Phenotype Model Of Frailty Research Articles

A scoping review of the measurement and analysis of frailty in randomised controlled trials.

Frailty is of increasing interest in trials, either as a target of intervention, as an outcome or as a potential treatment modifier. However, frailty measurement is often highly variable. This scoping review assessed how frailty is quantified in randomised controlled trials (RCTs), in what context and for what purposes. We searched five electronic databases for RCTs in which frailty was measured among trial participants. We extracted data on intervention type, the frailty measure used and the purpose for which frailty was assessed. We then compared these data according to reasons for frailty assessment. We identified 415 RCTs assessing frailty across a range of interventions. Frailty was used to define the target population (166 trials), as an outcome (156 trials), as an effect modifier examining interaction of frailty on treatment effect (61 trials), as a purely descriptive characteristic (42 trials) or as a prognostic marker examining the impact of frailty on future health outcome (78 trials). The trials used 28 different measures of frailty (plus 29 additional trial-specific measures). The frailty phenotype model was the most common overall (164 trials), for defining the target population (90/166 trials) and as an outcome (81/156 trials). The cumulative deficit model frailty index was also frequently used (102 trials) and was most common among trials assessing treatment effect modification (21/61 trials). Frailty measurement in RCTs is highly variable. Understanding the properties of respective frailty measures and how these relate to frailty as encountered in clinical practice is a priority to ensure that trial findings can inform healthcare delivery for people living with frailty.

Frailty assessment tools and their predictive value in preoperative evaluation for general surgery: a narrative review

The world’s population is ageing rapidly, with projections suggesting a significant increase in the elderly demographic over the next few decades. This shift is likely to result in long periods of infirmity and frailty, thus escalating healthcare demands. Frailty has been investigated almost exclusively in the elderly because of its association with the senior population. It is considered to be an unavoidable aspect of old age, rather than a variable aspect of ageing. It has been linked to a greater risk of falls and decreased mobility, increased hospitalizations, institutionalization, and mortality. Due to elevated complication risk, the frail group required expert surgical treatment and specialised care. Various models have been described in recent years, including the Frailty Phenotype model, the Edmonton Frailty scale, and the Clinical Frailty scale, among many others. Each model employs several components to assess an individual’s frailty. There is, however, no gold standard for measuring fragility. The use of such assessment methods in pre-operative frailty screening as a risk-stratification instrument has been shown to be effective in older persons undergoing general surgery. The use of frailty assessment tools to determine the level of frailty together with evidence-based treatment and management should be supported by collaborative decision making. In this narrative review, we discussed the four commonly used frailty assessment tools with a particular emphasis on the need for additional research to refine these models and enhance their predictive accuracy in surgical settings.

Cognitive impairment in people with physical frailty using the phenotype model: A systematic review and meta analysis.

We performed a systematic review and meta-analysis to study the relationship between cognitive functioning and phenotypic frailty status. We searched Pubmed, Cochrane Library and Epistemonikos from 2000 until March 2022, and used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Samples included both sexes, age ≥55years, assessed with standardized measures of the different cognitive domains and the frailty phenotype model and analyzing the relationship between the frailty subtypes pre-frail, frail and robust and specific cognitive function. Eleven studies published from 2008 until March 2022 fulfilled the inclusion criteria, and 10 were included in our meta-analyses. Sample sizes varied from 104 to 4649 individuals. Mean Mini-Mental State Examination (MMSE) scores ranged from 17.0 to 27.6, with mean difference (MD) of -2.55 (95% confidence interval [CI] -3.32, -1.78) in frail compared to robust, MD -1.64 (95% CI -2.21, -1.06) in frail compared to prefrail and MD -0.68 (95% CI -0.94, -0.43) in prefrail compared to robust. In subgroup analyses, frail persons had lower scores in the memory domain with standardized mean difference (SMD) -1.01 (95% CI -1.42, -0.59). MMSE scores were significantly lower in frail compared to robust and prefrail persons and in prefrail compared to robust persons. Subgroup analysis of memory revealed significantly poorer scores in frail compared to robust. The results indicate a strong relationship between physical frailty and cognitive impairment suggesting incorporation of cognitive function in frailty assessments.

Frailty Risks of Prescription Analgesics and Sedatives across Frailty Models: the Health and Retirement Study.

Limited evidence for incident frailty risks associated with prescription analgesics and sedatives in older (≥65 years) community-living adults prompted a more comprehensive investigation. We used data from older Health and Retirement Study respondents and three frailty models (frailty index, functional domain, frailty phenotype with 8803, 10,470, and 6850 non-frail individuals, respectively) and estimated sub-hazard ratios of regular prescription drug use (co-use, analgesic use, and sedative use), by frailty model. We addressed confounding with covariate adjustment and propensity score matching approaches. The baseline prevalence of analgesic and sedative co-use, analgesic use, and sedative use among non-frail respondents was 1.8%, 12.8%, and 4.7% for the frailty index model, 4.2%, 16.2%, and 5.3% for the functional domain model, and 4.3%, 15.4%, and 6.1% for the frailty phenotype model, respectively. Cumulative frailty incidence over 10 years was 39.3%, 36.1%, and 14.2% for frailty index, functional domain, and frailty phenotype models, respectively; covariate-adjusted sub-hazard ratio estimates were 2.00 (1.63-2.45), 1.83 (1.57-2.13), and 1.68 (1.21-2.33) for co-use; 1.72 (1.56-1.89), 1.38 (1.27-1.51), and 1.51 (1.27-1.79) for analgesic use; and 1.46 (1.24-1.72), 1.25 (1.07-1.46), and 1.31 (0.97-1.76) for sedative use. Frailty risk ranking (co-use > analgesic use > sedative use) persisted across all model sensitivity analyses. Consistently significant frailty risk estimates of regular prescription analgesic and sedative co-use and of prescription analgesic use support existing clinical, public health, and regulatory guidance on opioid and benzodiazepine co-prescription, on opioid prescription, and on NSAID prescription. Frailty phenotype measurement administration limited power to detect significant frailty risks. Research into specific pharmaceutical exposures and comparison of results across cohorts will be required to contribute to the deprescribing evidence base.

The role of frailty in the association between depression and fall risk among older adults

Objectives Although there is a recognized association between depression and greater fall risk among older adults, the mechanisms explaining this association are unclear. This study evaluated the role of frailty, a common geriatric syndrome, in determining greater risk of falls among older adults with depression. Method We used longitudinal data from three biennial waves of the Health and Retirement Study (HRS; 2010–2014). The sample included community-dwelling survey respondents age ≥ 65 who participated in objective physiological measures. Major Depression (MD) was measured using Composite International Diagnostic Interview for depression short form. Frailty was measured using criteria outlined in the frailty phenotype model. Causal mediation analysis was used to differentiate the direct effect of depression and indirect effect mediated by frailty on falls, fall injuries, and multiple falls. Results Major depression was associated with significantly greater odds of experiencing a fall (OR: 1.91; 95% CI: 1.31, 2.77), fall injury (OR: 1.86; 95% CI: 1.17, 2.95), and multiple falls (OR: 2.26; 95% CI: 1.52, 3.37) over a two-year period. Frailty was a significant mediator of the effects of depression on falls and multiple falls, accounting for approximately 18.9% and 21.3% of the total effects, respectively. We found no evidence of depression-frailty interaction. Sensitivity analyses showed that results were robust to unmeasured confounding and alternative operationalizations of depression. Conclusion Frailty explains a significant proportion of increased likelihood of falls among older adults with depression. Treatment and management of frailty symptoms may be an important components of fall prevention among older adults with depression.

Oral health-related quality of life is associated with physical frailty: A cross-sectional study of Japanese community-dwelling older adults.

BackgroundPhysical frailty is related to adverse outcomes, and poor oral health has been linked to malnourishment. Subjective measures of oral health‐related quality of life (OHRQoL) have been used as indicators of the oral health problems of older adults, and they have been associated with malnourishment. This study aimed to assess OHRQoL's association with physical frailty.MethodsCross‐sectional study was conducted using data from the Nagasaki Islands Study that enrolled participants aged ≥60 years at Japanese national medical check‐ups from 2014 to 2019. Physical frailty phenotype criteria were determined using the modified Fried frailty phenotype model. OHRQoL was assessed using the Geriatric Oral Health Assessment Index (GOHAI). Dentists conducted clinical dental examinations. Simple correlation and linear regression analyses were performed to investigate the associations of number of physical frailty phenotype criteria with GOHAI and other oral health indicators.ResultsAmong 1341 participants with a mean age of 72 years, GOHAI score was significantly associated with number of physical frailty phenotype criteria (B = −0.01, 95% confidence interval: −0.02 to −0.01, p < 0.001). The association remained significant after adjustment for age, sex, body mass index, history of hypertension, history of diabetes mellitus, smoking status, Kessler‐6 score, and number of remaining teeth.ConclusionsOral health‐related quality of life was associated with physical frailty in Japanese community‐dwelling older adults.

Revisiting the hypothesis of syndromic frailty: a cross-sectional study of the structural validity of the frailty phenotype

BackgroundFried’s Phenotype Model of Frailty (PMF) postulates that frailty is a syndrome. Features of a syndrome are a heterogeneous population that can be split into at least two classes, those presenting and those not presenting the syndrome. Syndromes are characterized by a specific mixture of signs and symptoms which increase in prevalence, from less to more severe classes. So far, the null hypothesis of homogeneity – signs and symptoms of frailty cannot identify at least two classes – has been tested using Latent Class Analysis (LCA) on the five dichotomized components of PMF (unintentional weight loss, exhaustion, weakness, slowness, and low physical activity). The aim of this study is to investigate further the construct validity of frailty as a syndrome using the extension offered by Factor Mixture Models (FMM).MethodsLCA on dichotomized scores and FMM on continuous scores were conducted to test homogeneity on the five PMF components in a sample of 1643 community-dwelling older adults living in Québec, Canada (FRéLE).ResultsWith dichotomized LCA, three frailty classes were found: robust, prefrail and frail, and the hypothesis of homogeneity was rejected. However, in FMM, frailty was better represented as a continuous variable than as latent heterogeneous classes. Thus, the PMF measurement model of frailty did not meet the features of a syndrome in this study.ConclusionUsing the FRéLE cohort, the PMF measurement model validity is questioned. Valid measurement of a syndrome depends on an understanding of its etiological factors and pathophysiological processes, and on a modelling of how the measured components are linked to these processes. Without these features, assessing frailty in a clinical setting may not improve patient health. Research on frailty should address these issues before promoting its use in clinical settings.

Frailty Raises as a New Target in Patients with Chronic Hemodialysis in anyCountry and the Problem we Should Solve

The population undergoing dialysis is aging and increasing around the world. A particularly problematic issue plaguing the elderly population is the frailty. Frailty is usually defined as “a state of increased vulnerability to stressors resulting from a decrease in physiologic reserves in multiple organ systems causing limited capacity to maintain homeostasis” [1]. There are two major approaches to assess frailty: one is frailty phenotype model developed by Fried et al. and the other is accumulated deficit model by Rockwood et al. While the most widely used assessment of frailty is Fried’s phenotype model, regardless of assessment method, frailty remains highly associated with an increased risk of several deleterious outcomes including disability, falls, hospitalization, institutionalization, and death [2].

Linking Persistent Pain and Frailty in Older Adults.

In older adults, the impact of persistent pain goes beyond simple discomfort, often contributing to worsening functional outcomes and ultimately frailty. Frailty is a geriatric syndrome that, like persistent pain, increases in prevalence with age and is characterized by a decreased ability to adapt to common stressors such as acute illness, thereby increasing risk for multiple adverse health outcomes. Evidence supports a relationship between persistent pain and both the incidence and progression of frailty, independent of health, social, and lifestyle confounders. In this article, we synthesize recent evidence linking persistent pain and frailty in an effort to clarify the nature of the relationship between these two commonly occurring geriatric syndromes. We propose an integration of the frailty phenotype model by considering the impact of persistent pain on vulnerability toward external stressors, which can ultimately contribute to frailty in older adults. Incorporating persistent pain into the frailty construct can help us better understand frailty and ultimately improve care for patients with, as well as those at increased risk for, pain and frailty.

Prevalence of frailty in older adults in outpatient physiotherapy in an urban region in the western part of Germany: a cross-sectional study

ObjectiveTo investigate the prevalence of frailty in older people in outpatient physiotherapy services in an urban region in the western part of Germany.DesignCross-sectional study.SettingOutpatient physiotherapy clinics were recruited in the...

Identifying Data Elements to Measure Frailty in a Dutch Nationwide Electronic Medical Record Database for Use in Postmarketing Safety Evaluation: An Exploratory Study.

The role of frailty in postmarketing drug safety is increasingly acknowledged. Few European electronic medical records (EMRs) have been used to explore frailty in observational drug safety research. The aim of this study was to identify data elements, beyond multimorbidity and polypharmacy, that could potentially contribute to measuring frailty among older adults in the Dutch nationwide Integrated Primary Care Information (IPCI) database. Persons aged between 65 and 90years in the IPCI database were identified from 2008 to 2013. Clinical non-disease, non-drug measurements that could potentially contribute to measuring frailty were identified and selected if they were recorded in > 0.005% of patients and could be included in at least one of three definitions of frailty: the frailty phenotype model, the cumulative deficit model, and direct evaluations of frailty through standardized frailty scores. The frequency of these measures was calculated. Overall, 314,191 (17% of the source population) elderly persons were identified. Of these, 7948 (2.53%) had one or more of 12 clinical measurements identified that could potentially contribute to measuring frailty, such as clinical evaluations of cognition, mobility, and cachexia, as well as direct measures of frailty, such as the Groningen Frailty Index. Three of five measurements required for the frailty phenotype were identified in < 0.5% of the population: cachexia, reduced walking speed, and reduced physical activity; weakness and fatigue were not identified. The measurements outlined above may be appropriate for the cumulative deficit definition of frailty, provided that at least 30 deficits, including comorbidities and drug utilization, are evaluated in total. The most commonly recorded item identified that could potentially be used in a cumulative frailty model was the Mini-Mental State Examination score (N= 2850; 0.91%); the only recorded direct measurement of frailty was the Groningen Frailty Index (N = 2382; 0.76%). Non-disease, non-drug clinical data that could potentially contribute to a frailty model was not commonly recorded in the IPCI; less than 3% of a cohort of elderly persons had these data recorded, suggesting that the use of these data in postmarketing drug safety evaluation may be limited.

A Cross-Sectional Study of the Impact of Pain in Older People with Frailty: Findings from the Community Ageing Research 75+ (CARE75+) Study

Background: Pain prevalence is higher in older people with frailty compared to fit older people. However, little is known about pain impact on the lives of older people with frailty. Objectives: To investigate pain impact in community dwelling older people (≥75 years) using data from the Community Ageing Research 75+ (CARE75+) cohort study (UKCRN 18043). Methods: Participants were assessed as not frail, pre-frail or frail (phenotype model of frailty). Pain impact was measured using the Geriatric Pain Measure Short-Form (GPM-12), an instrument incorporating 10 items on how pain impacts on ambulation, social engagement, ability to accomplish tasks and sleep, along with current pain intensity and average pain intensity (last 7 days). Intrusive pain was calculated from an item in the Short-Form 36 questionnaire. Differences in the GPM-12 scores between frailty categories were compared using Kruskal-Wallis H tests. Logistic regression models were used to investigate the association between frailty and intrusive pain. Results: 887 participants: not frail 139; pre-frail 471; and frail 268. Total GPM-12 median (IQR): not-frail 5.0 (0.0, 12.5); pre-frail 10.0 (0.0, 27.5); and frail 40.0 (10.0, 65.0) (p ≤ 0.0001). Current pain: not frail 0.0 (0.0, 1.0); pre-frail 0 (0.0, 3.0); and frail 3.0 (0.0, 5.0) (p ≤ 0.0001). Average pain: not-frail 0.0 (0.0, 2.0); pre-frail 1 (0.0, 4.0); frail 4.0 (2.0, 6.8) (p ≤ 0.0001). There was a strong association between being frail and intrusive pain (adjusted for sex, ethniciaty, mood and high comorbid burden): OR 3.53 (95% CI 2.47, 5.04). Conclusions: This research has identified an important new finding that pain in older people with frailty appears to be of sufficient severity to impact negatively on multiple aspects of day-to-day life

Convergent validity of the electronic frailty index.

the electronic frailty index (eFI) has been developed and validated using routine primary care electronic health record data. The focus of the original big data study was on predictive validity as a form of criterion validation. Convergent validity is a subtype of construct validity and considered a core component of the validity of a test. to investigate convergent validity between the eFI and research standard frailty measures. cross-sectional validation study using data from the Community Ageing Research 75+ (CARE 75+) cohort. multi-site UK community-based cohort study. three hundred fifty-three community-dwelling older people (median age 80 years, IQR 77-84), excluding care home residents and people in the terminal stage of life. Median eFI score of participants was 0.22 (IQR 0.14-0.31). convergent validities between the eFI and: a research standard frailty index (FI); the phenotype model of frailty; Clinical Frailty Scale (CFS) and Edmonton Frail Scale were assessed using scatter plots and Spearman's rank tests to estimate correlation coefficients (Spearman's rho, ρ) and 95% confidence intervals. results indicate strong correlation between the eFI and both the research standard FI (ρ = 0.68, 95% CI 0.62-0.74) and Edmonton Frail Scale (ρ = 0.63, 95% CI 0.57-0.69). There was evidence for moderate correlation between the eFI and both the CFS (ρ = 0.59, 95% CI 0.49-0.65) and phenotype model (ρ = 0.51, 95% CI 0.42-0.59). This study provides evidence for convergent validity of the eFI, a core component of test validity.

Aging-related frailty and sarcopenia. The concepts and diagnostic criteria of frailty.

The historical change in the concept of frailty has been observed from 1980s, and even now there are major two models of frailty, phenotype model and deficit accumulation model. Furthermore besides physical frailty, psychological frailty, which encompasses cognitive and mood components, and social frailty, which includes the lack of participation in social networks and perceived lack of contacts and support, have been proposed, although there is no consensus regarding the definition as well as criteria of these frailty. In super-aged society, Japan, from the viewpoint of healthy life expectancy, the concept of phenotype model of frailty is important.

Validity of maximum isometric tongue pressure as a screening test for physical frailty: Cross-sectional study of Japanese community-dwelling older adults.

Maximum isometric tongue pressure (MIP) seems to have a diagnostic value for oral phase dysphagia. The present study aimed to examine the association between MIP and frailty, and to assess the screening validity of MIP for physical frailty. We carried out a cross-sectional study, and enrolled participants aged ≥60 years from Japanese national medical check-ups in 2015 and 2016. The Fried frailty phenotype model was used. We analyzed odds ratios (OR) and 95% confidence intervals (CI) of physical frailty using one standard deviation increments of tongue pressure. Receiver operating characteristic curves were obtained to predict physical frailty using MIP values. Out of 1603 participants, 968 were categorized as non-frail, 605 as pre-frail and 30 as frail. In logistic regression analysis, one standard deviation increment of MIP significantly differentiated frail and pre-frail: the OR for frail with one standard deviation increment in MIP was 0.37 (95% CI 0.26-0.54, P < 0.001), and the OR for pre-frail was 0.63 (95% CI 0.57-0.70, P < 0.001). The area under the receiver operating characteristic curve for predicting frailty with MIP score was as high as 0.776 (95% CI 0.689-0.862). A point of MIP 35 kPa had a sensitivity of 90.0%, specificity of 40.4%, a positive likelihood ratio of 1.5 and a negative likelihood ratio of 0.2 for predicting frailty. MIP performance is independently associated with frailty. MIP also can be used as a simple screening tool for frailty. Geriatr Gerontol Int 2018; 18: 240-249.

Is Technology Present in Frailty? Technology a Back-up Tool for Dealing with Frailty in the Elderly: A Systematic Review.

This study analyzes the technologies used in dealing with frailty within the following areas: prevention, care, diagnosis and treatment. The aim of this paper is, on the one hand, to analyze the extent to which technology is present in terms of its relationship with frailty and what technological resources are used to treat it. Its other purpose is to define new challenges and contributions made by physiotherapy using technology. Eighty documents related to research, validation and/or the ascertaining of different types of hardware, software or both were reviewed in prominent areas. The authors used the following scales: in the area of diagnosis, Fried’s phenotype model of frailty and a model based on trials for the design of devices. The technologies developed that are based on these models accounted for 55% and 45% of cases respectively. In the area of prevention, the results proved similar regarding the use of wireless sensors with cameras (35.71%), and Kinect™ sensors (28.57%) to analyze movements and postures that indicate a risk of falling. In the area of care, results were found referring to the use of different motion, physiological and environmental wireless sensors (46,15%), i.e. so-called smart homes. In the area of treatment, the results show with a percentage of 37.5% that the Nintendo® Wii™ console is the most used tool for treating frailty in elderly persons. Further work needs to be carried out to reduce the gap existing between technology, frail elderly persons, healthcare professionals and carers to bring together the different views about technology. This need raises the challenge of developing and implementing technology in physiotherapy via serious games that may via play and connectivity help to improve the functional capacity, general health and quality of life of frail individuals.

Multiple somatic symptoms and frailty: cross-sectional study in Japanese community-dwelling elderly people.

Physical frailty is relevant to adverse outcomes, but appropriate procedures for screening populations are lacking. We hypothesized that frailty is associated with multiple somatic symptoms because frail elderly people might have several somatic symptoms attributed to deterioration of multiple organs. To examine the association between multiple somatic symptoms and frailty. We conducted a cross-sectional study and enrolled 1818 participants aged ≥60 years from Japanese national medical check-up in 2015. Frailty status was categorized into frail, pre-frail or non-frail based on the definition of the Fried frailty phenotype model. Sixteen self-reported subjective somatic symptoms were recorded at the timing of medical check-up. Odds ratio (OR) and 95% confidence interval (CI) of frail or pre-frail were analyzed using number of somatic symptoms. Out of total of 1818 subjects, 44 (2.4%) frail subjects, 635 (34.9%) pre-frail subjects and 1139 (62.7%) non-frail subjects were detected. More than two somatic symptoms were significantly associated with the frail phenotype (OR 6.20, 95% CI 2.95, 13.03, P < 0.001) and were associated with the pre-frail phenotype (OR 2.06, 95% CI 1.69, 2.51, P < 0.001). Associations remained significant after multi-adjustment for age, sex, past medical cardiovascular diseases and depressive mood The number of somatic symptoms ≥2 was thought to be the optimal cut-off point to predict frail with a sensitivity of 79.6%, specificity of 61.5%. Our study shows that multiple somatic symptoms are independently associated with frailty. Using more than two multiple somatic symptoms as a prescreening tool for frailty may be appropriate.