Search

Background Approaches for quantifying physical resilience in older adults have not been described. Methods We apply two conceptual approaches to defining physical resilience to existing longitudinal data sets in which outcomes are measured after an acute physical stressor. A “recovery phenotype” approach uses statistical methods to describe how quickly and completely a patient recovers. Statistical methods using a recovery phenotype approach can consider multiple outcomes simultaneously in a composite score (eg, factor analysis and principal components analysis) or identify groups of patients with similar recovery trajectories across multiple outcomes (eg, latent class profile analysis). An “expected recovery differential” approach quantifies how patients’ actual outcomes are compared to their predicted outcome based on a population-derived model and their individual clinical characteristics at the time of the stressor. Results Application of the approaches identified different participants as being the most or least physically resilient. In the viral respiratory cohort (n = 186) weighted kappa for agreement across resilience quartiles was 0.37 (0.27–0.47). The expected recovery differential approach identified a group with more comorbidities and lower baseline function as highly resilient. In the hip fracture cohort (n = 541), comparison of the expected recovery differentials across 10 outcome measures within individuals provided preliminary support for the hypothesis that there is a latent resilience trait at the whole-person level. Conclusions We posit that recovery phenotypes may be useful in clinical applications such as prediction models because they summarize the observed outcomes across multiple measures. Expected recovery differentials offer insight into mechanisms behind physical resilience not captured by age and other comorbidities.

Cognitive and visual impairments frequently coexist. With the aging of populations worldwide, the prevalence of these conditions are projected to increase substantially over time. A number of studies suggest that cognitive function and vision impairment are associated, and it is hypothesized to be due to a (1) common cause etiology, where both share common risk factors, and/or (2) causal association, where visual impairment causes cognitive decline. Sensory loss can lead to increased cognitive load, structural and functional changes in the brain, and/or decreased emotional, social, and physical well-being, all of which could potentially increase the risk of cognitive impairment. We conducted a systematic review of the existing literature, examining the association between cognitive and visual impairment among older adults. A total of 80 observational studies that reported a measure of association between visual and cognitive function and met the following criteria were included: (1) cross-sectional or longitudinal study design, (2) baseline mean age of participants ≥50 years, and (3) sample size of ≥100 participants. Of these 80 studies, 56 found a positive, significant association between visual function and cognitive decline. Forty-nine of the 56 studies used objective measures to test for visual acuity, contrast sensitivity, or visual fields. The sample included participants from 14 countries, including the US, UK, China, and Australia among others. Converging evidence of an association between vision impairment and subsequent cognitive decline suggests that visual impairment is a possible modifiable risk factor for cognitive decline and dementia. This hypothesis should be tested in prospective, controlled studies.

In 2019, the World Health Organization World Report on Vision estimated that that 2.2 billion people have a vision impairment, of which almost half could have been prevented or is yet to be addressed. As the global population ages and the prevalence of visual impairment increases, inequities in eye care and the downstream health and aging consequences of vision loss will become magnified. This session will: (1) provide key information regarding the burden of eye disease and visual impairment among older adults worldwide; (2) outline a framework created to conceptualize the aging and long-term health implications of vision loss, and (3) discuss the global public health challenges to eye care and to maximizing health for older adults with visual impairments.

Older adults can experience vision impairment following stroke in combination with pre-existing ophthalmologic disease. The new ICD-10 coding system for identifying vision related health conditions provides a much higher level of detail for coding these complex scenarios than the previous ICD-9 system. While this new coding system has advantages for clinical care and billing, the degree to which providers are utilizing the expanded code structure is unknown. The study objective was to describe the use of ICD-10 vision codes in a large cohort of stroke survivors. We used a retrospective cohort design to study national 100% Medicare claims files from 2015 through 2017. Data were analyzed using all available ICD-10 vision codes for beneficiaries who had an acute care stay because of a stroke and who also had an ICD-10 visual code recorded at least once in their claims chart. The cohort (n= 269,314) was mostly female (57.1%) with ischemic stroke (87.8%). Approximately 15% were coded as having one or more vision impairments. Unspecified glaucoma was the most frequently used code among men (2.83%), beneficiaries over 85+ (4.80%) and non-Hispanic blacks (4.12%). But multiple vision codes were used in few patients, overall (0.6%). Less than 3% of those in the oldest group (85+ years) had two vision codes noted in their claims. Despite more available codes, the coding used to describe the vision impairments in this population of stroke survivors was not specific or diverse. Hospital providers should pay attention to specificity in order to improve coding practices.

Background Resilience has been described in the psychosocial literature as the capacity to maintain or regain well-being during or after adversity. Physical resilience is a newer concept that is highly relevant to successful aging. Our objective was to characterize the emerging construct of resilience as it pertains to physical health in older adults, and to identify gaps and opportunities to advance research in this area. Methods We conducted a systematic review to identify English language papers published through January 2015 that apply the term "resilience" in relation to physical health in older adults. We applied a modified framework analysis to characterize themes in implicit or explicit definitions of physical resilience. Results Of 1,078 abstracts identified, 49 articles met criteria for inclusion. Sixteen were letters or concept papers, and only one was an intervention study. Definitions of physical resilience spanned cellular to whole-person levels, incorporated many outcome measures, and represented three conceptual themes: resilience as a trait, trajectory, or characteristic/capacity. Conclusions Current biomedical literature lacks consensus on how to define and measure physical resilience. We propose a working definition of physical resilience at the whole person level: a characteristic which determines one's ability to resist or recover from functional decline following health stressor(s). We present a conceptual framework that encompasses the related construct of physiologic reserve. We discuss gaps and opportunities in measurement, interactions across contributors to physical resilience, and points of intervention.

Combinations of sensory and cognitive impairments are common and associated with significant disability. We examined the association between self-reported hearing impairment (HI), vision impairment (VI), and dual sensory impairment (DSI), stratified by dementia status, on hospital admissions and healthcare costs. We used a nationally representative sample of 24,009 community-dwelling adults aged 65+ years from the Medicare Current Beneficiary Survey between 1999–2006. Dementia status was determined by self-report or diagnosis codes in claims data. Primary outcomes included risk of hospital admission (derived from Medicare claims), Medicare Fee-for-Service costs (verified by Medicare claims), and total healthcare costs (combining Medicare claims and other self-reported payments). Self-reported DSI was present in 30.2% (n=263/871) of participants with dementia and 17.8% (n=4,112/23,138) without dementia. In multivariable logistic regression models, HI, VI, or DSI were associated with increased odds of hospitalization regardless of dementia status. In a generalized linear model with Gamma distribution and log-link adjusted for demographics, annual total healthcare costs were greater for those with dementia and DSI compared to without dementia and with DSI ($28,875 vs. $3,340, respectively). Presence of any sensory impairment was generally associated with higher costs. However, in fully adjusted models including demographics, Medicaid status, and chronic conditions, DSI compared with no sensory impairment was not associated with much higher costs in those with dementia ($11,303 vs. $10,466, p=0.395) or without dementia ($1,151 vs. $1,056, p

Defining common patterns of recovery after an acute health stressor (resiliency phenotypes) has clinical and research implications. We examined groups of patients with similar recovery patterns across 10 outcomes following hip fracture to determine the most important predictors of resiliency group membership. This study is a secondary analysis of three prospective cohort studies. Participants, community-dwelling adults aged >65 with recent surgical repair of a hip fracture (n=541), were recruited from eight hospitals near Baltimore and followed for up to one year. Self-reported function and activity measures were collected using validated scales at baseline, 2, 6, and 12 months. Physical performance tests were administered at all follow-up visits. Stressor characteristics, co-morbidities, psychosocial and environmental factors were collected at baseline, and latent class profile analysis was used to identify resiliency phenotypes and logistic regression models to identify associated factors. Three resiliency phenotypes had similar patterns across the 10 outcome measures and were defined as “high resilience” (n=163, 30.1%), “medium resilience” (n=242, 44.7%), and “low resilience” (n=136, 25.2%). Recovery trajectories for outcome measures were plotted for each resiliency group. Self-reported pre-fracture function was by far the strongest predictor of resilience group membership (AUC 0.84). Demographic factors, co-morbidities, stressor characteristics, environmental factors, and psychosocial characteristics were less predictive, but several factors remained significant in a fully adjusted model (AUC 0.88). These three resiliency phenotypes have immediate utility for clinical decision-making. They can be measured in future studies with a more parsimonious set of variables, and may prove useful for understanding mediators of physical resilience.

The combination of sensory and cognitive impairment is increasingly prevalent among older adults and may be an important driver of healthcare cost due to functional disability and reduced self-care. This presentation focuses on the relationship between hearing and/or vision impairment and cognitive impairment with hospital admissions and healthcare cost using data from the Medicare Current Beneficiary Survey, a nationally representative sample of community-dwelling adults. We show that the presence of sensory impairment is associated with increased risk of hospitalization regardless of dementia status. In adjusted models, annual total healthcare costs were generally higher among those with sensory impairments compared to those without sensory impairments. We will also discuss work related to the development of a prognostic model that provides estimates of hospitalization risk among older adults with self-reported hearing and/or vision impairment. This model may help inform allocation of health care resources to those at highest risk for adverse outcomes.

Early Alzheimer’s Disease (AD) can be difficult to diagnose with traditional cognitive assessment, but brain imaging studies suggest that people with early AD exhibit an altered metabolic response to certain tasks. This proof-of-concept study was designed to determine whether differences in brain metabolic activity with increasing task demand were related to one’s risk of AD. We recruited 26 individuals (age 59–72) with prior genetic testing who had normal cognitive scores and no cognitive complaints. Because the APOE e4 allele is linked to higher risk and earlier onset of AD, we included 13 APOE e4 carriers (high risk group) and a demographically balanced sample of 13 APOE e4 non-carriers (low risk group). Participants performed executive control tasks while undergoing functional magnetic resonance imaging (fMRI). Participants performed an easier version (neutral condition) followed by a harder version (stress condition) of the task. Groups did not differ in accuracy or response time in the neutral or stress condition. Next, we considered the change in brain activation between neutral and stress conditions. The low risk group exhibited larger increases in activation in right frontoparietal regions, even after adjustment for age. Age was associated with greater neutral-to-stress-condition increase in activation, but the age effect was less pronounced in the high risk group. Our hypothesis is that recruitment of supplemental networks helps support executive control in late life, and early AD may interfere with engagement of these resources. Although less activation was not associated with worse performance here, performance deterioration may occur with more challenging tasks.

Dr. Whitson will elaborate the emerging construct of physical resilience, defined as one’s capacity to resist or recover from functional decline following an acute or chronic health stressor. The talk will present a conceptual framework whereby physical resilience is constrained by physiological reserve at the level of cells, tissues, or organ systems. In this model, resilient outcomes are further influenced by factors such as psychological attributes, environment, and social support. Two methodological approaches to quantifying physical resilience will be presented through an example that uses data from older adults admitted to the hospital for upper respiratory infections. One approach characterizes resilient phenotypes based solely on observed outcomes (e.g., survival, intensity of services required). The second approach utilizes propensity scores to identify individuals whose observed outcomes were significantly better (more resilient) or worse (less resilient) that we would have predicted, based on pre-stressor characteristics.

Physical resilience, defined as one’s ability to withstand or recover from functional decline following an acute or chronic health stressor, is a concept that resonates with older patients and providers. Predicting an individual’s likelihood of a successful response to a given stressor would inform medical decision-making and help target resources. In August 2015, the National Institute on Aging convened a workshop to outline research needs to improve measures to predict and assess resilience in human aging (Hadly EC et al, J Gerontol A Biol Sci Med Sci 2017. 72(7): 980–990). One need that was identified was feasible clinical tests, such as simulation tests, to predict and assess resilient responses to “real world” stressors. This session, moderated by NIA Program Officers, will present several test paradigms currently under study as predictors of physical resilience to common stressors. Each test quantifies the individual’s response to a certain perturbation (e.g., vaccine, anesthesia, change of position) for the purpose of predicting future recovery from a health stressor. First, the moderators will introduce the emerging conceptual framework of physical resilience, emphasizing the dynamic nature of physiologic responses to stressors and their role in maintaining or regaining normal homeostasis and function. Brief presentations will follow on active lines of research, linked through the NIA-funded Physical Resiliencies: Indicators and Mechanisms in the Elderly (PRIME) Collaborative. Each speaker will describe a clinical test paradigm which is employed before, during, or soon after a stressor and will present data on the test’s ability to predict physical resilience.

As the global population ages, identifying ways to optimize recovery following acute and chronic stressors will be important to public health. Understanding “physical resilience”, a characteristic at the whole person level which determines an individual’s ability to resist functional decline or recover physical health following a stressor, is therefore of intense interest. Physical resilience is thought to be constrained in part by underlying physiologic reserve across organ systems, and further influenced by factors such as genetics, the environment, and psychosocial factors including psychological resilience. Measuring physical resilience is a critical but challenging first step in identifying potential pathways for intervention.

The precise characterization of frailty as a state of physiologic vulnerability has been extremely useful for research (1,2). In particular, the frailty index first validated in the Cardiovascular Health Study (CHS) is a reliable predictor of adverse outcomes and a valuable and versatile tool (3–7). In contrast, conceptualizing frailty as physiologic vulnerability can be problematic, partly because clinicians typically apply the word “frail” to functionally limited or even overtly disabled elders who are suffering the cumulative effects of disease-related, psychosocial, and environmental challenges (8–13). Thus, the research definition creates cognitive dissonance because it does not fully equate to what many physicians have in mind when they envision a frail elder (Figure 1). Figure 1 Hypothetical Venn diagram depicting incomplete overlap between individuals identified by the Cardiovascular Health Study (CHS) Frailty Index versus the conventional clinical notion of frailty. If a state of pure physiologic vulnerability exists, perhaps we should call it “phrailty” (physiologic frailty), with “F–frailty” (full-blown functional frailty) reserved for multi-factorial vulnerability that is accompanied by functional limitations and almost always reflects comorbidities as well as environmental and psychosocial interactions. To be clear, we introduce this terminology to make a point and are not proposing that the field should actually adopt new vocabulary. After all, if phrailty in the most extreme sense refers to physiologic vulnerability before any apparent clinical manifestations, it would be impossible to identify phrail patients for observation and study, and the definition would have little practical utility. For the sake of discussion, however, let us imagine phrailty and F-frailty as overlapping states within a spectrum that encompasses both the research definition, which relies heavily on physiologic compromise, and a more traditional notion of frailty, which takes into account functional limitations and external factors such as environment and social support (Figure 2). Figure 2 The frailty spectrum within the geriatric functional continuum, based on the Hamerman model. Hamerman’s geriatric functional continuum depicts frailty as one of several midpoints between independence and pre-death (14). In this adaptation, frailty ... There is nothing new about the idea that different degrees of frailty exist, nor are we the first to suggest that the varying shades of frailty are intuitively determined by functional capacity. The American Geriatric Society Intervention on Frailty Working Group has recognized that “a physically frail state may be clinically detected before disability, as well as a more advanced state of ‘clinically overt’ physical frailty that has already determined some initial degree of functional disability (15).” We agree with the suggestion that the state of physiologic vulnerability, especially in the absence of devastating functional loss, has such important implications that it warrants special attention. However, we do not believe that most of the popular frailty phenotypes adequately distinguish between isolated physiologic vulnerability and more severe functional compromise. For example, the CHS Frailty Index includes usual gait speed (4), a measure of functional performance which is likely to be abnormal both early and late in the disablement process. The problem is not that gait speed is included in the diagnostic criteria for frailty; rather, the problem is that the criteria fail to distinguish between persons with physiologic derangement who are at risk of functional decline and persons who are already severely functionally limited or disabled. Acknowledging the degree of functional compromise associated with an individual’s frailty (is it phrailty or F-frailty?) may have important advantages for both research and clinical care. Of course, a potential caveat in considering functional capacity in any working definition of frailty is that it raises the question of how, or whether, frailty is distinct from disability. The central feature that distinguishes frailty from disability in the minds of clinicians may be the recognition of frailty as a multifactorial state brought about by accumulated conditions and age-related processes. We submit that a hallmark of the most advanced stage of the frailty process is compromised function, that compromised function differs from and often precedes dependency and disability, and that frailty with functional compromise deserves specific consideration and distinction from physiologic vulnerability in the absence of functional loss. This notion calls to mind previous World Health Organization (WHO) and Nagi models of the disablement process, in which impairment (physiologic vulnerability) is distinguished from functional limitation (e.g., walking speed), and both concepts are distinct from dependency (e.g., activities of daily living [ADL] disability) (16). In our model of phrailty and F-frailty, patients may progress from one state to another along a theoretical continuum, although this is not always the case. F-frailty might result after a phrail patient develops new comorbidities or environmental challenges that cause him to fall below some threshold of cumulative reserve necessary to remain functional. Alternatively, F-frailty could occur in the absence of any physical decline or underlying physiologic process. For example, a clinician might label an older patient who lives alone and suffers from vision loss and moderate dementia as frail, even if the patient is physically robust and would not score positively on measures that focus on a physiological phenotype (e.g., nutritional status, strength, mobility, endurance). Likewise, this patient might not be categorized as frail if he were assessed in a well-lit clinical setting, but he appears quite frail when observed on an unfamiliar, dark, cobblestone street. Although many existing measures of frailty do not account for factors which are external to the patient (4,15,17), the traditional geriatric approach is to consider psychosocial and environmental issues as crucial components in determining an older patient’s vulnerability. We also note that experimental model species of aging and disablement have supported the notion that the consequences of physical impairments on function and life span are “inextricably linked to the environments in which the impaired individuals reside (18).” Figure 3 illustrates this theoretical relationship between physiologic reserve and psychosocial and/or environmental reserve as contributors to a conventional clinical concept of frailty (F-frailty). Any patient could be assigned a location on the graph based on his physiologic reserve (x axis coordinate) and level of psychosocial and/or environmental support (y axis coordinate). We have drawn a hypothetical line, with patients who graph to the left of the line considered F-frail. A patient with relatively good physiologic reserve (positive x axis location) could cross the “F-frailty line” if he becomes severely depressed, loses his spouse, or moves to an apartment with no elevator (descending y axis location). Figure 3 Theoretical relationship between physiologic reserve and psychosocial and/or environmental reserve determining F-frailty status. Any patient with a specific level of physiologic reserve (x axis) and a specific level of psychosocial/environmental support ... For clinical research purposes, it is useful to draw a clear distinction between patients in a state of physiologic vulnerability but relatively preserved function, and those who are overtly functionally compromised. Physiologically vulnerable patients represent excellent potential targets for intervention and therapy, whereas F-frailty may be more tractable. Including both types of frail patients in clinical trials could wash out effects, if more severely frail patients are more or less amenable to intervention. Unless the distinction is made very clear (e.g., stratification by functional categories), patients and their physicians may be confused by the application of the term “frailty,” and pharmaceutical companies and institutional review boards may be skeptical of trials that propose to enroll “frail” patients. The emerging and interwoven fields of frailty and disability face many challenges with terminology, and a discourse about the precise meaning of labels and words is both inevitable and desirable. Current frailty phenotypes are powerful research tools because they help to delineate a clinically important condition and have made important innovations and insights possible in this field. However, the condition defined by some of these tools may not overlap completely with the conventional biopsychosocial connotation of frailty. We believe that both understandings of frailty would benefit from distinctions that emphasize functional limitation as an indicator of vulnerability. The traditional clinical notion of frailty is broader and more elusive than a state of physiologic vulnerability (phrailty) and may not lend itself as readily to operationalization by standard criteria. Admittedly, almost any stressor of any type could contribute to diminishing functional reserve, and thus, to the traditional concept of frailty as we’ve characterized it. Most clinicians and patients apply the term frailty by gestalt: They know it when they see it. However, confusion becomes likely when this clinical understanding is at odds with the research definition. The field of frailty may become a bit less murky if clinicians and investigators explicitly note the degree of functional loss associated with any frail state and acknowledge whether the factors contributing to their own assessment of frailty are purely physiologic.