The Development of Multidimensional Frailty Over Seven Years A longitudinal study among Dutch community-dwelling older people using the Tilburg Frailty Indicator

Disease activity at baseline is an independent predictor of frailty at one year in pre-frail patients with rheumatoid arthritis; a multicenter retrospective observational study

OBJECTIVES

To investigate factors predicting frailty for one year in pre-frail patients with rheumatoid arthritis (RA).

METHOD

A total of 298 RA patients who were pre-frail in 2020 were evaluated in this structured, retrospective observational study. Of the 298 patients, 42 who were frail and 256 who were not in 2021 were assigned to the frailty and non-frailty groups, respectively. After comparing characteristics of both groups using univariate analysis, predictive factors of frailty were assessed by logistic regression analysis. The proportion of frail patients in 2021 by DAS28-ESR level in 2020 was examined by the Cochran-Armitage trend test and chi-squared test. After dividing pre-frail patients into those with DAS28-ESR ≥3.2 and DAS28-ESR <3.2 in 2020, one-year change in DAS28-ESR in the frailty and non-frailty groups for both subgroups were compared by the paired t-test.

RESULTS

The frailty group was older (mean: 71.0 vs. 65.4 years) and had a higher DAS28-ESR (mean: 3.22 vs. 2.70) than the non-frailty group. DAS28-ESR was identified as a predictive factor for frailty (OR: 1.49). Among patients with DAS28-ESR ≥3.2 in 2020, DAS28-ESR improved in the non-frailty group in 2021 (mean: 3.97 in 2020 vs. 3.13 in 2021) but did not in the frailty group (3.97 in 2020 vs. 3.81 in 2021). Among those with DAS28-ESR <3.2 in 2020, DAS28-ESR was unchanged in the non-frailty group in 2021 (2.15 in 2020 vs. 2.23 in 2021) but increased in the frailty group (2.53 in 2020 vs. 3.23 in 2021).

CONCLUSIONS

Disease activity at baseline is an independent predictor of frailty one year later in pre-frail patients with RA.

Translation and Validation of the Swedish Version of the Tilburg Frailty Indicator

The Tilburg Frailty Indicator (TFI) is a questionnaire with 15 questions designed for screening for frailty in community-dwelling older people. TFI has a multidimensional approach to frailty, including physical, psychological, and social dimensions. The aim of this study was to translate TFI into Swedish and study its psychometric properties in community-dwelling older people with multimorbidity. A cross-sectional study of individuals 75 years and older, with ≥3 diagnoses of the ICD-10 and ≥3 visits to the Emergency Department in the past 18 months. International guidelines for back-translation were followed. Psychometric properties of the TFI were examined by determining the reliability (inter-item correlations, internal consistency, test-retest) and validity (concurrent, construct, structural). A total of 315 participants (57.8% women) were included, and the mean age was 83.3 years. The reliability coefficient KR-20 was 0.69 for the total sum. A total of 39 individuals were re-tested, and the weighted kappa was 0.7. TFI correlated moderately with other frailty measures. The individual items correlated with alternative measures mostly as expected. In the confirmatory factor analysis (CFA), a three-factor model fitted the data better than a one-factor model. We found evidence for adequate reliability and validity of the Swedish TFI and potential for improvements.

Exploring the concept of psychological frailty in older adults: a systematic scoping review

OBJECTIVES

We reviewed the existing definitions of psychological frailty and provided a comprehensive overview of the concept and associated measurements.

STUDY DESIGN AND SETTING

We followed the PRISMA guidelines for scoping reviews and the Joanna Briggs Institute Manual for Evidence Synthesis. The eligibility criteria for including studies were developed based on the participants-concept-context framework. We searched the Cumulative Index to Nursing and Allied Health Literature, Scopus, PubMed, Web of Science and PsycINFO databases, and other sources for relevant studies published between January 2003 and March 2022.

RESULTS

The final scoping review included 58 studies. Of these, 40 defined psychological frailty, seven provided a novel definition, and 11 focused on the components defining psychological frailty. We proposed four groups of components to better characterize psychological frailty: mood, cognitive, other mental health, and fatigue-related problems. We identified 28 measuring tools across studies, and the Tilburg Frailty Indicator was the most frequently used (46.6%).

CONCLUSION

Psychological frailty is a complex concept whose definition seems to lack consensus. It could include both psychological and physical features. Depression and anxiety are commonly used to define it. This scoping review outlined future research directions for refining the concept of psychological frailty.

Bayesian Techniques in Predicting Frailty among Community-Dwelling Older Adults in the Netherlands

Background Frailty is a syndrome that is defined as an accumulation of deficits in physical, psychological, and social domains. On a global scale, there is an urgent need to create frailty-ready healthcare systems due to the healthcare burden that frailty confers on systems and the increased risk of falls, healthcare utilization, disability, and premature mortality. Several studies have been conducted to develop prediction models for predicting frailty. Most studies used logistic regression as a technique to develop a prediction model. One area that has experienced significant growth is the application of Bayesian techniques, partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. Objective We compared ten different Bayesian networks as proposed by ten experts in the field of frail elderly people to predict frailty with a choice from ten dichotomized determinants for frailty. Methods We used the opinion of ten experts who could indicate, using an empty Bayesian network graph, the important predictors for frailty and the interactions between the different predictors. The candidate predictors were age, sex, marital status, ethnicity, education, income, lifestyle, multimorbidity, life events, and home living environment. The ten Bayesian network models were evaluated in terms of their ability to predict frailty. For the evaluation, we used the data of 479 participants that filled in the Tilburg Frailty indicator (TFI) questionnaire for assessing frailty among community-dwelling older people. The data set contained the aforementioned variables and the outcome "frail". The model fit of each model was measured using the Akaike information criterion (AIC) and the predictive performance of the models was measured using the area under the curve (AUC) of the receiver operator characteristic (ROC). The AUCs of the models were validated using bootstrapping with 100 repetitions. The relative importance of the predictors in the models was calculated using the permutation feature importance algorithm (PFI). Results The ten Bayesian networks of the ten experts differed considerably regarding the predictors and the connections between the predictors and the outcome. However, all ten networks had corrected AUCs >0.700. Evaluating the importance of the predictors in each model, "diseases or chronic disorders" was the most important predictor in all models (10 times). The predictors "lifestyle" and "monthly income" were also often present in the models (both 6 times). One or more diseases or chronic disorders, an unhealthy lifestyle, and a monthly income below 1800 euro increased the likelihood of frailty. Conclusions Although the ten experts all made different graphs, the predictive performance was always satisfying (AUCs >0.700). While it is true that the predictor importance varied all the time, the top three of the predictor importance consisted of "diseases or chronic disorders", "lifestyle" and "monthly income". All in all, asking for the opinion of experts in the field of frail elderly to predict frailty with Bayesian networks may be more rewarding than a data-driven forecast with Bayesian networks because they have expert knowledge regarding interactions between the different predictors.

Progression of frailty as measured by a cumulative deficit index: A systematic review

BACKGROUND

Frailty is a risk factor for adverse health outcomes. There is a paucity of literature on frailty progression defined by a cumulative deficit model among community dwelling older people. The objective of this review was to synthesise evidence on these changes in health and mortality among community-dwelling older people.

METHODS

Six databases (Medline, Embase, CINAHL, Cochrane, PsycInfo, Web of Science) and a clinical trials registry were searched in July 2021. The inclusion criteria were studies using a frailty index and providing information on transition between frailty states or to death in community-dwelling older people aged ≥ 50. Exclusion criteria were studies examining specific health conditions, conference abstracts and non-English studies. To standardise the follow-up period and facilitate comparison, we converted the transition probabilities to annual transition rates.

RESULTS

Two reviewers independently screened 5078 studies and 61 studies were included for analysis. Of these, only three used the same frailty state cut-points to facilitate cross-cohort comparison. This review found that frailty tends to increase with time, people who are frail at baseline have greater likelihood to progress in frailty and die, and the main factor that accelerates frailty progression is age. Other risk factors for progression are having chronic disease, smoking, obesity, low-income or/and low-education levels. A frailty index is an accurate predictor of adverse outcomes and death.

DISCUSSION

This systematic review demonstrated that worsening in frailty was a common frailty transition, and older people who are frail at baseline are more likely to die. A frailty index has significant power to predict adverse health outcomes. It is a useful tool for within-cohort comparison but there are challenges comparing different cohorts due to dependence of frailty progression on age and differences in how frailty index is defined and measured.

Oral Health and Frailty in Community-Dwelling Older Adults in the Northern Netherlands: A Cross-Sectional Study

The aim of this study was to explore the association between oral health and frailty in community-dwelling Dutch adults aged 55 years and older. Included were 170 participants (n = 95 female [56%]; median age 64 years [IQR: 59−69 years]). Frailty was assessed by the Groningen Frailty Indicator. Oral health was assessed by the Oral Health Impact Profile-14-NL (OHIP-NL14). OHIP-NL14 item scores were analyzed for differences between frail and non-frail participants. Univariate and multivariate logistic regression analyses were performed to assess the association between oral health and presence of frailty. The multivariate analysis included age, gender, and depressive symptoms as co-variables. After adjustment, 1 point increase on the OHIP-NL14 scale was associated with 21% higher odds of being frail (p = 0.000). In addition, significantly more frail participants reported presence of problems on each OHIP-NL14 item, compared to non-frail participants (p < 0.003). Contrast in prevalence of different oral health problems between frail and non-frail was most prominent in ‘younger’ older adults aged 55−64 years. In conclusion: decreased oral health was associated with frailty in older adults aged ≥55 years. Since oral health problems are not included in most frailty assessments, tackling oral health problems may not be sufficiently emphasized in frailty policies.

FRAGILIDADE EM PESSOAS IDOSAS RESIDENTES NO DOMICÍLIO INSCRITAS NUMA UNIDADE DE SAÚDE DO NORTE DE PORTUGAL

Introdução: Com o aumento da longevidade e declínio da função física, psicológica e social dos idosos é essencial perceber as condições sociodemográficas e de saúde que concorrem para a fragilidade. Objetivos: Analisar o perfil de fragilidade dos idosos de uma unidade de saúde do norte de Portugal. Metodologia: Estudo descritivo, transversal com 173 idosos a residir no domicílio e inscritos numa Unidade de Saúde. Como instrumento de recolha de dados usou-se um inquérito, realizado por telefone, contendo dados sociodemográficos, de saúde e o Índice de fragilidade de Tilburg (TFI). Resultados: Amostra predominantemente feminina com idade média de 81,11 anos, maioritariamente casados, com diversas comorbilidades e polimedicados. A representação da fragilidade foi de 60,7%, estando essa condição significativamente associada ao género, estado civil, número de doenças crónicas, polimedicação e autoperceção da saúde. Para a maioria dos idosos (83,8%), a condição de fragilidade é influenciada cumulativamente pelas dimensões físicas, psicológicas e sociais. Conclusão: A fragilidade é uma condição prevalente e o perfil está associado a um conjunto de caraterísticas nas quais é possível intervir retardando a progressão da fragilidade que ocorre com o envelhecimento.