Published on in Vol 7 (2024)

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The Frailty Trajectory’s Additional Edge Over the Frailty Index: Retrospective Cohort Study of Veterans With Heart Failure

The Frailty Trajectory’s Additional Edge Over the Frailty Index: Retrospective Cohort Study of Veterans With Heart Failure

The Frailty Trajectory’s Additional Edge Over the Frailty Index: Retrospective Cohort Study of Veterans With Heart Failure

1Baylor College of Medicine, , Houston, TX, , United States

2Institute on Aging, University of Texas Health Science Center, , Houston, TX, , United States

3VA Health Services Research & Development, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, , Houston, TX, , United States

4Big Data Scientist Training Enhancement Program, VA Office of Research and Development, , Washington, DC, , United States

5New England Geriatrics Research, Education, and Clinical Center, VA Boston Health Care System, , Boston, MA, , United States

6Brigham & Women's Hospital, Harvard Medical School, , Boston, MA, , United States

7Division of General Internal Medicine, Department of Medicine, Weill Medical College of Cornell University, , New York, NY, , United States

8Geriatrics and Extended Care Data Analysis Center, Canandaigua VA Medical Center, , Canandaigua, NY, , United States

9Public Health Sciences, University of Rochester School of Medicine and Dentistry, , Rochester, NY, , United States

10Department of Management, Policy, and Community Health, School of Public Health, University of Texas Health Science Center, , Houston, TX, , United States

Corresponding Author:

Javad Razjouyan, PhD

Individuals with heart failure (HF) have a high burden of health 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 are integrating automated tools within electronic health records to measure frailty. However, using longitudinal data to measure frailty and better predict outcomes among those with HF has rarely been considered [2-5]. We sought to evaluate the predictive value of adding longitudinal data to a standard frailty index (FI) and evaluate predictions of 1-year outcomes in patients with HF.

Study Design

This was a retrospective cohort study that used national Veterans Health Administration (VA) data. Veterans aged ≥50 years with an index hospital admission for HF from 2016 to 2019 were included. We excluded veterans with <2 primary care visits in the 3 years before their date of admission to indicate regular use of VA care. We included those with documentation of ejection fraction. We used the validated VA FI, which captures 31 deficits in health based on International Classification of Diseases, Tenth Revision, and Current Procedural Terminology codes [6]. We estimated the FI for each preceding year, without overlap. We fit a linear line to 3 calculated FIs for each year prior to the index date of admission and reported the slope 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 were reported as the percentage of improvement (ΔAUC = 100% × [AUCFT+FI – AUCFI]/AUCFI). We recursively calculated the AUCs and ΔAUC by including patients whose FIs at admission were <0.1 and, at each step, increased the FI level by 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 Medicine Institutional Review Board (institutional review board number: H-464220).

In total, 54,774 veterans were included (age: mean 73.3, SD 10.1 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 veterans across 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.
Admit year 2016, n (%)12,875 (23.5)
Admit year 2017, n (%)13,585 (24.8)
Admit year 2018, n (%)14,082 (25.7)
Admit year 2019, n (%)14,232 (26)
Age (y), mean (SD)73.3 (10.1)
<65, n (%)9776 (17.8)
65‐75, n (%)22,772 (41.6)
≥85, n (%)22,226 (40.6)
Sex, n (%)
Male53,899 (98.4)
Female875 (1.6)
Race, n (%)
White30,406 (55.5)
Black9340 (17.1)
Othera15,028 (27.4)
Hispanic ethnicity, n (%)2093 (3.8)
BMI (kg/m2), mean (SD)30.1 (7.5)
≥30, n (%)24,352 (44.5)
Frailty status (frailty index), mean (SD)0.35 (0.11)
Robust (<0.1), n (%)b297 (0.5)
Prefrail (0.1‐0.2), n (%)b5715 (10.5)
Frail (>0.2), n (%)b48,762 (89)
All-cause mortality, n (%)
30-day mortality2848 (5.2)
1-year mortality14,460 (26.4)
All-time mortality37,027 (67.6)
Time to death (mo), median (IQR)18.2 (5.6-36.4)
HFrEFc, n (%)27,223 (49.7)
HFmEFd, n (%)4546 (8.3)
HFpEFe, n (%)23,005 (42.0)
Living in a CLCf, n (%)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, to January 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 the AUCs 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 was reported in black (ΔAUC) and calculated by using the following formula: ΔAUC= (AUCFI+FTAUCFI)AUCFI ×100. AUC: area under the curve; FI: frailty index; FT: frailty trajectory.

In a national cohort of veterans who were admitted to the 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, we observed 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 clinically important, as interventions that mitigate frailty may be most impactful in this population [7]. Patients with prefrailty may benefit from interventions (eg, cardiac rehabilitation) that improve frailty status and cardiovascular outcomes [1]. These findings enrich our understanding of the importance of FT in patients 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 useful predictions of adverse outcomes among adults with HF. The addition of longitudinal frailty data improves predictions for patients 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 to patients with HF who may benefit most from tailored interventions.


JR is supported by seed funding from Baylor College of Medicine, Houston, TX, United States; the Center for Innovations in Quality, Effectiveness and Safety (CIN 13–413), Michael E. DeBakey VA Medical Center, Houston, TX, United States; and National 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 IK2-CX001800). MJH is supported by VA HSR CDA-2 award 1IK2HX003163-01A2.

Conflicts of Interest

None declared.

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AUC: area under the curve
FI: frailty index
FT: frailty trajectory
HF: heart failure
VA: Veterans Health Administration

Edited by Qiping Fan; submitted 13.01.24; peer-reviewed by Ahmed Hassan, Xiangwei Li; final revised version received 29.04.24; accepted 29.04.24; published 27.06.24.


© Javad Razjouyan, Ariela Orkaby, Molly Horstman, Parag Goyal, Orna Intrator, Aanand D Naik. Originally published in JMIR Aging (, 27.6.2024.

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