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Accessing the discriminatory performance of FRAIL-NH in two-class and three-class frailty and examining its agreement with the frailty index among nursing home residents in mainland China.

ABSTRACT: BACKGROUND:FRAIL-NH has been commonly used to assess frailty in nursing home residents and validated in many ethnic populations; however, it has not been validated in mainland China, where such an assessment tool is lacking. This study aimed to (1) assess the discriminatory performance of FRAIL-NH in two-class frailty (non-frail+ pre-frail vs. frail) and three-class frailty (non-frail vs. pre-frail vs. frail), based on the Frailty Index (FI), (2) determine the appropriate cutoff points for FRAIL-NH that distinguish two-class and three-class frailty, and (3) examine the agreement in classification between FRAIL-NH and FI. METHODS:A cross-sectional study of 302 residents aged 60?years or older from six nursing homes in Changsha was conducted. The FRAIL-NH scale and 34-item FI were used to measure frailty. Two-way and three-way receiver operating characteristic (ROC) curves were used to estimate the performance of FRAIL-NH. Cohen's Kappa statistics were used to examine the agreement between these two measures. RESULTS:The agreement between FRAIL-NH and FI ranged from 0.33 to 0.55. Regardless of what FI cutoff points were based on, the volume under the ROC surface (VUS) for FRAIL-NH from the three-way ROC were higher than the VUS of a useless test (1/6), and the area under the ROC curve (AUC) for FRAIL-NH from the two-way ROC were higher than the clinically meaningless value (0.5). When using FI cutoff points of 0.20 for pre-frail and 0.45 for frail, FRAIL-NH cutoff points of 1 and 9 in classifying three-class frailty had the highest VUS and the largest correct classification rates. Whichever FI was chosen, the performance of FRAIL-NH in distinguishing between pre-frailty and frailty, and between non-frailty and pre-frailty was equivalent. According to FRAIL-NH, the proportion of individuals with frailty misclassified as pre-frailty was higher than that of individuals with non-frailty misclassified as pre-frailty. CONCLUSION:FRAIL-NH can be used as a preliminary frailty screening tool in nursing homes in mainland China. FI should be further used especially for those classified as pre-frailty by FRAIL-NH. It is not advisable to simply combine adjacent two classes of FRAIL-NH to create a new frailty variable in research settings.

SUBMITTER: Ge F

PROVIDER: S-EPMC6822433 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Accessing the discriminatory performance of FRAIL-NH in two-class and three-class frailty and examining its agreement with the frailty index among nursing home residents in mainland China.

Ge Feng F Liu Weiwei W Liu Minhui M Tang Siyuan S Lu Yongjin Y Hou Tianxue T

BMC geriatrics 20191030 1

<h4>Background</h4>FRAIL-NH has been commonly used to assess frailty in nursing home residents and validated in many ethnic populations; however, it has not been validated in mainland China, where such an assessment tool is lacking. This study aimed to (1) assess the discriminatory performance of FRAIL-NH in two-class frailty (non-frail+ pre-frail vs. frail) and three-class frailty (non-frail vs. pre-frail vs. frail), based on the Frailty Index (FI), (2) determine the appropriate cutoff points f ...[more]

PMID: 31666011

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Project description:ObjectivesTo investigate frailty prevalence, cross-sectional associations, predictive validity, concurrent validity, and cross-cultural adaptations of the FRAIL-NH scale.DesignSystematic review.Setting and participantsFrail residents living in nursing homes.MethodsMEDLINE, EMBASE, CINAHL, and Cochrane Library were searched from January 2015 to June 2021 for primary studies that used the FRAIL-NH scale, irrespective of study designs and publication language.ResultsOverall, 40 studies conducted across 20 countries utilized the FRAIL-NH scale; majority in Australia (n=14), followed by China (n=6), United States (n=3), and Spain (n=3). The scale has been translated and back-translated into Brazilian Portuguese, Chinese, and Japanese. Various cut-offs have been used, with ≥2 and ≥6 being the most common cut-offs for frail and most frail, respectively. When defined using these cut-offs, frailty prevalence varied from 15.1-79.5% (frail) to 28.5-75.0% (most frail). FRAIL-NH predicted falls (n=2), hospitalization or length of stay (n=4), functional or cognitive decline (n=4), and mortality (n=9) over a median follow-up of 12 months. FRAIL-NH has been compared to 16 other scales, and was correlated with Fried's phenotype (FP), Frailty Index (FI), and FI-Lab. Four studies reported fair-to-moderate agreements between FRAIL-NH and FI, FP, and the Comprehensive Geriatric Assessment. Ten studies assessed the sensitivity and specificity of different FRAIL-NH cut-offs, with ≥8 having the highest sensitivity (94.1%) and specificity (82.8%) for classifying residents as frail based on FI, while two studies reported an optimal cut-off of ≥2 based on FI and FP, respectively.ConclusionIn seven years, the FRAIL-NH scale has been applied in 20 countries and adapted into three languages. Despite being applied with a range of cut-offs, FRAIL-NH was associated with higher care needs and demonstrated good agreement with other well-established but more complex scales. FRAIL-NH was predictive of adverse outcomes across different settings, highlighting its value in guiding care for frail residents in nursing homes.

Project description:BACKGROUND:A fundamental issue in elderly care is targeting those older people at risk and in need of care interventions. Frailty is widely used to capture variations in health risks but there is no general consensus on the conceptualization of frailty. Indeed, there is considerable heterogeneity in the group of older people characterized as frail. This research identifies frailty profiles based on the physical, psychological, social and cognitive domains of functioning and the severity of the problems within these domains. METHODS:This research was a secondary data-analysis of older persons derived from The Older Person and Informal Caregiver Minimum Dataset. Selected respondents were 60 years and older (n =?43,704; 59.6% female). The following variables were included: self-reported health, cognitive functioning, social functioning, mental health, morbidity status, and functional limitations. Using latent class analysis, the population was divided in subpopulations that were subsequently discussed in a focus group with older people for further validation. RESULTS:We distinguished six frailty profiles: relatively healthy; mild physically frail; psychologically frail; severe physically frail; medically frail and multi-frail. The relatively healthy had limited problems across all domains. In three profiles older people mostly had singular problems in either the physical or psychological domain and the severity of the problems differed. Two remaining profiles were multidimensional with a combination of problems that extended to the social and cognitive domains. CONCLUSIONS:Our research provides an empirical base for meaningful frailty profiles. The profiles showed specific patterns underlying the problems in different domains of functioning. The heterogeneous population of frail older people has differing needs and faces different health issues that should be considered to tailor care interventions. Evaluation research of these interventions should acknowledge the heterogeneity of frailty by profiling.

Project description:BackgroundLittle is known about the heterogeneous clinical profile of physical frailty and its association with cognitive impairment in older U.S. nursing home (NH) residents.MethodsMinimum Data Set 3.0 at admission was used to identify older adults newly-admitted to nursing homes with life expectancy ≥6 months and length of stay ≥100 days (n = 871,801). Latent class analysis was used to identify physical frailty subgroups, using FRAIL-NH items as indicators. The association between the identified physical frailty subgroups and cognitive impairment (measured by Brief Interview for Mental Status/Cognitive Performance Scale: none/mild; moderate; severe), adjusting for demographic and clinical characteristics, was estimated by multinomial logistic regression and presented in adjusted odds ratios (aOR) and 95% confidence intervals (CIs).ResultsIn older nursing home residents at admission, three physical frailty subgroups were identified: "mild physical frailty" (prevalence: 7.6%), "moderate physical frailty" (44.5%) and "severe physical frailty" (47.9%). Those in "moderate physical frailty" or "severe physical frailty" had high probabilities of needing assistance in transferring between locations and inability to walk in a room. Residents in "severe physical frailty" also had greater probability of bowel incontinence. Compared to those with none/mild cognitive impairment, older residents with moderate or severe impairment had slightly higher odds of belonging to "moderate physical frailty" [aOR (95%CI)moderate cognitive impairment: 1.01 (0.99-1.03); aOR (95%CI)severe cognitive impairment: 1.03 (1.01-1.05)] and much higher odds to the "severe physical frailty" subgroup [aOR (95%CI)moderate cognitive impairment: 2.41 (2.35-2.47); aOR (95%CI)severe cognitive impairment: 5.74 (5.58-5.90)].ConclusionsFindings indicate the heterogeneous presentations of physical frailty in older nursing home residents and additional evidence on the interrelationship between physical frailty and cognitive impairment.

Project description:To broaden bioarchaeological applicability of skeletal frailty indices (SFIs) and increase sample size, we propose indices with fewer biomarkers (2-11 non-metric biomarkers) and compare these reduced biomarker SFIs to the original metric/non-metric 13-biomarker SFI. From the 2-11-biomarker SFIs, we choose the index with the fewest biomarkers (6-biomarker SFI), which still maintains the statistical robusticity of a 13-biomarker SFI, and apply this index to the same Medieval monastic and nonmonastic populations, albeit with an increased sample size. For this increased monastic and nonmonastic sample, we also propose and implement a 4-biomarker SFI, comprised of biomarkers from each of four stressor categories, and compare these SFI distributions with those of the non-metric biomarker SFIs. From the Museum of London WORD database, we tabulate multiple SFIs (2- to 13-biomarkers) for Medieval monastic and nonmonastic samples (N = 134). We evaluate associations between these ten non-metric SFIs and the 13-biomarker SFI using Spearman's correlation coefficients. Subsequently, we test non-metric 6-biomarker and 4-biomarker SFI distributions for associations with cemetery, age, and sex using Analysis of Variance/Covariance (ANOVA/ANCOVA) on larger samples from the monastic and nonmonastic cemeteries (N = 517). For Medieval samples, Spearman's correlation coefficients show a significant association between the 13-biomarker SFI and all non-metric SFIs. Utilizing a 6-biomarker and parsimonious 4-biomarker SFI, we increase the nonmonastic and monastic samples and demonstrate significant lifestyle and sex differences in frailty that were not observed in the original, smaller sample. Results from the 6-biomarker and parsimonious 4-biomarker SFIs generally indicate similarities in means, explained variation (R2), and associated P-values (ANOVA/ANCOVA) within and between nonmonastic and monastic samples. We show that non-metric reduced biomarker SFIs provide alternative indices for application to other bioarchaeological collections. These findings suggest that a SFI, comprised of six or more non-metric biomarkers available for the specific sample, may have greater applicability than, but comparable statistical characteristics to, the originally proposed 13-biomarker SFI.

Project description:Introduction:Frailty has been studied among the old population due to its association with negative outcomes. Presently there is no gold standard for measuring frailty, but several studies have used the frailty phenotype of Fried consisting of five components (weakness, slowness, unintentional weight loss, exhaustion, and low physical activity) that classify individuals as robust, pre-frail, or frail, depending on the number of components affected, respectively, zero, one or two, and three or more. This study aims to explore the specific contribution of each of these components to the frailty phenotype in a sample of oldest old community-dwelling individuals. Materials and Methods:Individuals aged 80+ years old living in the community (N = 142) participated in this study. Sociodemographic data (age, sex, educational level, and marital status) and Fried's frailty phenotype were collected. Descriptive analysis summarized sociodemographic information and the frailty components. Multiple correspondence analysis (MCA) was performed to detect and explore relationships between frailty's five components. Results:Participants had a mean age of 88.07 years (SD = 5.30 years) and were mainly women (73.9%). The majority of the sample were considered frail (71.8%) and pre-frail (24.7%), and the most recurrent component for both groups was slowness. From the MCA analysis, a two-dimension solution was considered the most adequate, with 53.47% of variance explained. Dimension 1 (32.21% of variance explained) showed weakness as the most discriminant component; dimension 2 (21.26% of variance explained) showed unintentional weight loss as the most discriminant component. Discussion:Results revealed a high number of pre-frail and frail participants. MCA proved to add an important understanding in examining the frailty phenotype; it revealed weakness as the most discriminant component for dimension 1, suggesting a high association with the frailty phenotype. MCA also identified two main features of frailty: one related with physical features (motor behavioral and grip strength) including weakness, low physical activity, and slowness; and the second related with intrinsic conditions (unintentional weight loss and exhaustion). Conclusion:This study corroborates the need of a differentiated approach to the frailty phenotype among very old individuals, bringing for consideration the specific influence of its components.

Dataset Information

Accessing the discriminatory performance of FRAIL-NH in two-class and three-class frailty and examining its agreement with the frailty index among nursing home residents in mainland China.

Publications

Accessing the discriminatory performance of FRAIL-NH in two-class and three-class frailty and examining its agreement with the frailty index among nursing home residents in mainland China.

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