Study on the Clinical Implications of NLR and PLR for Diagnosing Frailty in Maintenance Hemodialysis Patients and Their Correlations with Patient Prognosis

Objective. To explore the clinical implications of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) for diagnosing frailty in patients with maintenance hemodialysis (MHD) and their correlations with patient prognosis. Methods. A total of 185 patients with MHD admitted to the hemodialysis center of our hospital were selected, 72 of whom were diagnosed with frailty according to the Chinese version of Tilburg Frailty Indicator (TFI). The relevant data were collected, and the influencing factors of frailty in MHD patients were analyzed by one-way analysis of variance (ANOVA) and multivariate logistic regression. The value of NLR and PLR in diagnosing frailty in MHD patients was observed, and patients’ all-cause mortality was compared during the 3-year follow-up. The influences of different levels of NLR and PLR on the survival of MHD patients were investigated. Results. Multivariate regression analysis identified that serum albumin, dialysis adequacy, NLR, and PLR are independent risk factors for frailty in MHD patients ( P < 0.05 ). The area under the receiver operating characteristic (ROC) curve of NLR and PLR in diagnosing frailty in MHD patients was 0.859 and 0.799, respectively. Compared with the nonfrailty group, the 3-year mortality was higher, and the 3-year survival rate assessed by survival analysis was lower in the frailty group ( P < 0.05 ). Patients with high NLR and PLR levels showed a lower 3-year survival rate. Conclusions. Dialysis adequacy, serum albumin, NLR, and PLR are independently associated with frailty in MHD patients. NLR and PLR are of a certain diagnostic value for frailty in MHD patients. MHD patients with frailty have an unfavorable prognosis, as of those with high NLR and PLR levels.

Self-Reported Frailty Concerns in Older Breast Cancer Survivors During the COVID-19 Pandemic

Frailty among older adults is common, especially those who have undergone breast cancer treatment; however, we do not know how frailty among this group presented during the COVID-19 pandemic. The purpose of this descriptive, cross-sectional study was to examine self-reported frailty among older breast cancer survivors (BCS) during the pandemic. This IRB-approved study recruited BCS who were at least 1-year post-treatment and 60 years of age or older, via online advertisements (e.g., Dr. Susan Love Foundation). BCS completed demographic and Tilburg Frailty Indicator (TFI) RedCap questionnaires from 11/2020 to 05/2021. The TFI, is a 15-item measure with 3 sub-scales with published cut points indicating frailty: total (5), physical (3), psychological (2), and social (2). Descriptive statistics were used. Older BCS (n=203) who were on average 65.5 (SD=4.7) years of age, white (93.6%; n=190) and had stage II breast cancer at diagnosis (39.9%; n=81) participated. The average total (M=5.4, SD=2.5) and physical (M=3.2, SD=1.5) frailty scores were above the threshold for frailty. Overall, 58.6% (n=119) and 63.1% (n=128) scored at or above the threshold on the total and physical sub-scales, respectively. In addition,78.8% (n=160) responded that they ‘missed having people around’ on the social frailty sub-scale. Research has shown that higher TFI scores (more frailty) are associated with increased healthcare utilization, poorer quality of life, and even mortality. Thus, frailty among older BCS is an important health concern within the context of the pandemic. Further research is needed to understand the lasting effects of self-reported frailty for BCS including COVID-19 survivors.

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.

The influence of frailty syndrome on quality of life in patients with atrial fibrillation

Funding Acknowledgements Type of funding sources: None. Introduction Atrial fibrillation (AF) is associated with decreased quality of life (QoL) compared to the general population and to other cardiovascular conditions. In addition, the co-occurrence of frailty syndrome (FS) affects the risk of developing adverse health outcomes including disability and rehospitalizations. This may further translate into a decreased subjective sense of QoL. Purpose To evaluate the prevalence of FS and its impact on subjective QoL in a group of patients with AF. Methodology 116 patients (mean age 75.21 ± 8.19) with diagnosis of AF hospitalized at the Cardiology Department were included in the study. Medical record analysis and self-administered questionnaire were used to obtain basic sociodemographic and clinical data. The prevalence of FS was assessed using the Tilburg Frailty Indicator (TFI) questionnaire. The standardized Arrhythmia-Specific questionnaire in Tachycardia and Arrhythmia (ASTA) assessing quality of life was used. Results FS was diagnosed in 67.24% of the patients. The group with FS was predominantly elderly (p &lt; 0.001), female (p = 0.03), less educated (p = 0.04), single (p= &lt;0.001), with coexisting heart failure (p = 0.015 ) and with higher EHRA classification (p = 0.014). Better QoL was demonstrated in the group of patients without FS in the total score (p = 0.004), psychological domain (p = 0.014) and physical domain (p = 0.004). There was a significant positive correlation of TFI total score with overall QoL (B = 0.383; p &lt; 0.001) and psychological (B = 0.355; p &lt; 0.001) and physical (B = 0.336; p &lt; 0.001) domains. Conclusions FS is common in patients with AF. Assessment of FS occurrence is important for everyday clinical practice because FS lowers QoL. The consequence of FS and decreased QoL may be worse patient prognosis and increased number of hospitalizations.

Multimorbidity, Trauma Exposure, and Frailty of Older Adults in the Community

The aim of this study is to investigate the relation between multimorbidity, traumatic events and frailty among older adults in the community. The studied population consisted of 257 older people who were recipients of the services and active members of Open Care Centers for the Elderly (OCCE) of the Municipality of Grevena and meet a set of selection criteria. The collection of the data was carried out using a fully structured questionnaire, which consisted of two sections: a form of individual features and the Tilburg Frailty Indicator (TFI). The sample consisted of 114 men (44.4%) and 143 women (55.6%) aged between 61 and 96 years with an average of 75.12 years. The results showed that the mean scores were 2.70 for the Physical Frailty (standard deviation = 2.16), 1.43 for the Psychological Frailty (standard deviation = 1.21), 1.32 for the Social Frailty (standard deviation = 0.64) and 5.44 for the total Frailty (standard deviation = 3.02). We took into account the cut-off point five of 54.1% (n = 139) in terms of the participants’ frailty. Physical, Psychological, and Total Frailty are related to (a) the presence of two or more chronic diseases or disorders, (b) the experience of a serious illness in the previous year, and (c) the experience of a serious illness of a loved one during the previous year. The outcomes helped to identify frailty syndrome in older people and the factors associated with it.