Abstract Aim Screening is one of the most important ways for early frailty detection that contributes to its prevention and timely treatment. The aim of this study was to determine the diagnostic value of the Persian version of the Tilburg Frailty Indicator (P-TFI) in the frailty screening. Method This is a diagnostic test accuracy study that uses known group method. It was designed based on a STARD statement and performed on 175 elderly people in the City of Kashan, Iran. The subjects were selected among older people available in health centers affiliated to Kashan University of Medical Sciences using purposive sampling. Data analysis was carried out using SPSS v16. Descriptive statistics were used to describe the characteristics of the research subjects. Independent t-test was used to determine the ability of the P-TFI to discriminate frail and non-frail individuals, and to evaluate the cut-off point and instrument accuracy, the receiver operating characteristic (ROC) curve was used. The best cut-off point was determined among the proposed points using Youden index. At the determined cut-off point, the diagnostic value parameters of the P-TFI (sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, accuracy, and diagnostic odds ratio) were calculated and their range was estimated with 95 % confidence interval. Findings A total of 74.3 % of the sample was male and their mean age was 68.6 ± 54.44 years. The area under the ROC curve was calculated 0.922, indicating high accuracy of the instrument. The sensitivity and specificity of this instrument at the cut-off point of 4.5 were 0.95 and 0.86, respectively. Positive and negative predictive values were calculated 0.68 and 0.98, respectively, and the accuracy of the instrument was reported to be 0.88. Conclusion The P-TFI can be used as a sensitive and accurate instrument, which is highly applicable to screen frailty in older people.
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Abstract Aim Screening is one of the most important ways for early frailty detection that contributes to its prevention and timely treatment. The aim of this study was to determine the diagnostic value of the Persian version of the Tilburg Frailty Indicator (P-TFI) in the frailty screening. Method This is a diagnostic test accuracy study that uses known group method. It was designed based on a STARD statement and performed on 175 elderly people in the City of Kashan, Iran. The subjects were selected among older people available in health centers affiliated to Kashan University of Medical Sciences using purposive sampling. Data analysis was carried out using SPSS v16. Descriptive statistics were used to describe the characteristics of the research subjects. Independent t-test was used to determine the ability of the P-TFI to discriminate frail and non-frail individuals, and to evaluate the cut-off point and instrument accuracy, the receiver operating characteristic (ROC) curve was used. The best cut-off point was determined among the proposed points using Youden index. At the determined cut-off point, the diagnostic value parameters of the P-TFI (sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, accuracy, and diagnostic odds ratio) were calculated and their range was estimated with 95 % confidence interval. Findings A total of 74.3 % of the sample was male and their mean age was 68.6 ± 54.44 years. The area under the ROC curve was calculated 0.922, indicating high accuracy of the instrument. The sensitivity and specificity of this instrument at the cut-off point of 4.5 were 0.95 and 0.86, respectively. Positive and negative predictive values were calculated 0.68 and 0.98, respectively, and the accuracy of the instrument was reported to be 0.88. Conclusion The P-TFI can be used as a sensitive and accurate instrument, which is highly applicable to screen frailty in older people.
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Background: Modern modeling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. Objective: In this study, we aimed to examine the predictive performance of eight modeling techniques to predict mortality by frailty. Methods: We performed a longitudinal study with a 7-year follow-up. The sample consisted of 479 Dutch community-dwelling people, aged 75 years and older. Frailty was assessed with the Tilburg Frailty Indicator (TFI), a self-report questionnaire. This questionnaire consists 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 modeling techniques, such as support vector machine (SVM), neural network (NN), random forest, and least absolute shrinkage and selection operator, as well as classical techniques, such as logistic regression, two Bayesian networks, and recursive partitioning (RP). The area under the receiver operating characteristic curve (AUROC) indicated the performance of the models. The models were validated using bootstrapping. Results: We found that the NN model had the best validated performance (AUROC=0.812), followed by the SVM model (AUROC=0.705). The other models had validated AUROC values below 0.700. The RP model had the lowest validated AUROC (0.605). The NN model had the highest optimism (0.156). The predictor variable “difficulty in walking” was important for all models. Conclusions: Because of the high optimism of the NN model, we prefer the SVM model for predicting mortality among community-dwelling older people using the TFI, with the addition of “gender” and “age” variables. External validation is a necessary step before applying the prediction models in a new setting.
