forpatients with HF and prefrailty. These findings aid clinician and health system decision-making, as this population
benefits most from interventions that slow or prevent frailty
progression, and suggest that longitudinal data for modeling
FT provide additional evidence for tailoring interventions
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Razjouyan et alto patients with HF who may benefit most from tailoredinterventions.
Acknowledgments
JR is supported by seed funding from Baylor College of Medicine, Houston, TX, United States; the Center for Innovations inQuality, Effectiveness and Safety (CIN 13–413), Michael E. DeBakey VA Medical Center, Houston, TX, United States; andNational Institutes of Health (NIH), National Heart, Lung, and Blood Institute (NHLBI) K25 funding (1K25HL152006-01).
ARO is supported by VA Clinical Science Research & Development Career Development Award Level 2 (award IK2CX001800). MJH is supported by VA HSR CDA-2 award 1IK2HX003163-01A2.
Conflicts of Interest
None declared.
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Orkaby AR, Nussbaum L, Ho YL, et al. The burden of frailty among US veterans and its association with mortality,
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Ushijima A, Morita N, Hama T, et al. Effects of cardiac rehabilitation on physical function and exercise capacity inelderly cardiovascular patients with frailty. J Cardiol. Apr 2021;77(4):424-431. [doi: 10.1016/j.jjcc.2020.11.012][Medline: 33288376]
Abbreviations
AUC: area under the curve
FI: frailty index
FT: frailty trajectory
HF: heart failure
VA: Veterans Health Administration

Edited by Qiping Fan; peer-reviewed by Ahmed Hassan, Xiangwei Li; submitted 13.01.2024; final revised version received
29.04.2024; accepted 29.04.2024; published 27.06.2024
Please cite as:Razjouyan J, Orkaby AR, Horstman MJ, Goyal P, Intrator O, Naik AD
The Frailty Trajectory’s Additional Edge Over the Frailty Index: Retrospective Cohort Study of Veterans With Heart
FailureJMIR Aging 2024;7:e56345
URL: https://aging.jmir.org/2024/1/e56345
doi: 10.2196/56345© Javad Razjouyan, Ariela Orkaby, Molly Horstman, Parag Goyal, Orna Intrator, Aanand D Naik. Originally published inJMIR Aging (https://aging.jmir.org), 27.06.2024. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work, first published in JMIR Aging, is properly cited. The complete

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Razjouyan et albibliographic information, a link to the original publication on https://aging.jmir.org, as well as this copyright and license
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