Transição entre níveis de fragilidade em idosos no município de Belo Horizonte, Minas Gerais

Natural history trajectories of frailty in community-dwelling older Japanese adults

Background

The gap between the average life expectancy and healthy life expectancy remains wide. Understanding the natural history of frailty development is necessary to prevent and treat frailty to overcome this gap. This study elucidated the trajectories of 5 frailty assessment components using group-based multitrajectory modeling.

Methods

Overall, 845 community-dwelling older adults (aged 65–91 years; 433 males and 412 females) who underwent longitudinal frailty assessments at least 3 times were included in the analysis. The mean follow-up period (±SD, range) was 7.1 (±2.3, 3.8–11.3) years. In each wave, the physical frailty was assessed for the following 5 partially modified components of the Cardiovascular Health Study criteria: shrinking, weakness, exhaustion, slowness, and low activity. Using group-based multitrajectory modeling, we identified subgroups that followed distinctive trajectories regarding the 5 frailty components.

Results

Five frailty trajectory groups were identified: weakness-focused frail progression group (Group 1 [G1]; 10.9%), robust maintenance group (Group 2 [G2]; 43.7%), exhaustion-focused prefrail group (Group 3 [G3]; 24.3%), frail progression group (Group 4 [G4]; 6.7%), and low activity–focused prefrail group (Group 5 [G5]; 14.4%). The Cox proportional hazards model analysis showed that G1, G4, and G5 had significantly higher mortality risks after adjusting for sex and age (G2 was the reference group).

Conclusion

Based on the natural history of frailty, the 5 distinctive trajectory groups showed that some individuals remained robust, while others remained predominantly prefrail or progressed primarily owing to physical mobility decline. Therefore, identifying individuals belonging to these progressive frailty groups and providing interventions according to the characteristics of each group may be beneficial.

A multidimensional approach to frailty compared with physical phenotype in older Brazilian adults: data from the FIBRA-BR study

BACKGROUND

Frailty is a predictor of negative health outcomes in older adults. The physical frailty phenotype is an often used form for its operationalization. Some authors have pointed out limitations regarding the unidimensionality of the physical phenotype, introducing other dimensions in the approach to frailty. This study aimed to create a multidimensional model to evaluate frailty in older Brazilian adults and to compare the dimensions of the model created among the categories of the physical frailty phenotype.

METHODS

A cross-sectional study was conducted using data from 3569 participants (73.7 ± 6.6 years) from a multicenter and multidisciplinary survey (FIBRA-BR). A three-dimensional model was developed: physical dimension (poor self-rated health, vision impairment, hearing impairment, urinary incontinence, fecal incontinence, and sleeping disorder), social dimension (living alone, not having someone who could help when needed, not visiting others, and not receiving visitors), and psychological dimension (depressive symptoms, concern about falls, feelings of sadness, and memory problems). The five criteria of the phenotype created by Fried and colleagues were used to evaluate the physical frailty phenotype. The proposed multidimensional frailty model was analyzed using factorial analysis. Pearson's chi-square test was used to analyze the associations between each variable of the multidimensional frailty model and the physical phenotype categories. Analysis of variance compared the multidimensional dimensions scores among the three categories of the physical frailty phenotype.

RESULTS

The factorial analysis confirmed a model with three factors, composed of 12 variables, which explained 38.6% of the variability of the model data. The self-rated health variable was transferred to the psychological dimension and living alone variable to the physical dimension. The vision impairment and hearing impairment variables were dropped from the physical dimension. The variables significantly associated with the physical phenotype were self-rated health, urinary incontinence, visiting others, receiving visitors, depressive symptoms, concern about falls, feelings of sadness, and memory problems. A statistically significant difference in mean scores for physical, social, and psychological dimensions among three physical phenotype categories was observed (p < 0.001).

CONCLUSIONS

These results confirm the applicability of our frailty model and suggest the need for a multidimensional approach to providing appropriate and comprehensive care for older adults.

Frailty state among Indonesian elderly: prevalence, associated factors, and frailty state transition

Background

Information about frailty status and its transition is important to inform clinical decisions. Predicting frailty transition is beneficial for its prevention. While Indonesia is the 4th largest geriatric population in Asia, data about frailty transition is limited. This study aimed to obtain data on prevalence of frailty, its risk factors, frailty state transition and its prognostic factors, as well as to develop prognostic score for frailty state transition.

Methods

Multicenter study on subjects aged ≥60 years old was done to obtain the prevalence of frailty status and to identify risk factors of frailty. Prospective cohort over 12 months was done to obtain data on frailty state transition. Multiple logistic regression analysis was performed to identify its prognostic factors from several clinical data, which then were utilized to develop prognostic score for frailty state worsening.

Results

Cross-sectional data from 448 subjects showed that 25.2% of the subjects were frail based on Frailty index-40 items. Risk factors of frailty were age (OR 2.72; 95% CI 1.58–4.76), functional status (OR 2.89; 95% CI 1.79–4.67), and nutritional status (OR 3.75; 95% CI 2.29–6.13). Data from the 162 subjects who completed the cohort showed 27.2% of the cohort had frailty state worsening. Prognostic factors for frailty state worsening were being 70 years or older (OR 3.9; 95% CI 1.2–12.3, p < 0.05), negative QoL, i.e., fair and poor QoL (OR 2.5; 95% CI 1.1–5.9, p < 0.05), and slow gait speed (OR 2.8; 95% CI 1.3–6.4, p < 0.05). The internal validation of the prognostic score consisted of those three variables showed good performance.

Conclusion

The prevalence of frailty in this study among Indonesian elderly in outpatient setting was 25.2%. The risk factors of frailty were age, functional status and nutritional status. The prognostic factors for frailty state worsening were being 70 years old or older, negative QoL (fair or poor quality of life), and slow gait speed. A prognostic score to predict frailty state worsening in 12 months had been developed.