Understanding Frailty

Higher baseline resting metabolic rate is associated with 1-year frailty decline among older adults residing in an urban area

BACKGROUND

Dysregulated energy metabolism is one hypothesized mechanism underlying frailty. Resting energy expenditure, as reflected by resting metabolic rate (RMR), makes up the largest component of total energy expenditure. Prior work relating RMR to frailty has largely been done in cross section with mixed results. We investigated whether and how RMR related to 1-year frailty change while adjusting for body composition.

METHODS

N = 116 urban, predominantly African-American older adults were recruited between 2011 and 2019. One-year frailty phenotype (0-5) was regressed on baseline RMR, frailty phenotype, demographics and body composition (DEXA) in an ordinal logistic regression model. Multimorbidity (Charlson comorbidity scale, polypharmacy) and cognitive function (Montreal Cognitive Assessment) were separately added to the model to assess for change to the RMR-frailty relationship. The model was then stratified by baseline frailty status (non-frail, pre-frail) to explore differential RMR effects across frailty.

RESULTS

Higher baseline RMR was associated with worse 1-year frailty (odds ratio = 1.006 for each kcal/day, p = 0.001) independent of baseline frailty, demographics, and body composition. Lower fat-free mass (odds ratio = 0.88 per kg mass, p = 0.008) was independently associated with worse 1-year frailty scores. Neither multimorbidity nor cognitive function altered these relationships. The associations between worse 1-year frailty and higher baseline RMR (odds ratio = 1.009, p < 0.001) and lower baseline fat-free mass (odds ratio = 0.81, p = 0.006) were strongest among those who were pre-frail at baseline.

DISCUSSION

We are among the first to relate RMR to 1-year change in frailty scores. Those with higher baseline RMR and lower fat-free mass had worse 1-year frailty scores, but these relationships were strongest among adults who were pre-frail at baseline. These relationships were not explained by chronic disease or impaired cognition. These results provide new evidence suggesting higher resting energy expenditure is associated with accelerate frailty decline.

Relationship Between Frailty, Sociodemographic and Clinical Characteristics, and Disease Severity of Older Adults With Acute Coronary Syndrome

This study aimed to evaluate the relationship between frailty, sociodemographic and clinical characteristics, and disease severity of older adults with acute coronary syndrome (ACS). A total of 57 hospitalized patients ≥60 years with ACS were assessed for frailty through the Tilburg Frailty Indicator. Disease severity was assessed by the Global Registry of Acute Coronary Events, by the maximum troponin level, and by the number of severely obstructed coronary arteries. The relationship between variables was assessed by Mann Whitney's test, Pearson's chi-square test, likelihood-ratio test, Fisher's exact test, or Student's t test. Analyses were bootstrapped to 1,000 to reduce potential sample bias. About 54.4% were frail. Frailty was associated with ethnicity (p = .02), marital status (p = .05), ischemic equivalents (p = .01), self-perceived health (p = .002), arthritis/rheumatism/arthrosis (p = .002), and number of severely obstructed coronary arteries (p = .05). These relationships can support intensified surveillance planning for the elderly at greatest risk, structuring of transitional care, appropriate nurse-coordinated secondary prevention delivery in primary care, and cardiac rehabilitation following ACS.

Prevalence and factors associated with frailty in older hospitalized patients

OBJECTIVES

This study aimed to determine the prevalence and factors associated with frailty in older hospitalized patients.

METHODS

The point-prevalence study was completed on 263 patients aged 65 and over hospitalized in internal medicine and surgical clinics at a tertiary hospital in Türkiye. Data were collected between July 19th and July 22nd, 2021. A comprehensive geriatric assessment was performed on the participants. The Edmonton Frailty Scale (EFS) and FRAIL scale were used for frailty assessment.

RESULTS

The mean age of the individuals was 72.40 ± 6.42, 51.7% were female, and 63.9% were hospitalized in internal medicine and surgical units. The prevalence of frailty was 57.4% according to the FRAIL scale and 46.8% according to EFS. Factors affecting frailty were gender (OR 3.36, 95% CI 1.48-7.64), comorbidity (OR 1.29, 95% CI 1.01-1.64), polypharmacy (OR 0.33, 95% CI 0.13-0.80), history of falling in the last year (OR 3.54, 95% CI 1.34-9.35), incontinence (OR 5.93, 95% CI 2.47-14.27), and functional dependency (ADL, OR 0.65, 95% CI 0.46-0.92; IADL, OR 0.59, 95% CI 0.46-0.76). This model correctly predicted the participants' frailty at 70.5%.

CONCLUSIONS

The importance of frailty, which affects one out of every two hospitalized older persons, to the health care system should not be overlooked. Considering the increasing trend of the aging person population, national and global plans should be made to prevent and manage frailty.

Nurses’ Opinions on Frailty

Nurses come into frequent contact with frail older people in all healthcare settings. However, few studies have specifically asked nurses about their views on frailty. The main aim of this study was to explore the opinions of nurses working with older people on the concept of frailty, regardless of the care setting. In addition, the associations between the background characteristics of nurses and their opinions about frailty were examined. In 2021, members of professional association of nurses and nursing assistants in the Netherlands (V&VN) received a digital questionnaire asking their opinions on frailty, and 251 individuals completed the questionnaire (response rate of 32.1%). The questionnaire contained seven topics: keywords of frailty, frailty domains, causes of frailty, consequences of frailty, reversing frailty, the prevention of frailty, and addressing frailty. Regarding frailty, nurses especially thought of physical deterioration and dementia. However, other domains of human functioning, such as the social and psychological domains, were often mentioned, pointing to a holistic approach to frailty. It also appears that nurses can identify many causes and consequences of frailty. They see opportunities to reverse frailty and an important role for themselves in this process.

Biomarkers of Frailty: miRNAs as Common Signatures of Impairment in Cognitive and Physical Domains

The past years have seen an increasing concern about frailty, owing to the growing number of elderly people and the major impact of this syndrome on health and social care. The identification of frail people passes through the use of different tests and biomarkers, whose concerted analysis helps to stratify the populations of patients according to their risk profile. However, their efficiency in prognosis and their capability to reflect the multisystemic impairment of frailty is discussed. Recent works propose the use of miRNAs as biological hallmarks of physiological impairment in different organismal districts. Changes in miRNAs expression have been described in biological processes associated with phenotypic outcomes of frailty, opening intriguing possibilities for their use as biomarkers of fragility. Here, with the aim of finding reliable biomarkers of frailty, while considering its complex nature, we revised the current literature on the field, for uncovering miRNAs shared across physical and cognitive frailty domains. By applying in silico analyses, we retrieved the top-ranked shared miRNAs and their targets, finally prioritizing the most significant ones. From this analysis, ten miRNAs emerged which converge into two main biological processes: inflammation and energy homeostasis. Such markers, if validated, may offer promising capabilities for early diagnosis of frailty in the elderly population.

Frailty Assessment for Outcome Prediction of Patients With Prostate Cancer Receiving Radical Prostatectomy: A Meta-Analysis of Cohort Studies

A meta-analysis was conducted to investigate the association between frailty and postoperative complications in patients with prostate cancer following radical prostatectomy. A systematic search of PubMed, Embase, and Web of Science was conducted for relevant cohort studies. A random-effect model was chosen to combine the results. Five cohort studies including 171,929 patients were included. Results showed that patients with frailty had higher risk of severe postoperative complications (Clavien-Dindo IV complications, risk ratio [RR]: 1.87, 95% confidence interval [CI]: 1.67 to 2.10, p < .001; I² = 18%) and all-cause mortality (RR: 2.89, 95% CI: 1.86 to 4.50, p < 0.001; I² = 18%). Subgroup analyses showed consistent results in patients receiving open and robot-assisted radical prostatectomy, and also in studies with univariate and multivariate analyses. In conclusion, preoperative frailty may be a predictor of severe postoperative complications and all-cause mortality of patients with prostate cancer following radical prostatectomy.

Older Adults' Perceptions of Frailty Language: A Scoping Review

There is growing interest in conceptualizing and diagnosing frailty. Less is understood, however, about older adults' perceptions of the term "frail", and the implications of being classified as "frail". The purpose of this scoping review was to map the breadth of primary studies; and describe the meaning, perceptions, and perceived implications of frailty language amongst community-dwelling older adults. Eight studies were included in the review and three core themes were identified: (1) understanding frailty as inevitable age-related decline in multiple domains, (2) perceiving frailty as a generalizing label, and (3) perceiving impacts of language on health and health care utilization. Clinical practice recommendations for health care professionals working with individuals with frailty include: (1) maintaining a holistic view of frailty that extends beyond physical function to include psychosocial and environmental constructs, (2) using person-first language, and (3) using a strengths-based approach to discuss aspects of frailty.

Development and testing of a frailty-focused communication (FCOM) aid for older adults

The concept of frailty as it pertains to aging, health and well-being is poorly understood by older adults and the public-at-large. We developed an aging and frailty education tool designed to improve layperson understanding of frailty and promote behavior change to prevent and/or delay frailty. We subsequently tested the education tool among adults who attended education sessions at 16 community sites. Specific aims were to: 1) determine acceptability (likeability, understandability) of content, and 2) assess the likelihood of behavior change after exposure to education tool content. Results: Over 90% of participants "liked" or "loved" the content and found it understandable. Eighty-five percent of participants indicated that the content triggered a desire to "probably" or "definitely" change behavior. The desire to change was particularly motivated by information about aging, frailty and energy production. Eight focus areas for proactive planning were rated as important or extremely important by over 90% of participants.

Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches

Background

Frailty is one of the most critical age-related conditions in older adults. It is often recognized as a syndrome of physiological decline in late life, characterized by a marked vulnerability to adverse health outcomes. A clear operational definition of frailty, however, has not been agreed so far. There is a wide range of studies on the detection of frailty and their association with mortality. Several of these studies have focused on the possible risk factors associated with frailty in the elderly population while predicting who will be at increased risk of frailty is still overlooked in clinical settings.

Objective

The objective of our study was to develop predictive models for frailty conditions in older people using different machine learning methods based on a database of clinical characteristics and socioeconomic factors.

Methods

An administrative health database containing 1,095,612 elderly people aged 65 or older with 58 input variables and 6 output variables was used. We first identify and define six problems/outputs as surrogates of frailty. We then resolve the imbalanced nature of the data through resampling process and a comparative study between the different machine learning (ML) algorithms – Artificial neural network (ANN), Genetic programming (GP), Support vector machines (SVM), Random Forest (RF), Logistic regression (LR) and Decision tree (DT) – was carried out. The performance of each model was evaluated using a separate unseen dataset.

Results

Predicting mortality outcome has shown higher performance with ANN (TPR 0.81, TNR 0.76, accuracy 0.78, F1-score 0.79) and SVM (TPR 0.77, TNR 0.80, accuracy 0.79, F1-score 0.78) than predicting the other outcomes. On average, over the six problems, the DT classifier has shown the lowest accuracy, while other models (GP, LR, RF, ANN, and SVM) performed better. All models have shown lower accuracy in predicting an event of an emergency admission with red code than predicting fracture and disability. In predicting urgent hospitalization, only SVM achieved better performance (TPR 0.75, TNR 0.77, accuracy 0.73, F1-score 0.76) with the 10-fold cross validation compared with other models in all evaluation metrics.

Conclusions

We developed machine learning models for predicting frailty conditions (mortality, urgent hospitalization, disability, fracture, and emergency admission). The results show that the prediction performance of machine learning models significantly varies from problem to problem in terms of different evaluation metrics. Through further improvement, the model that performs better can be used as a base for developing decision-support tools to improve early identification and prediction of frail older adults.

Frailty syndrome and cognitive impairment in older adults: systematic review of the literature

OBJECTIVE

to synthesize the knowledge about the association of frailty syndrome and cognitive impairment in older adults.

METHOD

the Joanna Briggs Institute's systematic review of etiology and risk factors was adopted. The search for the studies was conducted by two independent reviewers in the databases MEDLINE, Embase, CINAHL and LILACS and by manual search was performed by tow reviewers independently. The measures of association Odds Ratio and Relative Risk were used in the meta-analysis. The software R version 3.4.3 and the meta-analysis package Metafor 2.0 were used for figure analysis.

RESULTS

three studies identified the association of frailty syndrome and cognitive impairment through Odds Ratio values show that frail older adults are 1.4 times more likely to present cognitive impairment than non-frail older adults. Four studies analyzed the association through the measure of Relative Risk and found no statistical significance, and four studies used mean values.

CONCLUSION

despite of the methodological differences of the studies and the lack of definition of an exact proportion in the cause and effect relationship, most studies indicate Frailty Syndrome as a trigger for Cognitive decline.

Biological Processes and Biomarkers Related to Frailty in Older Adults: A State-of-the-Science Literature Review

The objectives of this literature review were to (1) synthesize biological processes linked to frailty and their corresponding biomarkers and (2) identify potential associations among these processes and biomarkers. In September 2016, PubMed, Cumulative Index to Nursing and Allied Health, Cochrane Library, and Embase were searched. Studies examining biological processes related to frailty in older adults (≥60 years) were included. Studies were excluded if they did not employ specific measures of frailty, did not report the association between biomarkers and frailty, or focused on nonelderly samples (average age < 60). Review articles, commentaries, editorials, and non-English articles were also excluded. Fifty-two articles were reviewed, reporting six biological processes related to frailty and multiple associated biomarkers. The processes (biomarkers) include brain changes (neurotrophic factor, gray matter volume), endocrine dysregulation (growth hormones [insulin-like growth factor-1 and binding proteins], hormones related to glucose and insulin, the vitamin D axis, thyroid function, reproductive axis, and hypothalamic–pituitary–adrenal axis), enhanced inflammation (C-reactive protein, interleukin-6), immune dysfunction (neutrophils, monocytes, neopterin, CD8+CD28−T cells, albumin), metabolic imbalance (micronutrients, metabolites, enzyme-activity indices, metabolic end products), and oxidative stress (antioxidants, telomere length, glutathione/oxidized glutathione ratio). Bidirectional interrelationships exist within and between these processes. Biomarkers were associated with frailty in varied strengths, and the causality remains unclear. In conclusion, frailty is related to multisystem physiological changes. Future research should examine the dynamic interactions among these processes to inform causality of frailty. Given the multifactorial nature of frailty, a composite index of multisystem biomarkers would likely be more informative than single biomarkers in early detection of frailty.