Dataset Information

Development of a Rat Clinical Frailty Index.

ABSTRACT: Rats are a commonly used model for aging studies, and a frailty assessment tool for rats would be of considerable value. There has been a recent focus on the development of preclinical models of frailty in mice. A mouse clinical frailty index (FI) was developed based on clinical frailty assessment tools. This FI measures the accumulation of clinically evident health-related deficits in mice. This paper aimed to develop a rat clinical FI. Male Fischer 344 rats were aged from 6 to 9 months (n = 12), and from 13 to 21 months (n = 41). A FI comprised of 27 health-related deficits was developed from a review of the literature and consultation with a veterinarian. Deficits were scored 0 if absent, 0.5 if mild, or 1 if severe. A FI score was determined for each rat every 3-4 months, and for the older group mortality was assessed up to 21 months. Mean FI scores significantly increased at each time point for the older rats. A high FI score measured at both 17 months of age and terminally was also associated with decreased probability of survival as assessed with Kaplan-Meier curves. The rat clinical FI has significant value for use in aging and interventional studies.

SUBMITTER: Yorke A

PROVIDER: S-EPMC5458399 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Development of a Rat Clinical Frailty Index.

Yorke Amy A Kane Alice E AE Hancock Friesen Camille L CL Howlett Susan E SE O'Blenes Stacy S

The journals of gerontology. Series A, Biological sciences and medical sciences 20170701 7

Rats are a commonly used model for aging studies, and a frailty assessment tool for rats would be of considerable value. There has been a recent focus on the development of preclinical models of frailty in mice. A mouse clinical frailty index (FI) was developed based on clinical frailty assessment tools. This FI measures the accumulation of clinically evident health-related deficits in mice. This paper aimed to develop a rat clinical FI. Male Fischer 344 rats were aged from 6 to 9 months (n = 12 ...[more]

PMID: 28158648

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Project description:BackgroundPhysical frailty phenotype is characterized by decreased physiologic reserve to stressors and associated with poor outcomes, such as delirium and mortality, that may result from post-kidney transplant (KT) inflammation. Despite a hypothesized underlying pro-inflammatory state, conventional measures of frailty typically do not incorporate inflammatory biomarkers directly. Among KT candidates and recipients, we evaluated the inclusion of inflammatory biomarkers with traditional physical frailty phenotype components.MethodsAmong 1154 KT candidates and recipients with measures of physical frailty phenotype and inflammation (interleukin 6 [IL6], tumor necrosis factor alpha [TNFα], C-reactive protein [CRP]) at 2 transplant centers (2009-2017), we evaluated construct validity of inflammatory-frailty using latent class analysis. Inflammatory-frailty measures combined 5 physical frailty phenotype components plus the addition of an individual inflammatory biomarkers, separately (highest tertiles) as a sixth component. We then used Kaplan-Meier methods and adjusted Cox proportional hazards to assess post-KT mortality risk by inflammatory-frailty (n = 378); Harrell's C-statistics assessed risk prediction (discrimination).ResultsBased on fit criteria, a 2-class solution (frail vs nonfrail) for inflammatory-frailty was the best-fitting model. Five-year survival (frail vs nonfrail) was: 81% versus 93% (IL6-frailty), 87% versus 89% (CRP-frailty), and 83% versus 91% (TNFα-frailty). Mortality was 2.07-fold higher for IL6-frail recipients (95% CI: 1.03-4.19, p = .04); there were no associations between the mortality and the other inflammatory-frailty indices (TNFα-frail: 1.88, 95% CI: 0.95-3.74, p = .07; CRP-frail: 1.02, 95% CI: 0.52-2.03, p = .95). However, none of the frailty-inflammatory indices (all C-statistics = 0.71) improved post-KT mortality risk prediction over the physical frailty phenotype (C-statistics = 0.70).ConclusionsMeasurement of IL6-frailty at transplantation can inform which patients should be targeted for pre-KT interventions. However, the traditional physical frailty phenotype is sufficient for post-KT mortality risk prediction.

Project description:ObjectivesDetermine the effects of missing data in frailty identification and risk prediction.DesignAnalysis of the National Health in Aging Trends Study.SettingCommunity.ParticipantsAbout 6206 older adults.MeasurementsA 41-variable frailty index (FI) was constructed with the following domains: comorbidities, activities of daily living (ADLs), instrumental activities of daily living, self-reported physical limitations, physical performance, and neuropsychiatric tests. We evaluated discrimination after removing single and multiple domains, comparing C-statistics for predicting 5-year risk of mortality and 1-year risks of disability and falls.ResultsThe full FI yielded a mean of .18 and C-statistics of .72 (95% confidence interval, .70-.74) for mortality, .80 (.77-.82) for disability, and .66 (.64-.68) for falls. Removal of any single domain shifted the FI distribution, resulting in a mean FI ranging from .13 (removing comorbidities) to .20 (removing ADLs) and frailty prevalence (FI ≥ .25) from 16.0% to 28.7%. Among robust participants models missing ADLs misclassified most often, (19% as pre-frail). Among pre-frail and frail participants missing comorbidities misclassified most often(69.2% from pre-frail to robust, 24% from frail to pre-frail, and 4.9% from frail to robust). Removal of any single domain minimally changed C-statistics: mortality, .71-.73; disability, .79-.80; and falls, .64-.66. Removing neuropsychiatric testing and physical performance yielded comparable C-statistics of .70, .78, and .66 for mortality, ADLs, and falls, respectively. However, removal of three or four domains based on likely availability decreased C-statistics for mortality (.69, .66),disability (.75, .70), and falls (.64, .63), respectively.ConclusionWhile FI discrimination is robust to missing information in any single domain, risk prediction is affected by absence of multiple domains. This work informs the application of FI as a clinical and research tool. J Am Geriatr Soc 68:1771-1777, 2020.

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Development of a Rat Clinical Frailty Index.

Publications

Development of a Rat Clinical Frailty Index.

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