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

Ploeg, Tjeerd van der; Gobbens, Robbert; Salem, Benissa E.

doi:10.1016/j.archger.2022.104836

Cited by 7 publications

(4 citation statements)

References 61 publications

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“…We found no publications with a frailty assessment modeled by biochemical data. However, some reported studies describe models that can predict frailty and mortality related to frailty 37,38,39,40 , and most studies used logistic regression as a technique to develop a prediction model. Wang et al 41 used logistic regression modeling and ROC curve analysis to compare performance of two types of frailty index (FI) in classifying individuals who died and who survived.…”

Section: Discussionmentioning

confidence: 99%

Modeling the frailty phenotype by using biochemical markers measured in blood samples and multiple regression analysis

ZAMUDIO,

Garcia,

Palomo

et al. 2024

Preprint

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Background Human frailty syndrome is a complex phenotypic trait and the result of multiple sources of influence. Its assessment is not a simple task; however, it is important to preventing disability in adult people. However, it requires retrieval of clinical data and calculations that are time-consuming, which may be unfeasible in routine clinical practice. We propose a diagnostic tool based on multiple regression analysis and aimed to have a preliminary assessment of the frailty condition in senior adults. Because it depends on blood sample analyzes, our predictive functions are less invasive, expensive, time consuming, and less physically and psychologically discomforting. Method Data came from the Toledo Study for Healthy Aging, a prospective cohort study designed for evaluating the determinants, and consequences, of the frailty syndrome in older people living in the province of Toledo, Spain. The Garcia´s Frailty Trait Scale (FTS12) was considered as the dependent variable in the statistical analyzes. Forty-six traits assessed from blood samples were included as independent variables to be tested. They included 15 variables from hemograms, 19 variables from biochemical profiles, and 12 extra biochemical characteristics (not included in standard biochemical profiles). The stepwise regression method was used in deciding which explanatory variables were to be included in the predictive models. Results Two predictive functions of the observed frailty for each gender were obtained: a “full model” and a “reduced model”. A range of 0.615 to 0.649 of moderately high Pearson's correlations was recorded between observed and predicted FTS12 values across the four equations. The correlation between all functions of risk factors and FTS12 was larger than 0.4 and highly significant in all four study cases. With single risk factors, the highest correlation coefficient was 0.283 (p-value < 0.0001). Conclusions The frailty syndrome can be moderately well predicted by using biochemical information. However, full models showed a narrow superior predictive capability. Albumin was present in all functions of risk factors selected by the modeling. It showed to be a risk factor that helps in predicting the frailty phenotype. We expect that exploring Albumin’s potential as part of a predictive equation might help to design anti-aging therapeutic measures.

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Section: Discussionmentioning

confidence: 99%

Modeling the frailty phenotype by using biochemical markers measured in blood samples and multiple regression analysis

ZAMUDIO,

Garcia,

Palomo

et al. 2024

Preprint

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“…The sample was previously used for frailty studies, e.g. focusing on the psychometric properties of the TFI (Gobbens et al, 2010;Gobbens et al, 2012), the relationship between frailty and quality of life in older people (Gobbens et al, 2010), and the use of Bayesian techniques in predicting frailty (Van Der Ploeg et al, 2023).…”

Section: Study Population and Data Collectionmentioning

confidence: 99%

Disability Transitions in Dutch Community-Dwelling Older People Aged 75 Years or Older

Ploeg

Gobbens

2023

Preprint

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“…3,4 Over the past decade, interest in frailty has substantially grown among geriatric researchers because of its predictive mortality, 5 urgent hospitalization, 6 injurious falls, disability, 7 postoperative complications, 8 caregiver burden, 9 and decreased quality of life. 10 Since frailty is a reversible condition, early identification can help (frail) older people remain independent and self-reliant for as long as possible. 11 However, most of the study related to frailty was conducted in a referred medical center located in an urban area that had different sociodemographic characteristics than a rural population.…”

Section: Introductionmentioning

confidence: 99%

“…Frailty is a dynamic multidimensional geriatric syndrome with increased vulnerability to homeostasis after a stressor event and is a consequence of the cumulative decline in many physiological systems 3,4 . Over the past decade, interest in frailty has substantially grown among geriatric researchers because of its predictive mortality, 5 urgent hospitalization, 6 injurious falls, disability, 7 postoperative complications, 8 caregiver burden, 9 and decreased quality of life 10 . Since frailty is a reversible condition, early identification can help (frail) older people remain independent and self‐reliant for as long as possible 11 .…”

Section: Introductionmentioning

confidence: 99%

Cognitive status and low sun exposure as frailty major risk factor among older people in a rural area of East Java, Indonesia: A cross‐sectional study

Soenarti,

Mahendra,

Rudijanto

et al. 2023

Geriatrics Gerontology Int

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AimTo reveal the prevalence of frailty and factors that strongly affected the frailty condition among older adults in East Java, Indonesia.MethodWe conducted a cross‐sectional study carried out among 400 older adults aged ≥60 years without any acute illness. Data were collected from rural area in two locations in Malang and Pasuruan, East Java, Indonesia, in 2019–2020. For data collection, we used the sociodemographic profile assessment, Fried frailty phenotype, Geriatric Depression Scale, Mini Mental State assessment, sun exposure, handgrip strength, International Physical Activity Questionnaire, walk score, and body mass index. We used logistic regression statistics for data analysis.ResultsThe result showed that 2.5% were robust, 83% were prefrail, and 14.5% were frail. A higher proportion of subjects were aged 60–74 years (83.3%), women (70.3%), with lower educational status (84.5%). Multivariate analysis showed that the intrinsic factors low cognitive status (odds ratio [OR], 3.052 [95% confidence interval (CI), 1.691–5.508]) and older age (OR, 3.073 [95% CI, 1.637–5.767]) were associated with frailty among the older adults in a rural area. Depression was also associated with frailty (OR, 2.458 [95% CI, 0.465–12.985]). From extrinsic factors, we also found that low sun exposure (OR, 2.931 [95% CI, 1.650–5.204]) and unemployment (OR, 1.997 [95% CI, 1.112–3.588]) were associated with frailty.ConclusionFor the Indonesian elderly in this study, low cognitive status, older age, depression, low sun exposure, and unemployment were associated with frailty. Understanding the modifiable risk factors of frailty can provide a valuable reference for future prevention and intervention. Geriatr Gerontol Int 2023; ••: ••–••.

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

Cited by 7 publications

References 61 publications

Modeling the frailty phenotype by using biochemical markers measured in blood samples and multiple regression analysis

Modeling the frailty phenotype by using biochemical markers measured in blood samples and multiple regression analysis

Disability Transitions in Dutch Community-Dwelling Older People Aged 75 Years or Older

Cognitive status and low sun exposure as frailty major risk factor among older people in a rural area of East Java, Indonesia: A cross‐sectional study

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