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Validation of clinical frailty scale in Chinese translation

BMC Geriatrics volume 22, Article number: 604 (2022) Cite this article

Abstract

Background

Identification of frailty is crucial to guide patient care for the elderly. The Clinical Frailty Scale (CFS) is a reliable, synthesis and clinical judgment-based tool. However, a validated Chinese version of CFS (CFS-C) is lacking. The aim of this study is to describe the translation process of CFS into traditional Chinese and to evaluate its reliability and validity in a geriatric study population in Taiwan.

Methods

This cross-sectional study recruited 221 geriatric outpatients aged 65 years or older at a medical center in Taipei, Taiwan. The Chinese version of CFS was produced following Brislin’s translation model. Weighted kappa for agreement and Kendall’s tau for correlation were used to assess inter-rater reliability (a subgroup of 52 outpatients) between geriatricians and one research assistant, and validity tests (221 outpatients) by comparing CFS-C with Fried frailty phenotype and Frailty Index based on Comprehensive Geriatric Assessment (FI-CGA). Correlation between CFS-C and other geriatric conditions were also assessed.

Results

The inter-rater reliability revealed moderate agreement (weighted kappa = 0.60) and strong correlation (Kendall’s tau = 0.67). For criterion validity, CFS-C categorisation showed fair agreement (weighted kappa = 0.37) and significant correlation (Kendall’s tau = 0.46) with Fried frailty phenotype, and higher agreement (weighted kappa = 0.51) and correlation (Kendall’s tau = 0.63) with FI-CGA categorisation. CFS-C was significantly correlated with various geriatric assessments, including functional disability, physical performance, hand grip, comorbidity, cognition, depression, and nutrition status. No significant correlation was found between CFS-C and appendicular muscle mass.

Conclusions

The CFS-C demonstrated acceptable validity and reliability in Chinese older adults in Taiwan. Development of CFS-C enhanced consistency and accuracy of frailty assessment, both in research and clinical practice.

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Background

Frailty has become an emerging concern as the population ages worldwide with its prevalence varied from 4 to 59% according to different measures [1]. It is a state of decreased reserve capacity which leads to vulnerability to various stressors [2] and associates with increased risk for falls, fractures, disability, institutionalization, hospitalization and death [3,4,5,6,7,8]. As frailty indicates more about the aging process than chronological age alone, its assessment can help to identify older adults at risk and corresponding interventions [9]. Because of its dynamic and potential reversible nature, early identification of frailty is crucial to guide patient care for elderly with different degrees of frailty [10, 11].

Currently, there is no single standard definition of frailty. There are several operational instruments of frailty mainly derived from two approaches: frailty phenotype by Fried et al. and the Frailty Index (FI) of accumulation deficits by Rockwood et al. [11, 12]. The former defines frailty by using five standardized, physiologically based signs and symptoms, and the latter defines frailty by counting age-related deficits (at least 30), including not only signs and symptoms but also diseases and disabilities [2, 11, 13]. These two concepts, representing different aspects of frailty, are considered as complementary rather than substitutable [14]. However, measurement of grip strength and gait speed in frailty phenotype or collecting data of FI is sometimes time-consuming in clinical settings [12].

By contrast, the Clinical Frailty Scale (CFS) by Rockwood et al. has been adapted into a relatively quick, reliable and clinical judgment-based tool. A 7-point version was originally developed for the Canadian Study of Health and Ageing (CSHA) and was highly correlated with FI [15]. It was further updated as a 9-point version (CFS version 1.2) [16]. CFS was associated with mortality, comorbidity, cognition, falls, and function [17]. In this pandemic era, CFS was also associated with mortality in coronavirus disease 2019 (COVID-19) with dose–response relationship and was recommended as a tool for individualized assessment of frailty to manage COVID-19 in older adults [18, 19].

Validated CFS studies in different languages have been published, such as French, Danish, Greek, and Korean [20,21,22,23,24]. CFS usage is also growing in the Asia–Pacific region [12]. Since Taiwan is one of the fastest-ageing countries in the world, domestic policies have evolved to prioritize health and social care for older adults. CFS is the recommended tool for frailty assessment in integrated outpatient services in Taiwan and now serves as one of the inclusion criteria for the post-acute care program [25, 26]. As practice guidelines recommend identifying frailty using validated frailty measurement tools, validation of a Chinese version of CFS is warranted [12].

We previously validated a simplified telephone version in Chinese of the CSHA CFS for rapid screening of frail elders in the community [27]. However, it was adapted from the original 7-point CFS, and was never formally translated. Some differences, including considerations of cognition, pattern of disabilities, and life expectancy, existed between the 7-point and 9-point CFS [16]. Therefore, for more accurate assessment of different levels of frailty, the aim of this study is to describe the translation process of the 9-point CFS into Chinese (traditional Chinese) and to evaluate its reliability and criterion concurrent validity by comparing against two main instruments of frailty: Fried frailty phenotype and FI.

Methods

Study population and design

This was a cross-sectional validation study using data from a prospective cohort study recruiting geriatric outpatients at the National Taiwan University Hospital (NTUH) in Taipei, Taiwan. Data was collected between June and December 2019. The inclusion criteria were age ≥ 65 years and having at least one of the following geriatric syndromes: fall or functional decline in recent one year, polypharmacy ≥ 5, urinary incontinence, history of osteoporosis or weight loss (≥ 5% in one month or 10% in 6 months). Our study excluded patients with severe dementia, severe hearing or visual impairment, severe functional impairment or contact precautions for multidrug resistant organisms in order to avoid communication or cooperation barriers. The study was approved by the Research Ethics Committee at NTUH. Written informed consent of the study participants was obtained before enrollment.

Sample size calculation

For inter-rater reliability, we assumed the minimum acceptable kappa was 0.2 and anticipated a substantial agreement (weighted Kappa = 0.61–0.80) between CFS-C of physicians and one research assistant. At least 48 participants were required for assuring a power of 80% and a significant level of 0.05 to detect a statistically significant kappa coefficient [28, 29]. For criterion validity, we assumed the minimum acceptable Kendall’s tau correlation was 0.2 and expected a high correlation (Kendall’s tau > 0.3) between CFS-C and Fried frailty phenotype. Thus, at least 211 participants were required for assuring a power of 80% and a significance level of 0.05 to detect a statistically significant Kendall’s tau coefficient [30]. Allowing 5–10% attrition rate for missing data, our study enrolled 226 geriatric outpatients. After exclusion of 5 participants who had no CFS-C assessment (n = 4) or no BabyBot vital data (n = 1), a total of 221 subjects were included for criterion validity and 52 of them were included for reliability analysis [see Additional file 1].

Data collection

A wide range of demographic and health data was collected on BabyBot vital data recording system (Netown Corporation, Taiwan) and comprehensive geriatric assessment (CGA). BabyBot included a 68-item self-reported questionnaire, bioelectrical impedance analysis (Tanita BC-418), and tests of hand grip, timed-up and go (TUG), and 6-m walk. CGA, comprised of Mini-Mental State Examination (MMSE) [31], Geriatric Depression Scale-15 (GDS-15) [32], Mini-Nutritional Assessment (MNA) [33], Barthel Index (BI) [34], and Instrumental Activities of Daily Living (IADL) [35], was evaluated by a trained research assistant. To measure comorbidity, six geriatricians scored the Cumulative Illness Rating Scale for Geriatrics (CIRS-G) [36].

Translation of the clinical frailty scale into Chinese

With Dr. Rockwood’s permission, we undertook the translation process following Brislin’s translation model [37, 38]. To start, the English version CFS (referred to as the source CFS) was translated into traditional Chinese by one of the authors of this study, as well as by a bilingual translator working independently. The two translated CFS documents were evaluated and compared with the source CFS by a panel of experts (seven geriatricians and one nurse practitioner) to reach consensus. Afterwards, back translation was independently conducted by two bilingual primary care physicians who had never seen the source CFS. Lastly, three bilingual experts and a panel of geriatric experts were involved in group discussion to compare the two back translations with the source CFS. Minor discrepancies were resolved, and the expert reviewers agreed on the production of the final Chinese version of CFS (CFS-C, Fig. 1).

Fig. 1
figure 1

Chinese version of Clinical Frailty Scale (CFS-C)

Full size image

Assessment of frailty

The Chinese version of Clinical Frailty Scale (CFS-C)

The CFS-C was scored by the same trained research assistant after completing BabyBot and CGA. For the reliability group of 52 participants, CFS-C was scored independently and simultaneously by their geriatricians after reviewing the results of BabyBot and CGA. The results of CFS-C were blinded to each other. For criterion concurrent validity, CFS-C was categorised as robust (CFS-C = 1–2), prefrail (CFS-C = 3–4) and frail (CFS-C = 5–9) [17].

The Fried frailty phenotype

Fried frailty phenotype was assessed by five criteria: exhaustion, weight loss, low activity, weakness, and slowness [2]. We assessed presence of exhaustion, weight loss or low activity by reporting of a “yes” answer to the following items in the self-reported questionnaire: “Feeling tired or fatigue in recent one month”, “weight loss of more than 3 kg or 5% in the previous year” and “low physical activity”, respectively. Weakness was determined by having low grip strength below established cut-off (< 28 kg in men, < 18 kg in women) [39]. Slowness was defined as gait speed < 1 m/s based on the 6-m walk or the participant was not able to walk [39]. From a 5-point scale, participants scored 0 were defined as non-frail, scored 1 or 2 as prefrail, and scored ≥ 3 as frail.

Frailty Index based on a Comprehensive Geriatric Assessment (FI-CGA)

FI-CGA gathered information on ten standard domains from CGA and BabyBot, including cognition, emotion, communication, mobility, balance, bladder function, bowel function, nutrition, activities of daily living and social resources [40,41,42]. For each domain, “0” indicated no problem, “0.5” indicated a minor problem, and “1” indicated a major problem. Scores were summed up into an impairment index, ranging from 0 to 10. For co-morbidity index, CIRS-G was standardized to a range from 0 to 4, representing equivalence of 4 deficits. To construct FI-CGA, the sum of the impairment and co-morbidity index were further divided by 14 into a range from 0 to 1. The detailed scoring criteria were presented in Table 1. According to previous reported cutoffs, participants were categorised as robust (FI-CGA ≤ 0.08), prefrail (0.08 < FI-CGA < 0.25) and frail (FI-CGA ≥ 0.25) [43].

Table 1 Frailty Index based on a Comprehensive Geriatric Assessment (FI-CGA)
Full size table

Statistical analysis

Descriptive analysis was presented as numbers (%) for categorical data, and mean ± standard deviation for continuous variables. Weighted kappa for agreement and Kendall’s tau for correlation were used to assess inter-rater reliability and validity tests. Inter-rater reliability was assessed between physicians and the research assistant. For criterion concurrent validity, CFS-C was compared with both Fried frailty phenotype and FI-CGA. Kendall’s tau was used to assess correlation between CFS-C and other geriatric assessments, including BI, IADL, MNA, MMSE, GDS, CIRS-G, 6-m gait speed, TUG, hand grip and appendicular skeletal muscle mass (ASM). Data was analyzed by using SAS version 9.4 (SAS Institute Inc., Cary, NC). A two-sided p < 0.05 was set as statistically significance.

Results

Characteristics of the study population

Among 221 participants analyzed in the validation study, the mean age was 80.5 ± 7.1 years with a range from 65 to 97 years. Three-fifths (59%) of them were female, 53% had at least ≥ 9 years of education and half were classified as overweight or obese (BMI ≥ 24 kg/m2). For frailty assessment, the classification of CFS-C ranged from 1% (category 1) to 31% (category 4). None of the participants were classified as category 8 or 9. When using Fried frailty phenotype and FI-CGA, 53% and 56% of the participants were classified as frail, respectively. Other characteristics of the study population were presented in Table 2.

Table 2 Baseline characteristics of the study participants (n = 221)
Full size table

Inter-rater reliability

Of 52 participants in the reliability group, the inter-rater reliability revealed moderate agreement (weighted kappa = 0.60) and strong correlation (Kendall’s tau = 0.67). All p values were < 0.0001 (Table 3).

Table 3 Reliability and validation tests of CFS-C
Full size table

Criterion concurrent validity

CFS-C categorisation showed fair agreement (weighted kappa = 0.37) and significant correlation (Kendall’s tau = 0.46) with Fried frailty phenotype. For FI-CGA, strong correlation was achieved between CFS-C and FI-CGA (Kendall’s tau = 0.64). Moderate agreement (weighted kappa = 0.51) and strong correlation (Kendall’s tau = 0.63) were also found between categorisation of CFS-C and FI-CGA. All p values were < 0.0001 (Table 3).

Correlation between CFS-C and other geriatric assessments

CFS-C had significant negative correlation with BI, IADL, 6-m gait speed, hand grip, MMSE and MNA, and significant positive correlation with TUG, CIRS-G, and GDS (Table 4). The correlation between CFS-C and ASM was not significant (Table 4).

Table 4 Correlation between CFS-C and other geriatric assessments
Full size table

Sensitivity analyses

Different prefrail and frail cutoff points (CFS-C = 4–6) were used for frailty categorisation. Criterion concurrent validity between CFS-C categorisation and frailty phenotype was in fair agreement and significant correlation range (weighted kappa = 0.21–0.29, Kendall’s tau = 0.42–0.44). For validity between categorisation of CFS-C and FI-CGA, the results were in range of fair to moderate agreement and strong correlation (weighted kappa = 0.32–0.43, Kendall’s tau = 0.57–0.63). All p values were < 0.0001 [see Additional file 2].

Discussion

The Chinese version of CFS demonstrated a satisfactory validity and inter-rater reliability for frailty evaluation in Chinese older adults. It was also significantly correlated with various domains of CGA, including function, comorbidity, physical performance, nutrition, cognition and depression, indicating CFS to be a global and synthesis assessment of frailty. Development of a valid CFS-C promotes cross-cultural research of frailty in different populations.

In the 7-point CFS study reported by Rockwood and colleagues, CFS showed high correlation with FI [15]. Meanwhile, in our previous 7-point CFS Chinese version validation study, this tool showed significant agreement and correlation with frailty phenotype [27]. In our current study, we compared the 9-point CFS-C with both FI and frailty phenotype, showing that while there was a significant correlation with both, the correlation was higher with FI. The differential extent of agreement and correlation may reflect distinct concepts between frailty phenotype and FI [14]. Frailty phenotype defines frailty as specific components which constitute energetics and reserve dysregulation [2]. In contrast, FI emphasizes less on specific physical factors and focuses more on accumulation of health deficits [44].

CFS was evaluated as a summarized score after a comprehensive geriatric assessment. The content of CFS gathered information from several domains, including functional disability, comorbidity, cognition, physical activities and self-rated health. Our results of significant correlation between CFS-C and various geriatric conditions were in accordance with those elements and previous studies [15, 17, 27, 45]. Among them, BI and IADL showed the strongest correlation with CFS-C, as function disabilities are important decision points in the CFS classification tree [46]. In addition, we found CFS-C showed significant correlation with grip strength and walking speed, both of which are components of sarcopenia and frailty phenotype. However, no significant correlation was found with muscle mass, the core diagnostic component of sarcopenia. This finding was consistent with a previous study that low muscle mass was more prevalent in patients with sarcopenia than with frailty [47].

Previous studies used CFS of 3 to 6 as the frailty cut-off point with a scale of 5 being the most widely used [17]. In addition, few studies explored the cut-off point for prefrail categorisation for CFS. In sensitivity analyses, we used different cut-off points for prefrail and frailty categorisations. Higher agreement and correlation were achieved when using CFS-C of 3–4 as prefrail categorisation and CFS-C >  = 5 as frailty cut-off point in our study.

CFS was recently updated to version 2.0 [48]. Our team subsequently translated CFS 2.0 into Chinese by the same process as had been used for CFS 1.2 [see Additional file 3]. In agreement with previous study, we found minor differences between the two versions which, in the end, did not bring significant change in grading frailty [49]. Therefore, our results may still apply to CFS 2.0.

Our study has several strengths. First, we followed the standard translation model to develop CFS-C in order to minimize bias. Second, two main frailty assessment instruments, frailty phenotype and FI, were set as references for criterion concurrent validity. Therefore, our design was more appropriate than using only one tool or other surrogates of frailty as reference to measure validity. Third, we used BabyBot vital data recording system to provide user-friendly service and include detailed assessments to compute FI-CGA.

Our study has some limitations. First, uneven distribution of CFS with low percentage of CFS category 1 and lack of category 8–9 limits the external validity, which may partially be a result of our enrollment criteria. However, our results correspond to the characteristics of patients from geriatric clinics, being more complex and having more geriatric syndromes than general older populations. In Taiwan, elders who are categorised as very severely frail or terminally ill (CFS = 8–9) may receive home care, hospice or more frequent inpatient services. Second, the single-center and clinic-based design also limit the generalizability of our results. Validation of CFS-C in other settings will be needed to enhance external validity. Further analysis of our longitudinal cohort to explore predictive validity of CFS-C with different outcomes such as falls, hospitalization and mortality is also warranted.

Conclusions

In conclusion, the Chinese version of CFS is a valid tool for frailty assessment in Chinese older adults. Development of CFS-C enhanced consistency and accuracy of frailty assessment both in research and clinical practice.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

CFS:

Clinical Frailty Scale

CFS-C:

Chinese version of CFS

FI-CGA:

Frailty Index based on Comprehensive Geriatric Assessment

FI:

Frailty Index

CSHA:

Canadian Study of Health and Aging

COVID-19:

Coronavirus disease 2019

CGA:

Comprehensive geriatric assessment

TUG:

Timed-up and go

MMSE:

Mini-Mental State Examination

GDS-15:

Geriatric Depression Scale-15

MNA:

Mini-Nutritional Assessment

BI:

Barthel Index

IADL:

Instrumental Activities of Daily Living

CIRS-G:

Cumulative Illness Rating Scale for Geriatrics

ASM:

Appendicular skeletal muscle mass

BMI:

Body Mass Index

References

  1. Collard RM, Boter H, Schoevers RA, Oude Voshaar RC. Prevalence of frailty in community-dwelling older persons: a systematic review. J Am Geriatr Soc. 2012;60(8):1487–92.

    Article  PubMed  Google Scholar 

  2. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146–56.

    Article  CAS  PubMed  Google Scholar 

  3. Shamliyan T, Talley KM, Ramakrishnan R, Kane RL. Association of frailty with survival: a systematic literature review. Ageing Res Rev. 2013;12(2):719–36.

    Article  PubMed  Google Scholar 

  4. Kojima G. Frailty as a predictor of future falls among community-dwelling older people: a systematic review and meta-analysis. J Am Med Dir Assoc. 2015;16(12):1027–33.

    Article  PubMed  Google Scholar 

  5. Ensrud KE, Ewing SK, Taylor BC, Fink HA, Cawthon PM, Stone KL, et al. Comparison of 2 frailty indexes for prediction of falls, disability, fractures, and death in older women. Arch Intern Med. 2008;168(4):382–9.

    Article  PubMed  Google Scholar 

  6. Kojima G. Frailty as a predictor of disabilities among community-dwelling older people: a systematic review and meta-analysis. Disabil Rehabil. 2017;39(19):1897–908.

    Article  PubMed  Google Scholar 

  7. Kojima G. Frailty as a predictor of nursing home placement among community-dwelling older adults: a systematic review and meta-analysis. J Geriatr Phys Ther. 2018;41(1):42–8.

    Article  PubMed  Google Scholar 

  8. Kojima G. Frailty as a predictor of hospitalisation among community-dwelling older people: a systematic review and meta-analysis. J Epidemiol Community Health. 2016;70(7):722–9.

    Article  PubMed  Google Scholar 

  9. Schuurmans H, Steverink N, Lindenberg S, Frieswijk N, Slaets JPJ. Old or frail: what tells us more? J Gerontol A Biol Sci Med Sci. 2004;59(9):M962–5.

    Article  PubMed  Google Scholar 

  10. Lang PO, Michel JP, Zekry D. Frailty syndrome: a transitional state in a dynamic process. Gerontology. 2009;55(5):539–49.

    Article  PubMed  Google Scholar 

  11. Hoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP. Frailty: implications for clinical practice and public health. Lancet. 2019;394(10206):1365–75.

    Article  PubMed  Google Scholar 

  12. Dent E, Lien C, Lim WS, Wong WC, Wong CH, Ng TP, et al. The Asia-Pacific clinical practice guidelines for the management of frailty. J Am Med Dir Assoc. 2017;18(7):564–75.

    Article  PubMed  Google Scholar 

  13. Theou O, Brothers TD, Mitnitski A, Rockwood K. Operationalization of frailty using eight commonly used scales and comparison of their ability to predict all-cause mortality. J Am Geriatr Soc. 2013;61(9):1537–51.

    Article  PubMed  Google Scholar 

  14. Cesari M, Gambassi G, van Kan GA, Vellas B. The frailty phenotype and the frailty index: different instruments for different purposes. Age Ageing. 2014;43(1):10–2.

    Article  PubMed  Google Scholar 

  15. Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005;173(5):489–95.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Flaatten H, De Lange DW, Morandi A, Andersen FH, Artigas A, Bertolini G, et al. The impact of frailty on ICU and 30-day mortality and the level of care in very elderly patients (≥ 80 years). Intensive Care Med. 2017;43(12):1820–8.

    Article  PubMed  Google Scholar 

  17. Church S, Rogers E, Rockwood K, Theou O. A scoping review of the clinical frailty scale. BMC Geriatr. 2020;20(1):393.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Pranata R, Henrina J, Lim MA, Lawrensia S, Yonas E, Vania R, et al. Clinical frailty scale and mortality in COVID-19: a systematic review and dose-response meta-analysis. Arch Gerontol Geriatr. 2021;93: 104324.

    Article  CAS  PubMed  Google Scholar 

  19. The National Institute for Health and Care Excellence (NICE), London. COVID-19 rapid guideline: Managing COVID-19. https://www.nice.org.uk/guidance/ng191. Accessed 10 Nov 2021.

  20. Ko RE, Moon SM, Kang D, Cho J, Chung CR, Lee Y, et al. Translation and validation of the Korean version of the clinical frailty scale in older patients. BMC Geriatr. 2021;21(1):47.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Rodrigues MK, Nunes Rodrigues I, Vasconcelos Gomes da Silva DJ, de S Pinto JM, Oliveira MF. Clinical frailty scale: translation and cultural adaptation into the Brazilian Portuguese language. J Frailty Aging. 2021;10(1):38–43.

    CAS  PubMed  Google Scholar 

  22. Vrettos I, Voukelatou P, Panayiotou S, Kyvetos A, Kalliakmanis A, Makrilakis K, et al. Validation of the revised 9-scale clinical frailty scale (CFS) in Greek language. BMC Geriatr. 2021;21(1):393.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Abraham P, Courvoisier DS, Annweiler C, Lenoir C, Millien T, Dalmaz F, et al. Validation of the clinical frailty score (CFS) in French language. BMC Geriatr. 2019;19(1):322.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Nissen SK, Fournaise A, Lauridsen JT, Ryg J, Nickel CH, Gudex C, et al. Cross-sectoral inter-rater reliability of the clinical frailty scale - a Danish translation and validation study. BMC Geriatr. 2020;20(1):443.

    Article  PubMed  PubMed Central  Google Scholar 

  25. National Health Insurance Administration, Ministry of Health and Welfare, Taiwan. Post-acute care program (content in Chinese). https://www.nhi.gov.tw/Advanced_Search.aspx?q=post-acute%20care%20frailty. Accessed 1 Dec 2021.

  26. National Health Insurance Administration, Ministry of Health and Welfare, Taiwan. Hospital-based patient-centered integrated outpatient services (content in Chinese). https://www.nhi.gov.tw/Query/query11.aspx?pcode=AY&n=0BD4E98F812CFD86. Accessed 1 Dec 2021.

  27. Chan DC, Tsou HH, Chen CY, Chen CY. Validation of the Chinese-Canadian study of health and aging clinical frailty scale (CSHA-CFS) telephone version. Arch Gerontol Geriatr. 2010;50(3):e74-80.

    Article  PubMed  Google Scholar 

  28. Donner A, Eliasziw M. A goodness-of-fit approach to inference procedures for the kappa statistic: confidence interval construction, significance-testing and sample size estimation. Stat Med. 1992;11(11):1511–9.

    Article  CAS  PubMed  Google Scholar 

  29. Shoukri MM, Asyali MH, Donner A. Sample size requirements for the design of reliability study: review and new results. Stat Methods Med Res. 2004;13(4):251–71.

    Article  Google Scholar 

  30. Bonett DG, Wright TA. Sample size requirements for estimating pearson, kendall and spearman correlations. Psychometrika. 2000;65:23–8.

    Article  Google Scholar 

  31. Guo NW, Liu HC, Wong PF. Chinese version and norms of the mini-mental state examination. Journal of Rehabilitation Medicine Association. 1988;16:52–9.

    Google Scholar 

  32. Liu CY, Lu CH, Yu S, Yang YY. Correlations between scores on Chinese versions of long and short forms of the geriatric depression scale among elderly Chinese. Psychol Rep. 1998;82(1):211–4.

    Article  CAS  PubMed  Google Scholar 

  33. Rubenstein LZ, Harker JO, Salva A, Guigoz Y, Vellas B. Screening for undernutrition in geriatric practice: developing the short-form mini-nutritional assessment (MNA-SF). J Gerontol A Biol Sci Med Sci. 2001;56(6):M366–72.

    Article  CAS  PubMed  Google Scholar 

  34. Mahoney FI, Barthel DW. Functional evaluation: the barthel index. Md State Med J. 1965;14:61–5.

    CAS  PubMed  Google Scholar 

  35. Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 1969;9(3):179–86.

    Article  CAS  PubMed  Google Scholar 

  36. Miller MD, Paradis CF, Houck PR, Mazumdar S, Stack JA, Rifai AH, et al. Rating chronic medical illness burden in geropsychiatric practice and research: application of the cumulative illness rating scale. Psychiatry Res. 1992;41(3):237–48.

    Article  CAS  PubMed  Google Scholar 

  37. Brislin RW. Back-translation for cross-cultural research. J Cross Cult Psychol. 1970;1(3):185–216.

    Article  Google Scholar 

  38. Jones PS, Lee JW, Phillips LR, Zhang XE, Jaceldo KB. An adaptation of Brislin’s translation model for cross-cultural research. Nurs Res. 2001;50(5):300–4.

  39. Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, et al. asian working group for sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J Am Med Dir Assoc. 2020;21(3):300-7 e2.

    Article  PubMed  Google Scholar 

  40. Jones DM, Song X, Rockwood K. Operationalizing a frailty index from a standardized comprehensive geriatric assessment. J Am Geriatr Soc. 2004;52(11):1929–33.

    Article  PubMed  Google Scholar 

  41. Jones D, Song X, Mitnitski A, Rockwood K. Evaluation of a frailty index based on a comprehensive geriatric assessment in a population based study of elderly Canadians. Aging Clin Exp Res. 2005;17(6):465–71.

    Article  PubMed  Google Scholar 

  42. Ritt M, Radi KH, Schwarz C, Bollheimer LC, Sieber CC, Gassmann KG. A comparison of frailty indexes based on a comprehensive geriatric assessment for the prediction of adverse outcomes. J Nutr Health Aging. 2016;20(7):760–7.

    Article  CAS  PubMed  Google Scholar 

  43. Song X, Mitnitski A, Rockwood K. Prevalence and 10-year outcomes of frailty in older adults in relation to deficit accumulation. J Am Geriatr Soc. 2010;58(4):681–7.

    Article  PubMed  Google Scholar 

  44. Strandberg TE, Pitkälä KH, Tilvis RS. Frailty in older people. European Geriatric Medicine. 2011;2(6):344–55.

    Article  Google Scholar 

  45. Davis DHJ, Rockwood MRH, Mitnitski AB, Rockwood K. Impairments in mobility and balance in relation to frailty. Arch Gerontol Geriatr. 2011;53(1):79–83.

    Article  PubMed  Google Scholar 

  46. Theou O, Pérez-Zepeda MU, van der Valk AM, Searle SD, Howlett SE, Rockwood K. A classification tree to assist with routine scoring of the Clinical Frailty Scale. Age Ageing. 2021;50(4):1406–11.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Reijnierse EM, Trappenburg MC, Blauw GJ, Verlaan S, de van der Schueren MA, Meskers CG, et al. common ground? the concordance of sarcopenia and frailty definitions. J Am Med Dir Assoc. 2016;17(4):3717–12.

    Article  Google Scholar 

  48. Rockwood K, Theou O. Using the clinical frailty scale in allocating scarce health care resources. Canadian geriatrics journal : CGJ. 2020;23(3):210–5.

  49. Fournaise A, Nissen SK, Lauridsen JT, Ryg J, Nickel CH, Gudex C, et al. Translation of the updated clinical frailty scale 2.0 into Danish and implications for cross-sectoral reliability. BMC Geriatr. 2021;21(1):269.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank Professor Rockwood for providing permission for CFS use. We also thank Angela Pei-Zu Wu, You-Chen Mary Lor, Ming-Ming Liu and Jennifer Tsai for translation process.

Funding

Internal Research Grant from Department of Geriatrics and Gerontology, National Taiwan University Hospital.

Author information

Authors and Affiliations

  1. Department of Geriatrics and Gerontology, National Taiwan University Hospital, No.7, Chung Shan South Road, Taipei, 100, Taiwan

    Yi-Chun Chou, Ding-Cheng Derrick Chan, Chiung-Jung Wen, Feng-Ping Lu, Kun-Pei Lin, Meng-Chen Wu & Jen-Hau Chen

  2. Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County, 350, Taiwan

    Hsiao-Hui Tsou

  3. Department of Internal Medicine, College of Medicine, National Taiwan University, No.1, Jen Ai Road Section 1, Taipei, 100, Taiwan

    Ding-Cheng Derrick Chan, Feng-Ping Lu, Kun-Pei Lin, Yung-Ming Chen & Jen-Hau Chen

  4. Department of Family Medicine, College of Medicine, National Taiwan University, No.1, Jen Ai Road Section 1, Taipei, 100, Taiwan

    Chiung-Jung Wen

  5. Department of Neurology, National Taiwan University Hospital, No.7, Chung Shan South Road, Taipei, 100, Taiwan

    Meng-Chen Wu

  6. Superintendent Office, National Taiwan University Hospital Bei-Hu Branch, No. 87, Neijiang Street, Taipei, 108, Taiwan

    Yung-Ming Chen

Authors
  1. Yi-Chun Chou
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  2. Hsiao-Hui Tsou
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  3. Ding-Cheng Derrick Chan
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  4. Chiung-Jung Wen
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  5. Feng-Ping Lu
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  6. Kun-Pei Lin
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  7. Meng-Chen Wu
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  8. Yung-Ming Chen
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  9. Jen-Hau Chen
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Contributions

Y.C.C.: methodology, writing-original draft preparation. H.H.T.: sample size calculation, data analysis and interpretation. D.C.C.: methodology, writing-review and editing, supervision. C.J.W.: project administration and investigation. F.P.L., K.P.L., M.C.W.: investigation. Y.M.C.: supervision. J.H.C.: writing-review and editing, investigation, validation, supervision, and conceptualization. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jen-Hau Chen.

Ethics declarations

Ethics approval and consent to participate

All methods were carried out in accordance with relevant guidelines and regulations. The validation study was approved by the Research Ethics Committee at National Taiwan University Hospital (201903110RIND). Written informed consent of the study participants was obtained before enrollment.

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Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1.

Selection of study participants.

Additional file 2.

Sensitivity analyses of different CFS-C categorisations.

Additional file 3.

The Clinical Frailty Scale 2.0 in English (left) and the Chinese translation (right).

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Chou, YC., Tsou, HH., Chan, DC.D. et al. Validation of clinical frailty scale in Chinese translation. BMC Geriatr 22, 604 (2022). https://doi.org/10.1186/s12877-022-03287-x

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  • DOI: https://doi.org/10.1186/s12877-022-03287-x

Keywords

BMC Geriatrics

ISSN: 1471-2318

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