Assessing Frailty in Chinese Nursing Home Older Adults: A Comparison between the Frail-NH Scale and Frailty Index

Gao, Feng; Liu, Minhui; Tang, Siyuan; Lu, Yongde; Szanton, Sarah L.

doi:10.1007/s12603-019-1156-3

Cited by 15 publications

(17 citation statements)

References 25 publications

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“…Cut-off points of FI are needed to identify frail older adults and to estimate the prevalence of frailty at the population level 31. At present, the universally accepted category of FI scores are as follows: non-frail (0 to 0.10), vulnerable (0.10 to 0.21), frail (0.22 to 0.44) and frailest (≥0.45) 31. In the present study, the FI is categorised as non-frailty (0 to 0.21) and frailty (>0.21) 32…”

Section: Methodsmentioning

confidence: 99%

Association between the number of teeth and frailty among Chinese older adults: a nationwide cross-sectional study

Bai

et al. 2019

BMJ Open

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ObjectivesTo explore the association between the number of teeth and frailty among older Chinese adults using a nationally representative sample.DesignCross-sectional analysis was carried out using the 2014 wave data from the Chinese Longitudinal Healthy Longevity Survey, which used a targeted random-sampling design.SettingThis research was conducted in communities from nearly half of the counties and cities in 22 out of 31 provinces throughout China.ParticipantsOf the 6934 interviewees aged ≥65 years, the final analysis included 3635 older adults who had completed the 2014 wave survey on the variables included in the study.Primary and secondary outcome measuresOutcome variables included frailty, measured by the Frailty Index, and number of teeth. Covariates included demographic characteristics (ie, age, sex, co-residence, marital status, years of education and financial support), body mass index (BMI) and health behaviours (ie, smoking, drinking and exercise). A univariate logistic regression was used to test the factors associated with frailty. A multiple logistic regression model was used, using the frailty score as the dependent variable and the number of teeth together with significant covariates as the independent variables.ResultsThe prevalence of frailty was 27.68%. The mean number of teeth present was 9.23 (SD=10.03). The multiple logistic regression showed that older adults’ demographic variables, health behaviours, BMI, tooth number and chewing pain were significantly associated with frailty. After adjusting for the covariates, older adults with fewer teeth had significantly higher odds of frailty than those with 20 or more teeth (no teeth: OR=2.07, 95% CI 1.53 to 2.80; 1 to 10 teeth: OR=1.77, 95% CI 1.31 to 2.38), except for older adults with 11 to 20 teeth (OR=1.30, 95% CI 0.93 to 1.82).ConclusionsThe presence of fewer teeth is significantly associated with frailty status among older Chinese adults. Future studies are needed to explain the specific mechanisms underlying how oral health status is associated with frailty.

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Section: Methodsmentioning

confidence: 99%

Association between the number of teeth and frailty among Chinese older adults: a nationwide cross-sectional study

Bai

et al. 2019

BMJ Open

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“…The FRAIL-NH scale was applied in studies across 20 countries; the majority in Australia (n=14) ( 21 – 34 ), followed by China (n=6) ( 35 – 40 ), Spain (n=3) ( 41 – 43 ), United States (US) (n=3) ( 44 – 46 ), Brazil (n=2) ( 47 , 48 ), Japan (n=2) ( 49 , 50 ), Singapore (n=2) ( 51 , 52 ), Belgium (n=1) ( 53 ), Canada (n=1) ( 54 ), England (n=1) ( 50 ), Europe and Israel (n=1) ( 55 ), France (n=1) ( 56 ), Hong Kong (n=1) ( 57 ), Italy (n=1) ( 58 ), South Korea (n=1) ( 59 ), and Taiwan (n=1) ( 60 ). One study was an international multicenter study based on data from the Services and Health for Elderly in Long TERm care (SHELTER) study, conducted in 8 countries including Czech Republic, England, Finland, France, Germany, Israel, Italy, and the Netherlands ( 55 ).…”

Section: Resultsmentioning

confidence: 99%

“…Study designs included observational studies (n=31), experimental studies (n=6), qualitative studies (n=2), and a methodological study (n=1) (Table 1 ). Six articles utilized data from an Australian cohort study ( 23 , 25 , 26 , 29 – 31 ), five from an Australian randomized controlled trial (RCT) ( 22 , 27 , 28 , 32 , 33 ), three from a Spanish longitudinal study ( 41 – 43 ), and three from a Chinese cross-sectional study ( 35 – 37 ). The number of participants ranged from 13 to 571,139, with a mean age range of 79.0 ± 8.8 to 89.4 ± 4.6 years.…”

Section: Resultsmentioning

confidence: 99%

“…Other common cut-offs used to define frailty were ≥6 (n=5) ( 39 , 42 , 43 , 48 , 60 ) and ≥8 (n=8) ( 37 , 44 – 47 , 54 , 55 , 58 ). Ten studies assessed the sensitivity and specificity of different cut-offs ( 24 , 29 , 35 – 40 , 47 , 52 ), with ≥8 having the highest sensitivity (94.1%) and specificity (82.8%) for classifying residents as frail based on FI ( 37 ), while two studies reported an optimal cut-off of ≥2 using FI and FP, respectively ( 29 , 40 ). Optimal cut-offs were primarily established based on FI, FP, or the Comprehensive Geriatric Assessment (CGA), and determined using Youden Index or receiver operating characteristic curves.…”

Section: Resultsmentioning

confidence: 99%

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The FRAIL-NH Scale: Systematic Review of the Use, Validity and Adaptations for Frailty Screening in Nursing Homes

Liau

Lalic

Visvanathan

et al. 2021

The Journal of nutrition, health and aging

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Objectives To investigate frailty prevalence, cross-sectional associations, predictive validity, concurrent validity, and cross-cultural adaptations of the FRAIL-NH scale. Design Systematic review. Setting and Participants Frail residents living in nursing homes. Methods MEDLINE, 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. Results Overall, 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 cutoffs, 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. Conclusion In 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. Electronic Supplementary Material Supplementary material is available for this article at 10.1007/s12603-021-1694-3 and is accessible for authorized users.

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“…A total of 302 nursing home residents were included in this study based on the inclusion and exclusion criteria, and the completeness of data. Details about the study design and process were described elsewhere [25]. The study was approved by the Nursing and Behavioral Medicine Research Ethics Committee of Central South University Xiangya Nursing School (IRB approval number: 2018012).…”

Section: Methodsmentioning

confidence: 99%

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

Gao

Liu

et al. 2019

BMC Geriatr

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BackgroundFRAIL-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.MethodsA 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.ResultsThe 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.ConclusionFRAIL-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.

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Assessing Frailty in Chinese Nursing Home Older Adults: A Comparison between the Frail-NH Scale and Frailty Index

Cited by 15 publications

References 25 publications

Association between the number of teeth and frailty among Chinese older adults: a nationwide cross-sectional study

Association between the number of teeth and frailty among Chinese older adults: a nationwide cross-sectional study

The FRAIL-NH Scale: Systematic Review of the Use, Validity and Adaptations for Frailty Screening in Nursing Homes

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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