Psychometric Properties of the Taiwanese Version of the Tilburg Frailty Indicator for Community-Dwelling Older Adults

Screening the frailty level of older adults is essential to avoid morbidity, prevent falls and disability, and maintain quality of life. The Tilburg Frailty Indicator (TFI) is a self-report instrument developed to assess frailty for community-dwelling older adults. The aim of this study was to explore the psychometric properties of the Taiwanese version of TFI (TFI-T). The sample consisted of 210 elderly participants living in the community. The scale was implemented to conduct a confirmatory factor analysis (CFA) test for validity. The models were evaluated through sensitivity, specificity, area under the curve, and receiving operating characteristic (ROC) curve. CFA was performed to evaluate construct validity, and the TFI-T has a goodness of fit with the three-factor structure of the TFI. Totally, the 15 items of TFI-T have acceptable internal consistency (Cronbach’s alpha = 0.78), and test–retest reliability (r = 0.88, p < 0.001). The criterion-related validity was examined, the TFI-T correlation with the Kihon Checklist (KCL) score (r = 0.74; p < 0.001). The cutoff of 5.5 based on the Youden index was considered optimal. The area under the ROC curve analysis indicated that the TFI-T has good accuracy in frailty screening. The TFI-T exhibits good reliability and validity and can be used as a sensitive and accurate instrument, which is highly applicable to screen frailty in Taiwan among older adults.

A comparison of different modelling techniques in predicting mortality with the Tilburg Frailty Indicator (TFI) (Preprint)

BACKGROUND Background Modern modelling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. OBJECTIVE Objective We aimed to study the predictive performance of eight modelling techniques to predict mortality by frailty. METHODS Methods We performed a longitudinal study with a 7-year follow-up. The sample consisted of 479 Dutch community-dwelling people >=75 years. Frailty was assessed with the Tilburg Frailty Indicator (TFI), a self-report questionnaire. This questionnaire consisted of eight physical, four psychological and three social frailty components. The municipality of Roosendaal (a city in the Netherlands) provided the mortality dates. We compared modelling techniques such as support vector machine, neural net, random forest, least absolute shrinkage and selection operator and classical techniques such as logistic regression, two 1Bayesian networks and recursive partitioning. The area under the ROC-curve (AUC) indicated the performance of the models. The models were validated using bootstrapping. RESULTS Results We found that the neural net model had the best validated performance (AUC=0.812) followed by the support vector machine model (AUC=0.705). The other models had validated AUCs <0.700. The recursive partitioning model had the lowest validated AUC (0.605). The neural net model had the highest optimism (0.156). The predictor variable ’difficulty in walking’ was important for all models. CONCLUSIONS Conclusions Because of the high optimism of the NN model, we prefer the SVM model for predicting mortality in community-dwelling older people with the TFI with added to it ’gender’ and ’age’. External validation is a necessary step before applying the prediction models in a new setting.

Prevalence and Risk Factors of Preoperative Frailty in Chinese Elderly Inpatients with Gastrointestinal Cancer Undergoing Surgery: A Single-center Cross-sectional Study Using the Groningen Frailty Indicator

BackgroundFrailty is emerging as an important determinant for health. Compared with Western countries, research in the field of frailty started at a later stage in China and mainly focused on older community dwellers. Little is known about frailty in Chinese cancer patients, nor the risk factors of frailty. This study aimed to investigate the prevalence of frailty and its risk factors in elderly inpatients with gastrointestinal cancer. MethodsThis cross-sectional study was performed at a tertiary hospital in China from Mar. 2020 to Nov. 2020. The study enrolled 265 inpatients aged 60 and older with gastrointestinal cancer who underwent surgery. The demographic and clinical characteristics, biochemical laboratory parameters, and anthropometric data were collected from all patients. The Groningen Frailty Indicator was applied to assess the frailty status of patients. Multivariate logistic regression model analysis was carried out to identify risk factors of frailty and estimate their 95% confidence intervals. ResultsThe prevalence of frailty in elderly inpatients with gastrointestinal cancer was 43.8%. A multivariate logistic regression analysis showed that older age (OR=1.065, 95% CI: 1.001-1.132, P=0.045), low handgrip strength (OR=4.346, 95% CI: 1.739-10.863, P=0.002), no regular exercise habit (OR=3.228, 95% CI: 1.230-8.469, P=0.017), and low MNA-SF score (OR=11.090, 95% CI: 5.119-24.024, P<0.001) were risk factors of frailty. ConclusionsThis study suggested that the prevalence of frailty was high among elderly inpatients with gastrointestinal cancer. Older age, low handgrip strength, no regular exercise habit, and low MNA-SF score were identified as risk factors of frailty.

The prevalence of frailty, measured with different diagnostic tools, and autonomy decline: Results of the Crystal study

INTRODUCTION: Frailty prevalence differs across different population depending on the models used to assess, age, economic situation, social status, and the proportion of men and women in the study. The diagnostic value of different models of frailty varies from population to population. OBJECTIVES: To assess the prevalence of frailty using 4 different diagnostic models and their sensitivity for identifying persons with autonomy decline. MATERIAL AND METHODS: A random sample of 611 people aged 65 and over. Models used: the Age is not a blocking factor model, the SOF Frailty Index, the Groningen Frailty Indicator, L. Fried model. Covariates: nutritional status, anemia, functional status, depression, dementia, chronic diseases, grip strength, physical function. RESULTS: The prevalence of the Frailty Phenotype ranged from 16.6 to 20.4% and the Frailty Index was 32.6%. Frailty, regardless of the used models was associated with an increase in the prevalence of the geriatric syndromes: urinary incontinence, hearing and vision loss, physical decline, malnutrition and the risk of malnutrition, low cognitive functions and autonomy decline (p 0.05). The negative predictive value (NPV) of the Age is not a blocking factor model, the SOF Frailty Index, the Groningen Frailty Indicator for identifying individuals with autonomy decline was 8690%. CONCLUSION: The prevalence of frailty depended on the operational definition and varied from 16.6 to 32.6%. The Age is not a blocking factor model, the SOF Frailty Index, the Groningen Frailty Indicator, L. Fried model can be used as screening tools to identify older patient with autonomy decline. Regardless of the model used, frailty is closely associated with an increase in the prevalence of major geriatric syndromes.