Comparison of Two Models of Frailty for the Prediction of Mortality in Brazilian Community-Dwelling Older Adults: The FIBRA Study

Pereira, Alexandre Alves; Borim, Flavia S. A.; Aprahamian, Ivan; Néri, Anita Liberalesso

doi:10.1007/s12603-019-1264-0

Cited by 9 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Frailty is common in older adults, often accompanied by heterogeneous decline of physiologic capacity [ 2 ]. Many assessment tools were used to define frailty in screening, the frailty phenotype and the deficit accumulation approach were recognized as two operational approaches [ 3 ]. The Fried frailty phenotype consists of five physical components: unintentional weight loss, self-reported exhaustion, weakness, slow walking speed, and low physical activity, the presence of three or more of above was identified as frailer [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective

Jia

Xiang

et al. 2022

BMC Geriatr

View full text Add to dashboard Cite

Background This study aimed to identify long-term frailty trajectories among older adults (≥65) and construct interpretable prediction models to assess the risk of developing abnormal frailty trajectory among older adults and examine significant factors related to the progression of frailty. Methods This study retrospectively collected data from the Chinese Longitudinal Healthy Longevity and Happy Family Study between 2002 and 2018 (N = 4083). Frailty was defined by the frailty index. The whole study consisted of two phases of tasks. First, group-based trajectory modeling was used to identify frailty trajectories. Second, easy-to-access epidemiological data was utilized to construct machine learning algorithms including naïve bayes, logistic regression, decision tree, support vector machine, random forest, artificial neural network, and extreme gradient boosting to predict the risk of long-term frailty trajectories. Further, Shapley additive explanations was employed to identify feature importance and open-up the black box model of machine learning to further strengthen decision makers’ trust in the model. Results Two distinct frailty trajectories (stable-growth: 82.54%, rapid-growth: 17.46%) were identified. Compared with other algorithms, random forest performed relatively better in distinguishing the stable-growth and rapid-growth groups. Physical function including activities of daily living and instrumental activities of daily living, marital status, weight, and cognitive function were top five predictors. Conclusions Interpretable machine learning can achieve the primary goal of risk stratification and make it more transparent in individual prediction beneficial to primary screening and tailored prevention.

show abstract

Section: Introductionmentioning

confidence: 99%

Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective

Jia

Xiang

et al. 2022

BMC Geriatr

View full text Add to dashboard Cite

show abstract

“…For example, a conceptual development that involved scholarly collaboration from various disciplines could alleviate some of the aforementioned shortcomings. Notably, a hypothetical future model of frailty among elderly individuals should not be as restrictive as the physical phenotype or as broad as the index derived from multiple domains [10].…”

Section: B Aging Related Diseasesmentioning

confidence: 99%

Logistic Regression Modeling to Predict Sarcopenia Frailty among Aging Adults

Kaur¹,

Abdullah²,

Hairi³

et al. 2021

IJACSA

View full text Add to dashboard Cite

Sarcopenia and frailty have been associated with low aging population capacities for exercise and high metabolic instability. To date, the current models merely support one classification with an accuracy of 83%. The models also reflect overfitting dataset complexities in predicting the accuracy and detecting the misclassifications of rare diseases. As multiple classifications led to incongruent data analyses and methods, each evaluation yielded inaccurate results regarding high prediction accuracy. This study intends to contribute to the current medical informatics literature by comparing the most optimal model to identify relevant patterns and parameters for prediction model development. The methods were duly assessed on a real dataset together with the classification model. Meanwhile, the obesity physical frailty (OPF) model was presented as a conceptual study model. A matrix of accuracy, classification, and feature selection was also utilized to compare the computer output and deep learning models against current counterparts. Essentially, the study findings predicted that an individuals' risk of sarcopenia corresponded to physical frailty. Each model was compared with an accuracy matrix to determine the best-fitting model. Resultantly, logistic regression produced the highest results with an accuracy rate of 97.69% compared to the other four study models.

show abstract

“…A cohort study conducted on older people in Latin American countries, China, and India showed that the physical frailty phenotype is associated with functional dependence (RR=1.43, 95%CI 1.24-1.64) and mortality (RR=1.51, 95%CI 1.36-1.68) 19 . In Brazil, two longitudinal studies investigated the association of frailty with mortality using data from the Frailty in Brazilian Older People (FI-BRA) study conducted in Campinas, São Paulo, Brazil 20,21 . Pereira et al 20 investigated the association between frailty assessed by the frailty index and death in 674 older people (≥65 years; 68.7% women) and found that the frailty index was not associated with mortality over a five-year period in the studied sample.…”

Section: Introductionmentioning

confidence: 99%

“…In Brazil, two longitudinal studies investigated the association of frailty with mortality using data from the Frailty in Brazilian Older People (FI-BRA) study conducted in Campinas, São Paulo, Brazil 20,21 . Pereira et al 20 investigated the association between frailty assessed by the frailty index and death in 674 older people (≥65 years; 68.7% women) and found that the frailty index was not associated with mortality over a five-year period in the studied sample. Pereira et al 21 compared the accuracy of the frailty index and that of the physical frailty phenotype in predicting mortality and demonstrated that the phenotype was more accurate in predicting mortality in the older people evaluated.…”

Section: Introductionmentioning

confidence: 99%

Physical frailty, activity limitation and mortality in older Brazilians: longitudinal findings from FIBRA-BH study (2009-2019)

Venturini

Moreira

Silva

et al. 2022

Ciênc. saúde coletiva

View full text Add to dashboard Cite

The aim was to investigate the longitudinal association between physical frailty and change in the profile of limitation to perform basic and instrumental activities of daily living (BADL and IADL) and mortality in a 10-year period in Brazilian community-dwelling older people. A longitudinal study was conducted with data from the Frailty in Brazilian Older People (FIBRA) study, 2009-2019. Physical frailty was categorized into vulnerability (pre-frail and frail) and robustness (non-frail). The generalized estimating equation and the Cox proportional hazards models were used in the data analysis. Out of 200 older people evaluated in 2009 (moment 1), 139 were located in 2019 (moment 2). Of these, 102 were interviewed and 37 deaths were recorded. The chance of vulnerable older people at moment 1 being dependent on performing BADL at moment 2 was 4.19-fold the chance of robust older people. For IADL, the chance of vulnerable older people at moment 1 being dependent at moment 2 was 3.12-fold the chance of robust older people. Cox’s analysis showed that the risk of death among vulnerable older people was 2.50-fold that among robust older people. The results reinforce the importance of monitoring and early intervention to prevent frailty, and the limitation to performing activities of daily living and death among Brazilian older people.

show abstract

Comparison of Two Models of Frailty for the Prediction of Mortality in Brazilian Community-Dwelling Older Adults: The FIBRA Study

Cited by 9 publications

References 27 publications

Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective

Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective

Logistic Regression Modeling to Predict Sarcopenia Frailty among Aging Adults

Physical frailty, activity limitation and mortality in older Brazilians: longitudinal findings from FIBRA-BH study (2009-2019)

Contact Info

Product

Resources

About