Dataset Information

Comparison between FRAIL Scale and Clinical Frailty Scale in predicting hospitalization in hemodialysis patients.

ABSTRACT:

Background

To assess the prevalence of frailty by the Clinical Frailty Scale (CFS) and the 5-item FRAIL scale and their association with hospitalization in hemodialysis (HD) patients.

Methods

This was a prospective observational study. We included patients of both genders ≥ 18 years old in HD treatment for at least 3 months. Demographic, clinical, and routine laboratory data were retrieved from the medical charts. Two different frailty assessment tools were used, the CFS and the FRAIL scale. Participants were followed up for 9 months and hospitalizations for all causes were evaluated. A Venn diagram was constructed to show the overlap of possible frailty and pre-frailty. Cox regression was used to identify the association between frailty and hospitalization. The significance level was 5%.

Results

A total of 137 subjects were included in the analysis. The median age was 61 (52-67) years and 60% were male. The hospitalization rate and mortality in 9 months were 22.6% and 7.29%, respectively. Regarding frailty, the overall prevalence was 13.8% assessed by CFS and 36.5% according to the FRAIL scale. In the Cox regression, frailty by FRAIL scale was associated with a 2.8-fold increase in the risk of hospitalization (OR = 2.880; 95% CI = 1.361-6.096; p = 0.006), but frailty assessed by the CFS was not associated with the need for hospitalization.

Conclusion

In HD patients, the FRAIL scale proved to be an easy-to-apply tool, identifying a high prevalence of frailty and being a predictor of hospital admission.

SUBMITTER: Barbosa EMS

PROVIDER: S-EPMC9773694 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Publications

Comparison between FRAIL Scale and Clinical Frailty Scale in predicting hospitalization in hemodialysis patients.

Barbosa Elisabete Mouzinho Soares EMS Pereira Amanda Gomes AG Mori Vitória V da Silva Neves Renan R Vieira Nayane Maria NM Silva Maryanne Zilli Canedo MZC Seki Marcos Mitsuo MM Rodrigues Hellen Christina Neves HCN Costa Nara Aline NA Ponce Daniela D Balbi André Luís AL Zornoff Leonardo Antonio Mamede LAM Azevedo Paula Schmidt PS Polegato Bertha Furlan BF de Paiva Sérgio Alberto Rupp SAR Minicucci Marcos Ferreira MF de Souza Dorna Mariana M

Journal of nephrology 20221222 3

<h4>Background</h4>To assess the prevalence of frailty by the Clinical Frailty Scale (CFS) and the 5-item FRAIL scale and their association with hospitalization in hemodialysis (HD) patients.<h4>Methods</h4>This was a prospective observational study. We included patients of both genders ≥ 18 years old in HD treatment for at least 3 months. Demographic, clinical, and routine laboratory data were retrieved from the medical charts. Two different frailty assessment tools were used, the CFS and the F ...[more]

PMID: 36547774

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Project description:BackgroundOlder adults living with frailty who require treatment in hospitals are increasingly seen in the Emergency Departments (EDs). One quick and simple frailty assessment tool-the Clinical Frailty Scale (CFS)-has been embedded in many EDs in the United Kingdom (UK). However, it carries time/training and cost burden and has significant missing data. The Hospital Frailty Risk Score (HFRS) can be automated and has the potential to reduce costs and increase data availability, but has not been tested for predictive accuracy in the ED. The aim of this study is to assess the correlation between and the ability of the CFS at the ED and HFRS to predict hospital-related outcomes.MethodsThis is a retrospective cohort study using data from Leicester Royal Infirmary hospital during the period from 01/10/2017 to 30/09/2019. We included individuals aged + 75 years as the HFRS has been only validated for this population. We assessed the correlation between the CFS and HFRS using Pearson's correlation coefficient for the continuous scores and weighted kappa scores for the categorised scores. We developed logistic regression models (unadjusted and adjusted) to estimate Odds Ratios (ORs) and Confidence Intervals (CIs), so we can assess the ability of the CFS and HFRS to predict 30-day mortality, Length of Stay (LOS) > 10 days, and 30-day readmission.ResultsTwelve thousand two hundred thirty seven individuals met the inclusion criteria. The mean age was 84.6 years (SD 5.9) and 7,074 (57.8%) were females. Between the CFS and HFRS, the Pearson correlation coefficient was 0.36 and weighted kappa score was 0.15. When comparing the highest frailty categories to the lowest frailty category within each frailty score, the ORs for 30-day mortality, LOS > 10 days, and 30-day readmission using the CFS were 2.26, 1.36, and 1.64 and for the HFRS 2.16, 7.68, and 1.19.ConclusionThe CFS collected at the ED and the HFRS had low/slight agreement. Both frailty scores were shown to be predictors of adverse outcomes. More research is needed to assess the use of historic HFRS in the ED.

Project description:Aims and objectivesTo test the ability of the Reported Edmonton Frail Scale-Thai version to predict hospital outcomes compared with standard preoperative assessment measures (American Society of Anesthesiologists physical status classification and the Elixhauser Comorbidity Measure) in older Thai orthopaedic patients.BackgroundFrailty is a common geriatric condition. No previous studies have assessed frailty among orthopaedic patients in Thailand. Effective frailty screening could enhance quality of care.DesignProspective cohort study in a university hospital.MethodsTwo hundred hospitalised patients, aged 60 years or older and scheduled for orthopaedic surgery, participated in the study. Frailty was evaluated using the Reported Edmonton Frail Scale-Thai version. Multiple Firth logistic regression was used to model the effect of frailty on postoperative complications, postoperative delirium and discharge disposition. Length of stay was examined using Poisson regression. Comparing predictability of the instruments, the area under the receiver operating characteristic curve and mean squared errors were evaluated. The STROBE guideline was used.ResultsParticipants' mean age was 72 years; mostly were female, frail and underwent knee, spine and/or hip surgery. Poor health outcomes including postoperative complications, postoperative delirium, and not being discharged to the home were commonly identified. The length of stay varied from three days to more than ten weeks. Frailty was significantly associated with postoperative complications, postoperative delirium and prolonged length of stay. The Reported Edmonton Frail Scale-Thai version revealed good performance for predicting postoperative complications and postoperative delirium and was improved by combining with standard assessments.ConclusionThe Reported Edmonton Frail Scale-Thai version, alone or combined with standard assessment, was useful for predicting adverse outcomes in older adults undergoing orthopaedic surgery.Relevance to clinical practiceThese findings indicate that nurse professionals should apply culturally sensitive frailty screening to proactively identify patients' risk of frailty, improve care quality and prevent adverse outcomes.

Project description:The Clinical Frailty Scale (CFS) is the most used frailty measure in intensive care unit (ICU) patients. Recently, the modified frailty index (mFI), derived from 11 comorbidities has also been used. It is unclear to what degree the mFI is a true measure of frailty rather than comorbidity. Furthermore, the mFI cannot be freely obtained outside of specific proprietary databases.ObjectiveTo compare the performance of CFS and a recently developed International Classification of Diseases-10 (ICD-10) mFI (ICD-10mFI) as frailty-based predictors of long-term survival for up to 1 year.DesignA retrospective multicentric observational study.Setting and participantsAll adult (≥16 yr) critically ill patients with documented CFS scores admitted to sixteen Australian ICUs in the state of Victoria between April 1, 2017 to June 30, 2018 were included. We used probabilistic methods to match de-identified ICU admission episodes listed in the Australia and New Zealand Intensive Care Society Adult Patient Database with the Victorian Admission Episode Dataset and the Victorian Death Index via the Victorian Data Linkage Centre.Main outcomes and measuresThe primary outcome was the longest available survival following ICU admission. We compared CFS and ICD-10mFI as primary outcome predictors, after adjusting for key confounders.ResultsThe CFS and ICD-10mFI were compared in 7,001 ICU patients. The proportion of patients categorized as frail was greater with the CFS than with the ICD-10mFI (18.9% [n = 1,323] vs. 8.8% [n = 616]; p < 0.001). The median (IQR) follow-up time was 165 (82-276) days. The CFS predicted long-term survival up to 6 months after adjusting for confounders (hazard ratio [HR] = 1.26, 95% CI, 1.21-1.31), whereas ICD-10mFI did not (HR = 1.04, 95% CI, 0.98-1.10). The ICD-10mFI weakly correlated with the CFS (Spearman's rho = 0.22) but had a poor agreement (kappa = 0.06). The ICD-10mFI more strongly correlated with the Charlson comorbidity index (Spearman's rho 0.30) than CFS (Spearman's rho = 0.25) (p < 0.001).ConclusionsCFS, but not ICD-10mFI, predicted long-term survival in ICU patients. ICD-10mFI correlated with co-morbidities more than CFS. These findings suggest that CFS and ICD-10mFI are not equivalent.RelevanceCFS and ICD-10mFI are not equivalent in screening for frailty in critically ill patients and therefore ICD-10mFI in its current form should not be used.

Project description:BackgroundFrailty has emerged as one of the main geriatric syndromes to be prevented in order to improve quality of health and life in the elderly. In this sense, the characterization of this syndrome through reliable and feasible diagnostic tools for clinical use, such as the Frail Trait Scale 5 (FTS-5) and Frail Trait Scale 3 (FTS-3), represents the basis for this objective.ObjectivesTo characterize the frailty syndrome in a population of older adults using FTS-5, FTS-3, and Fried phenotype (FP) as frailty diagnostic tools.DesignCross-sectional study.Participants300 adults ≥65 years recruited from different Family Health Centers and community groups of older people in Talca, Chile.MethodsThe diagnosis of frailty was made according to FP, FTS-5, and FTS-3 tools. Data about sociodemographic characteristics and anthropometric measurements were collected by a clinical interview by a previously trained health professional.ResultsA total prevalence of frailty according to the FP of 19.7% was observed; while in the group of women and men it was 21.4% and 15.0%, respectively. Concerning the FTS-5 tool, the total prevalence of frailty was 18%, while in the group of women and men was 18.0% and 17.5%, respectively. The FTS-3 tool shows a total prevalence of frailty of 23.3%, while in the group of women and men a prevalence of 22.7% and 25.0%, respectively. A significant difference is observed with respect to the presence of the Fried criteria of "weakness" (women: 21.4%, men: 38.8%) and "weight loss" (women: 16.8%, men: 7.5%; p < 0.05). A significant difference is observed concerning the average score of "Handgrip" criteria, "walking time", and "Physical Activity Scale for the Elderly" (PASE) between the group of women and men. Frailty, diagnosed by FTS-3, is significantly associated with the risk factors of overweight (body mass index ≥ 25) (OR: 10.225, 95% CI: 1.297-80.617) and advanced age (age ≥ 75 years) (OR: 1.839, 95% CI: 1.040-3.250).ConclusionThe prevalence of frailty observed with the FTS-5 (18%) and FTS-3 (23.3%) tools are similar to the prevalence observed through the FP (19.7%) and those reported in other observational studies. Considering the similar prevalence of frailty diagnosed with the three tools, FTS-3 should be a valuable tool for the screening of frailty in the community.

Dataset Information

Comparison between FRAIL Scale and Clinical Frailty Scale in predicting hospitalization in hemodialysis patients.

Background

Methods

Results

Conclusion

Publications

Comparison between FRAIL Scale and Clinical Frailty Scale in predicting hospitalization in hemodialysis patients.

Similar Datasets

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