A pilot study to identify factors associated with frailty within the World Trade Center general responder cohort.

BACKGROUND: Given the significant exposures experienced by the World Trade Center (WTC) general responders, there is increasing interest in understanding the effect of these exposures on aging in this population. We aim to identify factors that may be associated with frailty, a clinical syndrome characterized by a decrease in one's reserve that has been linked to poor health outcomes. METHODS: WTC general responders enrolled in the WTC Health Program aged 50 and older provided informed consent. Validated frailty assessments, the Frailty Phenotype (with the Johns Hopkins Frailty Assessment Calculator) along with the FRAIL scale, categorized nonfrail from prefrail/frail. Fall risk, functional status, and cognition were also assessed. WTC variables, including an identified WTC-certified condition, were utilized. The risk of frailty was estimated using log binomial regression analysis. A 95% confidence interval (CI) was used to estimate the prevalence ratio (PR). RESULTS: One hundred and six participants were included; 38 (35.8%) were classified as pre-frail or frail. More of the pre-frail/frail group were obese (57.9% vs. 25%; p = 0.004) and had a WTC-certified condition (78.9% vs. 58.8%; p = 0.036). Obesity (PR = 2.43, 95% CI = 1.31, 4.53), a WTC-certified condition (PR = 1.77, 95% CI = 1.09, 2.89), and risk of falling (PR = 1.97, 95% CI = 1.01, 3.84) were independently associated with frailty. CONCLUSIONS: Obesity and having a WTC-certified condition were found to be risk factors for frailty in our pilot study. Future work may focus on further identifying risk factors for frailty in the larger WTC general responder population.

Comparison of three frailty measures for predicting hospitalization and mortality in the Canadian Longitudinal Study on Aging.

BACKGROUND: Few studies have compared different measures of frailty for predicting adverse outcomes. It remains unknown which frailty measurement approach best predicts healthcare utilization such as hospitalization and mortality. AIMS: This study aims to compare three approaches to measuring frailty-grip strength, frailty phenotype, and frailty index-in predicting hospitalization and mortality among middle-aged and older Canadians. METHODS: We analyzed baseline and the first 3-year follow-up data for 30,097 participants aged 45 to 85 years from the comprehensive cohort of the Canadian Longitudinal Study on Aging (CLSA). Using separate logistic regression models adjusted for multimorbidity, age and biological sex, we predicted participants' risks for overnight hospitalization in the past 12 months and mortality, at the first 3-year follow-up, using each of the three frailty measurements at baseline. Model discrimination was assessed using Harrell's c-statistic and calibration assessed using calibration plots. RESULTS: The predictive performance of all three measures of frailty were roughly similar when predicting overnight hospitalization and mortality risk among CLSA participants. Model discrimination measured using c-statistics ranged from 0.67 to 0.69 for hospitalization and 0.79 to 0.80 for mortality. All measures of frailty yielded strong model calibration. DISCUSSION AND CONCLUSION: All three measures of frailty had similar predictive performance. Discrimination was modest for predicting hospitalization and superior in predicting mortality. This likely reflects the objective nature of mortality as an outcome and the challenges in reducing the complex concept of healthcare utilization to a single variable such as any overnight hospitalization.

Clinical implications of frailty in peritoneal dialysis patients - A prospective observational study.

BACKGROUND: Frailty is an age-related condition that predicts adverse outcomes. The study was aimed to investigate the clinical implications of frailty evolution in patients undergoing peritoneal dialysis (PD). METHOD: In this prospective study, all new-onset (<6 months) and prevalent (≧6 months) PD patients completed frailty assessment at entry and 6 months by a semiautomated frailty index of 80 risk factors (FI80) which also contained the 5 components of Fried frailty phenotype. A score ≧13/80 (FI80 > 0.16) or ≧3/5 (frailty phenotype) was designated to define frailty. RESULT: 337 PD patients were recruited (new-onset 23.4%, prevalent 76.6%). Two hundred (59.3%) and 163 (48.4%) patients were frail by FI80 and frailty phenotype, respectively. Predictors for frailty were old age, dialysis, diabetes mellitus, gout and sleep disorder. New-onset patients aged <55 years displayed the best evolution of frailty over 6 months (stable or improved, n = 29/47, 61.7% by FI80, p = 0.0293), compared with other groups. Survival analysis found that frail patients exhibited the worse outcomes (overall death and hospitalization). Poisson regression showed frailty was associated with increased utilizations of outpatient and ER services; however multivariate Cox models identified only diabetes, gout and low body mass index (<19 kg/m2), but not frailty, predicted overall death and hospitalizations. CONCLUSION: Frailty is a common medical condition in PD patients, and the status of which can be stabilized or improved in new-onset, young patients at least over the short term. Compared with frailty, certain comorbidities (diabetes and gout) and undernutrition appeared to be more robust in the prediction of adverse outcomes.

Were Frailty Identification Criteria Created Equal? A Comparative Case Study on Continuous Non-Invasively Collected Neurocardiovascular Signals during an Active Standing Test in the Irish Longitudinal Study on Ageing (TILDA).

BACKGROUND: In this observational study, we compared continuous physiological signals during an active standing test in adults aged 50 years and over, characterised as frail by three different criteria, using data from The Irish Longitudinal Study on Ageing (TILDA). METHODS: This study utilised data from TILDA, an ongoing landmark prospective cohort study of community-dwelling adults aged 50 years or older in Ireland. The initial sampling strategy in TILDA was based on random geodirectory sampling. Four independent groups were identified: those characterised as frail only by one of the frailty tools used (the physical Frailty Phenotype (FP), the 32-item Frailty Index (FI), or the Clinical Frailty Scale (CFS) classification tree), and a fourth group where participants were not characterised as frail by any of these tools. Continuous non-invasive physiological signals were collected during an active standing test, including systolic (sBP) and diastolic (dBP) blood pressure, as well as heart rate (HR), using digital artery photoplethysmography. Additionally, the frontal lobe cerebral oxygenation (Oxy), deoxygenation (Deoxy), and tissue saturation index (TSI) were also non-invasively measured using near-infrared spectroscopy (NIRS). The signals were visualised across frailty groups and statistically compared using one-dimensional statistical parametric mapping (SPM). RESULTS: A total of 1124 participants (mean age of 63.5 years; 50.2% women) were included: 23 were characterised as frail only by the FP, 97 by the FI, 38 by the CFS, and 966 by none of these criteria. The SPM analyses revealed that only the group characterised as frail by the FI had significantly different signals (p < 0.001) compared to the non-frail group. Specifically, they exhibited an attenuated gain in HR between 10 and 15 s post-stand and larger deficits in sBP and dBP between 15 and 20 s post-stand. CONCLUSIONS: The FI proved to be more adept at capturing distinct physiological responses to standing, likely due to its direct inclusion of cardiovascular morbidities in its definition. Significant differences were observed in the dynamics of cardiovascular signals among the frail populations identified by different frailty criteria, suggesting that caution should be taken when employing frailty identification tools on physiological signals, particularly the neurocardiovascular signals in an active standing test.

Associations of physical frailty with incidence and mortality of overall and site-specific cancers: A prospective cohort study from UK biobank.

OBJECTIVE: Evidence regarding the role of physical frailty in cancer-related outcomes is limited. We aimed to examine the association of frailty with cancer incidence and mortality risk. METHODS: This prospective study included 348,144 participants free of cancer at baseline from the UK Biobank. Frailty phenotypes (non-frail, pre-frail, and frail) were constructed from 5 components: weight loss, exhaustion, low physical activity, slow gait speed, and low grip strength. The outcome was incidence and mortality of overall and cite-specific cancers. Cox proportional hazard regression was used to estimate the association of frailty phenotypes with cancer incidence and mortality risk. RESULTS: A total of 43,304 incident cancer cases and 10,152 cancer deaths were documented during a median of 12.0 years of follow-up. For overall cancer, compared with non-frailty, the incidence risk increased by 4% for pre-frailty and 11% for frailty, and the mortality risk increased by 11% for pre-frailty and 39% for frailty. Frailty phenotypes were also dose-dependently associated with a higher risk of incidence and mortality of some site-specific cancers (including liver and lung), with significant sex differences. We observed a synergetic association of frailty phenotypes and smoking with overall cancer incidence and mortality risk. CONCLUSIONS: Frailty phenotypes contributed significantly to a higher risk of overall and some site-specific cancers incidence and mortality in a stepwise manner or within individual categories. Future studies are warranted to emphasize the identification, management and prevention of frailty in the whole population and complements of lifestyle-targeted interventions such as quitting smoking.

Frailty prevalence and efficient screening in primary care-based memory clinics.

BACKGROUND: Little is known about the prevalence of frailty among patients with memory concerns attending a primary care-based memory clinic. OBJECTIVE: This study aims to describe the prevalence of frailty among patients attending a primary care-based memory clinic and to determine if prevalence rates differ based on the screening tool that is used. METHODS: We conducted a retrospective medical record review for all consecutive patients assessed in a primary care-based memory clinic over 8 months. Frailty was measured in 258 patients using the Fried frailty criteria, which relies on physical measures, and the Clinical Frailty Scale (CFS), which relies on functional status. Weighted kappa statistics were calculated to compare the Fried frailty and the CFS. RESULTS: The prevalence of frailty was 16% by Fried criteria and 48% by the CFS. Agreement between Fried frailty and CFS was fair for CFS 5+ (kappa = 0.22; 95% confidence interval: 0.13, 0.32) and moderate for CFS 6+ (kappa = 0.47; 0.34, 0.61). Dual-trait measures of hand grip strength with gait speed were found to be a valid proxy for Fried frailty phenotype. CONCLUSIONS: Among primary care patients with memory concerns, frailty prevalence rates differed based on the measure used. Screening for frailty in this population using measures relying on physical performance may be a more efficient approach for persons already at risk of further health instability from cognitive impairment. Our findings demonstrate how measure selection should be based on the objectives and context in which frailty screening occurs.

There is some evidence that frailty and dementia are inter-related. This study aimed to describe the prevalence of frailty among patients attending a primary care-based memory clinic using 2 commonly used frailty measures: the Fried frailty phenotype criteria and the Clinical Frailty Scale (CFS). Frailty prevalence in patients with memory concerns is at least double that of regular primary care practice; prevalence is 16% when the Fried frailty phenotype is used, which incorporates physical frailty measures, as compared with prevalence of 48% when the more function-based measure of CFS is used. Screening tools should be selected considering the objectives and context in which they are used. Within primary care-based memory clinics, physical frailty measures may be most optimal. Using hand grip and gait speed screening as a valid proxy for Fried frailty phenotype offers a feasible and practical way of identifying frailty relating more to physical underlying conditions. Based on our study findings, frailty screening within primary care-based memory clinics is justified for patients 65 years+; early identification and intervention may prevent further decline and adverse outcomes. Further research in this area will increase our understanding of frailty and dementia in this context and how to best plan care.

Prediction of adverse health outcomes using an electronic frailty index among nonfrail and prefrail community elders.

BACKGROUND: Early recognition of older people at risk of undesirable clinical outcomes is vital in preventing future disabling conditions. Here, we report the prognostic performance of an electronic frailty index (eFI) in comparison with traditional tools among nonfrail and prefrail community-dwelling older adults. The study is to investigate the predictive utility of a deficit-accumulation eFI in community elders without overt frailty. METHODS: Participants aged 65-80 years with a Clinical Frailty Scale of 1-3 points were recruited and followed for 2 years. The eFI score and Fried's frailty scale were determined by using a semiautomated platform of self-reported questionnaires and objective measurements which yielded cumulative deficits and physical phenotypes from 80 items of risk variables. Kaplan-Meier method and Cox proportional hazards regression were used to analyze the severity of frailty in relation to adverse outcomes of falls, emergency room (ER) visits and hospitalizations during 2 years' follow-up. RESULTS: A total of 427 older adults were evaluated and dichotomized by the median FI score. Two hundred and sixty (60.9%) and 167 (39.1%) elders were stratified into the low- (eFI ≤ 0.075) and the high-risk (eFI > 0.075) groups, respectively. During the follow-up, 77 (47.0%) individuals developed adverse events in the high-risk group, compared with 79 (30.5%) in the low-risk group (x2, p = 0.0006). In multivariable models adjusted for age and sex, the increased risk of all three events combined in the high- vs. low-risk group remained significant (adjusted hazard ratio (aHR) = 3.08, 95% confidence interval (CI): 1.87-5.07). For individual adverse event, the aHRs were 2.20 (CI: 1.44-3.36) for falls; 1.67 (CI: 1.03-2.70) for ER visits; and 2.84 (CI: 1.73-4.67) for hospitalizations. Compared with the traditional tools, the eFI stratification (high- vs. low-risk) showed better predictive performance than either CFS rating (managing well vs. fit to very fit; not discriminative in hospitalizations) or Fried's scale (prefrail to frail vs. nonfrail; not discriminative in ER visits). CONCLUSION: The eFI system is a useful frailty tool which effectively predicts the risk of adverse healthcare outcomes in nonfrail and/or prefrail older adults over a period of 2 years.

Low physical activity is the strongest factor associated with frailty phenotype and frailty index: data from baseline phase of Birjand Longitudinal Aging Study (BLAS).

BACKGROUND: Frailty is the most complicated expression of aging that is related to disability or multi-morbidity. The aim of the present study was to estimate the prevalence of frailty and its associated factors among community-dwelling aged population. METHODS: A total of 1529 eligible community- dwelling older adults (≥ 60 years) were enrolled in the baseline phase of Birjand Longitudinal Aging Study (BLAS) from 2019 to 2020. Their frailty status was assessed using the Fried's frailty phenotype and frailty index. Sociodemographic factors, including sex, age, marital status, and education level, were collected. Health status assessment included the history of hypertension, diabetes mellitus, cardiovascular disease, Alzheimer's diseases and dementia, and other health conditions. Furthermore, functional assessment (ADL, IADL) and anthropometric measurements including height, weight, waist, calf, and mid-arm circumference were made and the body mass index was calculated. The nutrition status and polypharmacy (use 3 or more medication) were also evaluated. RESULTS: The prevalence of frailty was 21.69% according to the frailty phenotype and 23.97% according to the frailty index. A multiple logistic regression model showed a strong association between low physical activity and frailty phenotype (OR = 36.31, CI = 16.99-77.56, P < 0.01), and frailty index (OR = 15.46, CI = 5.65-42.34, P < 0.01). Other factors like old age (≥80), female sex, malnutrition, polypharmacy, obesity, and arthritis were also associated with frailty. The Kappa coefficient of the agreement between these two instruments was 0.18. CONCLUSION: It seems that low physical activity is the most important determinant of frailty. Low physical activity and some other factors may be preventable or modifiable and thus serve as clinically relevant targets for intervention.

Frailty phenotype and associated nutritional factors in a sample of Portuguese outpatients with heart failure.

BACKGROUND AND AIM: Frailty phenotype (FP) is very common in heart failure (HF) and both syndromes worsen one another. The aim of this study is to first describe FP in a sample of Portuguese patients with HF, and to analyse its association with nutritional and clinical statuses, namely, muscle mass, obesity and functional class. METHODS AND RESULTS: In this cross-sectional study, a sample of 136 outpatients with HF (24-81 years, 33.8% women) were randomly selected from the appointments' listings of a HF and Transplant clinic in a Portuguese University Hospital. FP was assessed according to Fried et al. muscle mass was estimated from the mid-upper arm muscle circumference; weight status was assessed using the body mass index; HF functional classes were registered. The association between participants' characteristics and FP categories was analysed using logistic ordinal regression. The frequency of pre-frailty and frailty is 57.4% and 15.4%, respectively. Within frail individuals, 52.4% were under the age of 65. In multivariable analysis, frailty was positively associated with age 70 or older (OR = 3.44) and obesity (OR = 2.66), and negatively associated with muscle mass (OR = 0.77) and HF functional classes I (OR = 0.14) or II (OR = 0.29). CONCLUSION: Muscle mass seems to be an important predictor of frailty in patients with HF and should be taken into account when designing intervention plans that allow for reverting or modifying frailty and pre-frailty. Younger patients should be monitored for the presence and evolution of FP.

Longitudinal changes of frailty in 8 years: comparisons between physical frailty and frailty index.

BACKGROUND: Few studies have made longitudinal comparisons between frailty phenotype (FP) and frailty index (FI) changes. We aimed to investigate frailty status changes defined by FP and FI concurrently, and to compare the associated factors and incident disability among different combination of FI and FP trajectory groups. METHODS: Data on respondents aged over 50 who completed the 1999, 2003 and 2007 Taiwan Longitudinal Study on Aging (TLSA) surveys (n = 2807) were excerpted. Changes of FI, FP and major time-dependent variables were constructed by group-based trajectory modeling. Logistic regression was used to investigate the associated factors and relationships with incident disability among different frailty trajectories. RESULTS: We identified four FP trajectories - stably robust, worsened frailty, improved frailty, and stably frail and three FI trajectories - stable FI, moderate increase FI and rapid increase FI. Lower self-rated health, mobility impairment, and depressed mood were associated with unfavorable FP and FI changes (all p < 0.001). Regardless of FP trajectory groups, the moderate and rapid increase FI group had significantly more comorbidities than the stable FI group, and more visual, hearing, oral intake impairment, more difficulty in meeting living expenses, and poorer cognitive function in ≥65-year-olds (all p < 0.05). In addition, the worsened frailty, improved frailty, and stably frail groups had ORs for incident disability of 10.5, 3.0, and 13.4, respectively, compared with the stably robust group (all p < 0.01); the moderate and rapid increase FI groups had 8.4-fold and 77.5-fold higher risk than the stable FI group (both p < 0.001). When combining FI and FP trajectories, risk increased with FI trajectory steepness, independent of FP change (all p < 0.01 in rapid increase FI vs stable FI). CONCLUSIONS: Four FP trajectories (stably robust, worsened frailty, improved frailty, and stably frail) and three FI trajectories (stable FI, moderate increase FI and rapid increase FI) were identified. Lower self-rated health, mobility impairment, and depressed mood were associated with both unfavorable FP and FI trajectories. Nevertheless, even for individuals in stably robust or improved frailty FP groups, moderate or rapid increase in FI, either due to comorbidities, sensory impairment, cognitive deficits, or financial challenges, may still increase the risk of incident disability.

The association between medication or alcohol use and the incidence of frailty: a retrospective cohort study.

BACKGROUND: Understanding potentially modifiable factors that influence the risk of frailty is a key concern for the management of this urgent contemporary public health challenge. This study evaluates the association between the use of various medications or alcohol and the incidence of frailty among older adults. METHODS: This study was a retrospective cohort study on older adults (≥ 65 years) using data from the longitudinal Survey of Health, Ageing and Retirement in Europe (SHARE survey, 28 countries). Medication use was measured as taking several different groups of medications. Alcohol use was assessed with SHARE questions corresponding to AUDIT-C. The outcome measure was the incidence of frailty after two years, defined by frailty index (FI) and frailty phenotype (FP). A multiple logistic regression model was used to evaluate the association with adjustment for several potential confounding factors. RESULTS: Of the 14,665 FI-population participants, 1800 (12.3%) developed frailty within two years. Of the 8133 FP-population participants, 2798 (34.4%) developed pre-frailty and 247 (3.0%) developed frailty within two years of baseline. After adjustment for potential confounding variables, non-hazardous alcohol use (adjusted OR; 95% CI for the FI-population: 0.68; 0.60-0.77) and hazardous alcohol use (0.80; 0.68-0.93) are associated with lower incidence of frailty compared to no alcohol use. The odds of frailty are increased when taking medications; the largest effect size was observed in older adults taking medication for chronic bronchitis (adjusted OR; 95% CI for the FI-population: 2.45; 1.87-3.22), joint pain and other pain medication (2.26; 2.00-2.54), medication for coronary and other heart disease (1.72; 1.52-1.96), medication for diabetes (1.69; 1.46-1.96), and medication for anxiety, depression and sleep problems (1.56; 1.33-1.84). Additionally, the risk of frailty was increased with stroke, Parkinson's disease and dementia. CONCLUSIONS: Taking certain groups of medication was associated with increased incidence of frailty and pre-frailty, which might be due to either medication use or the underlying disease. Alcohol use was associated with a lower risk of pre-frailty and frailty compared to no alcohol use, which might be due to reverse causality or residual confounding. There was no significant interaction effect between medication groups and alcohol use on frailty incidence.

Digital Biomarkers of Physical Frailty and Frailty Phenotypes Using Sensor-Based Physical Activity and Machine Learning.

Remote monitoring of physical frailty is important to personalize care for slowing down the frailty process and/or for the healthy recovery of older adults following acute or chronic stressors. Taking the Fried frailty criteria as a reference to determine physical frailty and frailty phenotypes (slowness, weakness, exhaustion, inactivity), this study aimed to explore the benefit of machine learning to determine the least number of digital biomarkers of physical frailty measurable from a pendant sensor during activities of daily living. Two hundred and fifty-nine older adults were classified into robust or pre-frail/frail groups based on the physical frailty assessments by the Fried frailty criteria. All participants wore a pendant sensor at the sternum level for 48 h. Of seventeen sensor-derived features extracted from a pendant sensor, fourteen significant features were used for machine learning based on logistic regression modeling and a recursive feature elimination technique incorporating bootstrapping. The combination of percentage time standing, percentage time walking, walking cadence, and longest walking bout were identified as optimal digital biomarkers of physical frailty and frailty phenotypes. These findings suggest that a combination of sensor-measured exhaustion, inactivity, and speed have potential to screen and monitor people for physical frailty and frailty phenotypes.

Digital Biomarker Representing Frailty Phenotypes: The Use of Machine Learning and Sensor-Based Sit-to-Stand Test.

Since conventional screening tools for assessing frailty phenotypes are resource intensive and unsuitable for routine application, efforts are underway to simplify and shorten the frailty screening protocol by using sensor-based technologies. This study explores whether machine learning combined with frailty modeling could determine the least sensor-derived features required to identify physical frailty and three key frailty phenotypes (slowness, weakness, and exhaustion). Older participants (n = 102, age = 76.54 ± 7.72 years) were fitted with five wearable sensors and completed a five times sit-to-stand test. Seventeen sensor-derived features were extracted and used for optimal feature selection based on a machine learning technique combined with frailty modeling. Mean of hip angular velocity range (indicator of slowness), mean of vertical power range (indicator of weakness), and coefficient of variation of vertical power range (indicator of exhaustion) were selected as the optimal features. A frailty model with the three optimal features had an area under the curve of 85.20%, a sensitivity of 82.70%, and a specificity of 71.09%. This study suggests that the three sensor-derived features could be used as digital biomarkers of physical frailty and phenotypes of slowness, weakness, and exhaustion. Our findings could facilitate future design of low-cost sensor-based technologies for remote physical frailty assessments via telemedicine.

Frailty, a Dimension of Impaired Functional Status in Advanced COPD: Utility and Clinical Applicability.

Background and Objectives: In advanced chronic obstructive pulmonary disease (COPD), functional status is significantly impaired mainly as a result of disease related respiratory symptoms such as dyspnea or as a result of fatigue, which is the extra-respiratory symptom the most prevalent in this setting. "Physical" frailty, considered to be an aging phenotype, has defining traits that can also be considered when studying impaired functional status, but little is known about this relationship in advanced COPD. This review discusses the relevance of this type of frailty in advanced COPD and evaluates it utility and its clinical applicability as a potential outcome measure in palliative care for COPD. Materials and Methods: A conceptual review on the functional status as an outcome measure of mortality and morbidity in COPD, and an update on the definition and traits of frailty. Results: Data on the prognostic role of frailty in COPD are rather limited, but individual data on traits of frailty demonstrating their relationship with mortality and morbidity in advanced COPD are available and supportive. Conclusions: Frailty assessment in COPD patients is becoming a relevant issue not only for its potential prognostic value for increased morbidity or for mortality, but also for its potential role as a measure of functional status in palliative care for advanced COPD.

The disability process: is there a place for frailty?

BACKGROUND: frailty and disability are very common in older adults; they share some risk factors and pathophysiological mechanisms. Yet, they are different clinical entities. OBJECTIVES: this study aimed to explore a potential hierarchical relationship between frailty and disability along the continuum of the disablement process. DESIGN: prospective cohort study. SETTING: the French Three-City (3C) study. SUBJECTS: the sample included 943 participants aged 75 and older. METHODS: the Fried frailty phenotype, Instrumental Activities of Daily Living (IADL) and basic Activities of Daily Living (ADL) were used. We distinguished between four mutually excluding groups: (i) robust (no frailty and no disability); (ii) pure frailty (no disability); (iii) frailty with IADL disability (no ADL disability) and (iv) frailty with IADL and ADL disabilities. We used Cox's regression models to study the 4-year mortality risk associated with each status. RESULTS: Eight-two per cent of participants were classified according to the assumed hierarchy: 61.3% was robust, 5.4% frail, 10.5% frail and IADL-disabled and 4.8% frail, IADL and ADL-disabled. An extra group of 17% was identified with IADL-disabled individuals without frailty. This extra group was similar to pure frailty in terms of characteristics and risk of death, placing them along the continuum at an intermediate stage between robustness and the two most disabled sub-groups. CONCLUSIONS: our findings suggest that including frailty along the continuum could be relevant to describe the whole disablement process. Frailty would occur upstream of the process and might be relevant to identify an opportune time window, where specific monitoring and clinical interventions could be implemented in order to interrupt the process at a potentially more reversible stage.

Frailty as a predictor of future falls and disability: a four-year follow-up study of Chinese older adults.

BACKGROUND: Frailty, which is defined as aging-related multisystem impairments, can lead to adverse health outcomes. However, evidence for such a connection in Chinese older adults remains lacking. This study examined the association between frailty and future falls and disability among community-dwelling Chinese older adults. METHODS: Data were obtained from the 2011 and 2015 waves of the China Health and Retirement Longitudinal Study. Participants were aged 60 years and above at baseline in 2011 and completed the follow-up survey in 2015. Outcome measures were future falls, incident disability in activities of daily living (ADLs) and instrumental activities of daily living (IADLs), and worsening performance of ADLs and IADLs. A multivariate logistic regression was conducted to examine the association between frailty phenotype and falls, incident disability, and worsening disability during a four-year period. RESULTS: We found that frail participants were at increased risk at follow-up for: falls (OR 1.54, 95% CI, 1.14-2.08); developing new ADL difficulties (OR 4.10, 95% CI, 2.79-6.03) and IADL difficulties (OR 3.06, 95% CI, 2.03-4.61); and worsening ADLs performance (OR 2.27, 95% CI, 1.27-4.06), after adjusting for potential confounders. Prefrailty was also significantly associated with future falls, incident disability in ADLs and IADLs, but with a lower magnitude of effect. CONCLUSIONS: Frailty phenotype is an independent predictor of future falls, incident disability, and worsening performance in ADLs among Chinese older adults. The association suggests the need to pay special attention in caring for frail and prefrail elders and improving individuals' frailty status.

Frailty phenotype and multi-domain impairments in older patients with chronic kidney disease.

BACKGROUND: Older subjects with chronic kidney disease (CKD) are often affected by multiple geriatric impairments that may benefit from a comprehensive geriatric assessment (CGA). However, ordinary execution of CGA in all these individuals would be unaffordable. We evaluated if Frailty Phenotype (FP) could identify older CKD-patients that may benefit the most from a CGA. METHODS: We evaluated 112 CKD patients not yet on dialysis (age ≥ 65 years, eGFR < 45 ml/min). FP was defined according to the criteria proposed by Fried and co-authors. CGA evaluated four domains (nutrition, physical performance, cognition and depression). Malnutrition was defined in accordance to Malnutrition-Inflammation Score (MIS) and/or by the presence of Protein Energy Wasting syndrome (PEW). Physical performance was evaluated using Short Physical Performance Battery (SPPB) and handgrip strength. Cognitive status was assessed by using Mini Mental State Examination (MMSE) and Clock Drawing Test. Mood was investigated with Geriatric Depression Scale (GDS). RESULTS: Average age of our cohort was 80 ± 6 years and mean eGFR 24 ± 11 ml/min/1.73 m2. Prevalence of frailty was 45%. Frail patients (F-CKD) had higher prevalence of malnutrition (58 vs 29%, p = 0.0005), physical impairment (100% vs 78%; p < 0.0001), cognitive dysfunction (83% vs 37%; p < 0.0001) and depression (50% vs 21%; p < 0.001) compared to robust ones (NF-CKD). Moreover, F-CKD patients had higher probability to have > 2 impaired domains (83% sensitivity and 76% specificity) respect to NF-CKD individuals. CONCLUSIONS: FP is a reliable screening tool to identify older CKD-patients that may benefit from a CGA.

Accuracy of diagnosis and health service codes in identifying frailty in Medicare data.

BACKGROUND: Capturing frailty within administrative claims data may help to identify high-risk patients and inform population health management strategies. Although it is common to ascertain frailty status utilizing claims-based surrogates (e.g. diagnosis and health service codes) selected according to clinical knowledge, the accuracy of this approach has not yet been examined. We evaluated the accuracy of claims-based surrogates against two clinical definitions of frailty. METHODS: This cross-sectional study was conducted in a Health and Retirement Study subsample of 3097 participants, aged 65 years or older and with at least 12-months of continuous fee-for-service Medicare enrollment. We defined 18 previously utilized claims-based surrogates of frailty from Medicare data and evaluated each against clinical reference standards, ascertained from a direct examination: a deficit accumulation frailty index (FI) (range: 0-1) and frailty phenotype. We also compared the accuracy of the total count of 18 claims-based surrogates with that of a validated claims-based FI model, comprised of 93 claims-based variables. RESULTS: 19% of participants met clinical criteria for the clinical frailty phenotype. The mean clinical FI for our sample was 0.20 (standard deviation 0.13). Hospital Beds and associated supplies was the claims-based surrogate associated with the highest clinical FI (mean FI 0.49). Claims-based surrogates had low sensitivity ranging from 0.01 (cachexia, adult failure to thrive, anorexia) to 0.38 (malaise and fatigue) and high specificity ranging from 0.79 (malaise and fatigue) to 0.99 (cachexia, adult failure to thrive, anorexia) in discriminating the clinical frailty phenotype. Compared with a validated claims-based FI, the total count of claims-based surrogates demonstrated lower Spearman correlation with the clinical FI (0.41 [95% CI 0.38-0.44] versus 0.59 [95% CI, 0.56-0.61]) and poorer discrimination of the frailty phenotype (C-statistics 0.68 [95% CI, 0.66-0.70] versus 0.75 [95% CI, 0.73-0.77]). CONCLUSIONS: Claims-based surrogates, selected according to clinical knowledge, do not accurately capture frailty in Medicare claims data. A simple count of claims-based surrogates improves accuracy but remains inferior to a claims-based FI model.

The joint effects of frailty and telomere length for predicting mortality in older adults: the National Health and Nutrition Examination Survey 1999-2002.

BACKGROUND: Frailty and short telomere length, which address different aspects of biological aging, are separately associated with mortality in older adults. AIMS: To evaluate whether the combination of these two biomarkers would be a better predictor of mortality than either alone. METHODS: This present study included participants 60 years of age or older from the National Health and Nutrition Examination Survey in the 1999-2002 phase. The frailty phenotype was identified based on the Fried definition. Telomere length relative to standard reference DNA (T/S ratio) was assessed using quantitative polymerase chain reaction (PCR). Cox proportional hazards regression models were used to estimate the individual and combined effects of frailty phenotype and telomere length on all-cause and cardiovascular mortality. RESULTS: Compared with participants with neither impairment, the mortality risks increased slightly among participants with short telomere length only (hazard ratio [HR] 1.19, 95% confidence interval [CI]: 1.00-1.42) or pre-frailty only (HR 2.16, 95% CI 1.80-2.60) and gradually elevated approximately 3 folds with both short telomere length and pre-frailty (HR 2.23, 95% CI 1.81-2.74) or frailty (HR 3.57, 95% CI 2.56-4.98). Moreover, participants with both short telomere length and frailty had the highest increased all-cause mortality (HR 5.16, 95% CI 3.38-7.85) and cardiovascular mortality (HR 4.67, 95% CI 2.02-10.82). DISCUSSION AND CONCLUSIONS: The combined predictor had more capability of predicting mortality, which suggested that integrating both molecular biomarkers and physiological functional parameters would be a more informative measure of biological aging.

Investigation of a possible association of potentially inappropriate medication for older adults and frailty in a prospective cohort study from Germany.

OBJECTIVE: potentially inappropriate medications (PIMs) are commonly defined as drugs that should be avoided in older adults because they are considered to have a negative risk-benefit ratio. PIMs are suspected to increase the risk for frailty, but this has yet to be examined. DESIGN: prospective population-based cohort study. SETTING AND PARTICIPANTS: a German cohort of community-dwelling older adults (≥60 years) was followed from October 2008 to September 2016. METHODS: in propensity score-adjusted logistic and Cox regression models, associations between baseline PIM use and prevalent/incident frailty were investigated. Frailty was assessed using the definition by Fried and co-workers, PIM were defined with the 2015 BEERS criteria, the BEERS criteria to avoid in cognitively impaired patients (BEERS dementia PIM), the EU(7)-PIM and the PRISCUS list. RESULTS: of 2,865 participants, 261 were frail at baseline and 423 became frail during follow-up. Only BEERS dementia PIM use was statistically significantly associated with prevalent frailty (odds ratio (95% confidence interval), 1.51 (1.04-2.17)). The strength of the association was comparable for all frailty components. Similarly, in longitudinal analyses, only BEERS dementia PIM use was associated with incident frailty albeit not statistically significant (hazard ratio, 1.19 (0.84-1.68)). CONCLUSIONS: the association of PIM use and frailty seems to be restricted to drug classes, which can induce frailty symptoms (anticholinergics, benzodiazepines, z-substances and antipsychotics). Physicians are advised to perform frailty assessments before and after prescribing these drug classes to older patients and to reconsider treatment decisions in case of negative performance changes.