Frailty and Its Associated Risk Factors: First Phase Analysis of Multicentre Indonesia Longitudinal Aging Study

Setiati, Siti; Soejono, Czeresna Heriawan; Harimurti, Kuntjoro; Dwimartutie, Noto; Aryana, I Gusti Putu Suka; Sunarti, Sri; Budiningsih, Fatichati; Mulyana, Roza; Dwipa, Lazuardhi; Sudarso, Agus; Rensa, Rensa; Istanti, Rahmi; Azwar, Muhammad Khifzhon; Marsigit, Jessica

doi:10.3389/fmed.2021.658580

Cited by 15 publications

(14 citation statements)

References 30 publications

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“…A better grasp of frailty associated factors help prevent or if possible reverse frailty. Evidence is accumulating that various factors including disability, cognitive impairment, living alone, functional dependence, malnutrition, and prior history of falls and hospitalization increased frailty risks [ 3 – 5 ]. The identification of modifiable contributing factors can assist in designing individualized interventions that focused on improving frailty status, and preventing the development of adverse outcomes in early stages [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

The identification and prediction of frailty based on Bayesian network analysis in a community-dwelling older population

et al. 2022

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Background We have witnessed frailty, which characterized by a decline in physiological reserves, become a major public health issue in older adults. Understanding the influential factors associated with frailty may help prevent or if possible reverse frailty. The present study aimed to investigate factors associated with frailty status and frailty transition in a community-dwelling older population. Methods A prospective cohort study on community-dwelling subjects aged ≥ 60 years was conducted, which was registered beforehand (ChiCTR 2,000,032,949). Participants who had completed two visits during 2020–2021 were included. Frailty status was evaluated using the Fried frailty phenotype. The least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection. Bayesian network analysis with the max-min hill-climbing (MMHC) algorithm was used to identify factors related to frailty status and frailty transition. Results Of 1,981 subjects at baseline, 1,040 (52.5%) and 165 (8.33%) were classified as prefrailty and frailty. After one year, improved, stable, and worsening frailty status was observed in 460 (35.6%), 526 (40.7%), and 306 (23.7%) subjects, respectively. Based on the variables screened by LASSO regression, the Bayesian network structure suggested that age, nutritional status, instrumental activities of daily living (IADL), balance capacity, and social support were directly related to frailty status. The probability of developing frailty is 14.4% in an individual aged ≥ 71 years, which increases to 20.2% and 53.2% if the individual has balance impairment alone, or combined with IADL disability and malnutrition. At a longitudinal level, ADL/IADL decline was a direct predictor of worsening in frailty state, which further increased the risk of hospitalization. Low high-density lipoprotein cholesterol (HDL-C) and diastolic blood pressure (DBP) levels were related to malnutrition, and further had impacts on ADL/IADL decline, and ultimately led to the worsening of the frailty state. Knowing the status of any one or more of these factors can be used to infer the risk of frailty based on conditional probabilities. Conclusion Older age, malnutrition, IADL disability, and balance impairment are important factors for identifying frailty. Malnutrition and ADL/IADL decline further predict worsening of the frailty state.

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Section: Introductionmentioning

confidence: 99%

The identification and prediction of frailty based on Bayesian network analysis in a community-dwelling older population

et al. 2022

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“…Falls (OR 1,77, 95% CI 1,16-2,72) and hospital admissions during the period of 12 months (OR 1,46, 95% CI 0,97-2,20) were also associated with frailty. Corelation between frailty and polypharmacy was also identified (OR 2,42, 95% CI 1,50-3,91) [29].…”

Section: Epidemiology Of Frailtymentioning

confidence: 94%

Frailty in elderly patients with acute myocardial infarction

et al. 2022

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In recent years, there has been increasing interest in the frailty as a prognostic factor of acute myocardial infarction in elderly patients.Frailty is an important prognostic marker of frequent complications, readmission to hospital, high hospital mortality and major cardiovascular events in elderly patients with acute myocardial infarction. This category of persons is often not allowed to undergo invasive interventions and are often excluded from the recommended treatment, and they tolerate cardiac surgery worse, recovery from illness is slower, functionality decreases, then disability and death develop.The present review aims to investigate the impact of frailty on management of elderly patients with acute myocardial infarction (AMI).To analyze the literature, we searched for information on this issue in PubMed / MEDLINE, PMC, Web of Science, Scopus, The Сochrane Library. The search depth was 15 years: from 2006 to 2021.One of the important factors in improving clinical outcomes, improving the quality of life in elderly patients with acute myocardial infarction is the early detection of frailty. Frailty assessment is a valuable tool for risk stratification that can be helpful to clinicians in deciding the optimal pathway for management and treatment strategies. Risk prediction is also important for deciding secondary prevention and cardiac rehabilitation measures in the elderly with acute myocardial infarction.

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“…A questionnaire was used to record medical history and number of medications taken. It requested information on the following conditions that have been linked to frailty: heart disease [26], osteoporosis [27], chronic obstructive pulmonary disease [28], knee osteoarthritis [29], anemia [30], depression [31], number of chronic diseases [32], Alzheimer's disease [33], chronic kidney disease [34], rheumatoid arthritis [35], Parkinson's disease [36], and stroke [36].…”

Section: Medical Interviewsmentioning

confidence: 99%

Oral Factors as Predictors of Frailty in Community-Dwelling Older People: A Prospective Cohort Study

Takeuchi

Sawada

Ekuni

et al. 2022

IJERPH

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The purpose of this prospective cohort study was to identify predictors for frailty among possible oral factors in community-dwelling older people. Ninety-seven participants (≥60 years old) without frailty at baseline were included and assigned to either the robust or the frailty group after 2-year follow-up. The frailty was defined using the Japan Cardiovascular Health Study index. The numbers of present and functional teeth and periodontal disease severity were recorded. Bacterial counts on the dorsum of the tongue, oral moisture, tongue pressure, occlusal force, masticatory ability, and the oral diadochokinesis (ODK) rate were measured. Swallowing function, along with psychosocial status, relationships with communities and people, nutritional status, medical history, and comorbidities were evaluated using a questionnaire. The newly identified frailty group at follow-up showed significantly lower values in the number of teeth present, ODK/ta/sound and ODK/ka/sound rates, and clinical attachment level at baseline compared to the robust group. A logistic regression model showed a significantly negative association between the ODK/ta/sound rate at baseline and the incidence of frailty. Articulatory oral motor skill was found to be a predictor of frailty after two years.

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Frailty and Its Associated Risk Factors: First Phase Analysis of Multicentre Indonesia Longitudinal Aging Study

Cited by 15 publications

References 30 publications

The identification and prediction of frailty based on Bayesian network analysis in a community-dwelling older population

The identification and prediction of frailty based on Bayesian network analysis in a community-dwelling older population

Frailty in elderly patients with acute myocardial infarction

Oral Factors as Predictors of Frailty in Community-Dwelling Older People: A Prospective Cohort Study

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