scholarly journals A Comparison of Different Modelling Techniques in Predicting Mortality with the Tilburg Frailty Indicator (Preprint)

10.2196/31480 ◽  
2021 ◽  
Author(s):  
Tjeerd van der Ploeg ◽  
Robbert Gobbens
Keyword(s):  
Tilburg Frailty Indicator ◽  
Modelling Techniques ◽  
Frailty Indicator

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

2021 ◽  
Author(s):  
Tjeerd van der Ploeg ◽  
Robbert Gobbens
Keyword(s):  
Support Vector Machine ◽  
Prediction Models ◽  
Recursive Partitioning ◽  
Community Dwelling ◽  
Self Report ◽  
Support Vector ◽  
Neural Net ◽  
Tilburg Frailty Indicator ◽  
Modelling Techniques ◽  
Frailty Indicator

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.

scholarly journals European Portuguese Version of the Clinical Frailty Scale: Translation, Cultural Adaptation and Validation Study

2021 ◽  
Vol 34 (13) ◽  
Author(s):  
Mário Pereira Pinto ◽  
Sónia Martins ◽  
Edgar Mesquita ◽  
Lia Fernandes
Keyword(s):  
Sensitivity And Specificity ◽  
Validation Study ◽  
Convergent Validity ◽  
Outpatient Clinics ◽  
Clinical Frailty Scale ◽  
Retest Reliability ◽  
European Portuguese ◽  
Tilburg Frailty Indicator ◽  
Test Retest Reliability ◽  
Frailty Indicator

Introduction: This study aims to describe the translation and adaptation of the European Portuguese Clinical Frailty Scale and assess its convergent validity and test-retest reliability.Material and Methods: This validation study included a sample of elderly people admitted in two convalescence units from the National Network of Integrated Continuous Care in Northern Portugal and followed in two outpatient clinics of social solidarity institutions. Convergent validity of the scale was evaluated, against Tilburg Frailty Indicator. Test-retest reliability, sensitivity and specificity were assessed.Results: Overall, 51 patients were included (mean age = 78 years old). The Clinical Frailty Scale identified 43.1% patients with frailty. Kappa values for test-retest reliability (non-frail/frail) was 1.00. The intraclass correlation coefficient for the 9-point total scale was 0.999. A correlation between Clinical Frailty Scale and Tilburg Frailty Indicator was also found (rs = 0.683; p < 0.001). The Cohen’s kappa coefficient was 0.423 in the agreement analysis between these scales. The results for sensitivity and specificity defined that 62.0% of patients were true positives and 81.8% true negatives. The scale accuracy determined by the receiver operating characteristics curve analysis was 0.782.Discussion: This scale showed an excellent test-retest reliability. Robust results on convergent validity were also achieved, with a moderate correlation and agreement with the Tilburg Frailty Indicator, showing good sensitivity and accuracy, as well as high specificity.Conclusion: This version has an excellent test-retest reliability and good convergent validity, and is both a reliable and valid test for application in clinical practice for assessing Portuguese elderly population admitted in convalescence units and outpatient clinics.

Psychometric properties of the Tilburg Frailty Indicator in older Spanish people

2018 ◽  
Vol 78 ◽  
pp. 203-212 ◽  
Author(s):  
Kalliopi Vrotsou ◽  
Mónica Machón ◽  
Francisco Rivas-Ruíz ◽  
Estefanía Carrasco ◽  
Eugenio Contreras-Fernández ◽  
...  
Keyword(s):  
Psychometric Properties ◽  
Tilburg Frailty Indicator ◽  
Frailty Indicator

Tilburg Frailty Indicator--Brazilian Version

2013 ◽  
Author(s):  
Lívia Maria Santiago ◽  
Laércio Lima Luz ◽  
Inês Echenique Mattos ◽  
Robbert J. J. Gobbens ◽  
Marcel A. L. M. van Assen
Keyword(s):  
Brazilian Version ◽  
Tilburg Frailty Indicator ◽  
Frailty Indicator

scholarly journals The Relationship between Frailty Syndrome and Concerns about an Implantable Cardioverter Defibrillator

2020 ◽  
Vol 17 (6) ◽  
pp. 1954
Author(s):  
Agnieszka Mlynarska ◽  
Rafal Mlynarski ◽  
Izabella Uchmanowicz ◽  
Czeslaw Marcisz ◽  
Krzysztof S. Golba
Keyword(s):  
Implantable Cardioverter Defibrillator ◽  
Frailty Syndrome ◽  
Icd Implantation ◽  
Cardioverter Defibrillator ◽  
Questionnaire Score ◽  
Tilburg Frailty Indicator ◽  
Increased Sensitivity ◽  
The Impact ◽  
The Relationship ◽  
Frailty Indicator

Frailty syndrome may cause cognitive decline and increased sensitivity to stressors. This can result in an increased incidence of anxiety and depression, and thus, concerns about life with an implantable cardioverter defibrillator (ICD). The aim of the study was to assess the impact of frailty syndrome on the increase in the number of device-related concerns after the implantation of an ICD. Material and methods: The study sample was a group of 103 consecutive patients (85 M; aged 71.6 ± 8.2) with an implanted ICD. The ICD Concerns Questionnaire (ICDC) was used to analyze their concerns about life with an ICD, and the Tilburg Frailty Indicator scale (TFI) was used to diagnose frailty. Results: In the group of patients with an ICD implanted, 73% had recognized frailty (83.3% women, 74.1% men); the average point value was 6.55 ± 2.67. The total ICDC questionnaire score for the patients with an implanted cardioverter defibrillator was 34.06 ± 18.15. Patients with frailty syndrome had statistically (p = 0.039) higher scores (36.14 ± 17.08) compared to robust patients (27.56 ± 20.13). In the logistic regression analysis, the presence of frailty was strongly associated with the total questionnaire score (OR = 1.0265, p = 0.00426), the severity of the concerns (OR = 1.0417, p = 0.00451), and device-specific concerns (OR = 1.0982, p = 0.00424). Conclusion: Frailty syndrome occurs in about 80% of patients after ICD implantation. The presence of frailty syndrome was strongly associated with concerns about an implantable cardioverter defibrillator.

P1906Modified frailty as a novel factor to predict the effectiveness of electrical cardioversion of atrial fibrillation in the elderly population

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
A Mlynarska ◽  
R Mlynarski ◽  
C Marcisz ◽  
K S Golba
Keyword(s):  
Sinus Rhythm ◽  
Elderly Population ◽  
Independent Predictor ◽  
Area Under The Curve ◽  
Roc Curves ◽  
The Elderly ◽  
Electrical Cardioversion ◽  
Cutoff Value ◽  
Tilburg Frailty Indicator ◽  
Frailty Indicator

Abstract Electrical cardioversion is one of the recognized methods of treatment of atrial fibrillation (AF)/maintenance of sinus rhythm. There are no factors that allow to predict the response to electrical cardioversion in the elderly population. Frailty is a common geriatric syndrome that embodies an elevated risk of catastrophic declines in health and function among older adults – we hypothesized that frailty can be a factor to predict the effectiveness electrical cardioversion. Methods 199 consecutive patients over 60 years (average age 71.41±6.99; 40.2% W) with AF hospitalized due to electrical cardioversion. The follow-up period for maintenance of sinus rhythm was 180±14 days. The Tilburg Frailty Indicator was used to examine the frailty before cardioversion. A global score of 5 points or more let us to respond the frailty syndrome. Results 174 patients out of 199 (87.4%) were found to have electrical cardioversion be effective. Frailty was recognized in 68 out of the 199 (34.17%). There was 66.2% (45/68) effectiveness in the frailty-affected group, whereas there was 99.2% (130/131) in the robust group; p=0.ehz748.0653. 87.9% patients have maintenance sinus rhythm after 6 months; in the frailty affected group it was 75% vs 94.6% in robust group; p=0.ehz748.0653. In the logistic regression, frailty (OR: 0.41, 95% CI: 0.2870–0.0,5851; p<0.0001) was emerged as an independent predictor of effectiveness of electrical cardioversion. The ROC curves for frailty in the effectiveness electrical cardioversion are presented in the figure below – left panel. The area under the curve is 0.856 (95% CI - 0.799–0.902). The cutoff value for a frailty recognition was 4 (p<0.0001). Similarly, in the logistic regression, frailty (OR: 0.65, 95% CI: 0.5010–0.8330; p=0.0003) was also emerged as an independent predictor of maintenance sinus rhythm. The ROC curves for frailty in the maintenance of sinus rhythm are presented in the figure – right panel. The area under the curve is 0.718 (95% CI - 0.650–0.779). The cutoff value for a frailty recognition in this case was also 4 (p<0.0001). ROC curves Conclusion Frailty is a novel, independent factor that can be used to predict the effectiveness of electrical cardioversion and maintenance of sinus rhythm in the elderly population. Modifying the level of recognition in the Tilburg Frailty Indicator to a 4, improve the prediction of effectiveness of electrical cardioversion as well as maintenance of sinus rhythm.

scholarly journals Prevalence and factors associated with frailty among community-dwelling older people in rural Thanjavur district of South India: a cross-sectional study

BMJ Open ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. e032904 ◽  
Author(s):  
Kirubakaran Kesavan Kendhapedi ◽  
Niveditha Devasenapathy
Keyword(s):  
Older People ◽  
Fear Of Falling ◽  
Frailty Index ◽  
Cross Sectional Study ◽  
Community Dwelling ◽  
Cross Sectional ◽  
Domain Definition ◽  
Tilburg Frailty Indicator ◽  
Community Dwelling Older People ◽  
Frailty Indicator

ObjectiveThere is sparse data on the prevalence of frailty from rural parts of India. Our aim was to estimate prevalence of frailty among community-dwelling older people in rural South Indian population and explore socio-demographic factors associated with frailty. We further explored the associations between frailty with fear of falling and falls.DesignCommunity based cross-sectional study.SettingFour villages in Thanjavur district of Southern India.ParticipantsRandom sample of adults aged 60 years and above from four villages.MethodsWe sampled community-dwelling older adults from the electoral list of four villages using stratified random sampling. We report prevalence of frailty as defined by physical definition (Fried’s Phenotype), accumulation of deficits (Frailty Index) and multi-domain definition (Tilburg Frailty Indicator). We report proportion of agreement of frailty status between the frailty tools. We used logistic regressions with robust SEs to examine the associations between socio-demographic determinants with frailty and the association between frailty with fear of falling and falls.ResultsAmong the 408 participants, the weighted (non-response and poststratification for sex) prevalence and 95% CI of frailty was 28% (18.9 to 28.1) for physical definition, 59% (53.9 to 64.3) for accumulation of deficits and 63% (57.4 to 67.6) for multi-domain definition. Frailty Index and Tilburg Frailty Indicator had good agreement (80%). Age, female, lower education, lower socioeconomic status, minimum physical activity in routine work were independently associated with frailty irrespective of the frailty definitions. Frail elderly had higher odds of falls as well as fear of falling compared with non-frail, irrespective of the definitions.ConclusionPrevalence of frailty among older people in rural Thanjavur district of South India was high compared with low-income and middle-income countries. Understanding the modifiable determinants of frailty can provide a valuable reference for future prevention and intervention.

scholarly journals Reliability and Validity of the Tilburg Frailty Indicator in 5 European Countries

2020 ◽  
Vol 21 (6) ◽  
pp. 772-779.e6 ◽  
Author(s):  
Xuxi Zhang ◽  
Siok Swan Tan ◽  
Lovorka Bilajac ◽  
Tamara Alhambra-Borrás ◽  
Jorge Garcés-Ferrer ◽  
...  
Keyword(s):  
Reliability And Validity ◽  
European Countries ◽  
Tilburg Frailty Indicator ◽  
Frailty Indicator

scholarly journals The Italian Version of the Tilburg Frailty Indicator

2016 ◽  
Vol 38 (8) ◽  
pp. 842-863 ◽  
Author(s):  
Anna Mulasso ◽  
Mattia Roppolo ◽  
Robbert J. J. Gobbens ◽  
Emanuela Rabaglietti
Keyword(s):  
Italian Version ◽  
Tilburg Frailty Indicator ◽  
Frailty Indicator

Psychometric properties of the Brazilian version of the Tilburg frailty indicator (TFI)

2013 ◽  
Vol 57 (1) ◽  
pp. 39-45 ◽  
Author(s):  
Lívia Maria Santiago ◽  
Laércio Lima Luz ◽  
Inês Echenique Mattos ◽  
Robbert J.J. Gobbens ◽  
Marcel A.L.M. van Assen
Keyword(s):  
Psychometric Properties ◽  
Brazilian Version ◽  
Tilburg Frailty Indicator ◽  
Frailty Indicator
Sign in / Sign up

Export Citation Format

Share Document