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Record W2103940618 · doi:10.1093/ageing/afl041

Validity and reliability of the Edmonton Frail Scale

2006· article· en· W2103940618 on OpenAlexafffundabout
Darryl Rolfson, Sumit R. Majumdar, Ross T. Tsuyuki, Adeel Tahir, Kenneth Rockwood

Bibliographic record

VenueAge and Ageing · 2006
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsDalhousie UniversityUniversity of Alberta
FundersAlberta Heritage Foundation for Medical ResearchCanadian Institutes of Health ResearchDalhousie UniversityFondation pour la Recherche MédicaleUniversity of Alberta
KeywordsMedicineReliability (semiconductor)Scale (ratio)Reliability engineeringGerontologyCartography

Abstract

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SIR—Age is broadly recognised to confer a risk for adverse health outcomes, but it is an insensitive and non-specific measure for use in individual decision-making. Frailty has been emerging to take its rightful place as a better measure for over a decade [1]. Despite general consensus that the concept of frailty is clinically useful, the lack of agreement on its definition and the challenge of its measurement by front-line health providers mean that frailty remains only ‘heir apparent’ to chronological age as a criterion to select older persons at risk [2]. Frailty is multidimensional, heterogeneous and unstable, thus distinguishing it from disability or ageing alone [3]. Rather, it is widely conceived of as a state of vulnerability. Frailty is measured in many ways, including ‘rules based’ instruments, summative impairment lists and algorithms derived from clinical judgement [4-6]. However much these tools might have advanced research in frailty, most are impractical for bedside screening by front-line providers because they require the multidimensional clinical data that constitute a comprehensive geriatric assessment (CGA) and/or require special training. Often, neither is available to the primary care providers who care for these patients. Furthermore, previously validated frailty assessments are time-consuming, making them impractical in more volume-driven settings, such as a primary care physician’s office. We therefore developed and tested a brief and user-friendly screening interview for frailty in seniors commonly encountered by geriatricians in both the inpatient and outpatient settings. Our objective was to assess the validity and reliability of the Edmonton Frail Scale (EFS) in a sample referred for CGA (Table 1). All patients aged 65+ years were approached for informed consent; exclusions were only for communication barriers (deafness, blindness or the need for translation), problems with manual dexterity or previous enrolment in our study. Patients were a referral population for CGA seen during July 2000 in acute care wards, rehabilitation units, day hospitals and outpatient clinics in Edmonton, Alberta, a major Canadian metropolitan centre (population one million). The Edmonton Frail Scale A lay research assistant who had no formal medical training collected demographic and medical data and then administered the EFS [7]. The EFS samples 10 domains; the maximum score is 17 and represents the highest level of frailty. Two domains are tested using performance-based items: the Clock test [8] for cognitive impairment and the ‘Timed Get Up and Go’ [9] for balance and mobility. The other domains are mood, functional independence, medication use, social support, nutrition, health attitudes, continence, burden of medical illness and quality of life (all standard historical items in geriatric assessment). All patients had a minimum of 1 h specialist CGA, which included a personal and informant history, a physical examination, a functional performance assessment, a mental status examination and a formulation. The CGA was completed independent of and blinded to the EFS scoring. Following each CGA, the specialist completed a questionnaire (developed by the study investigators, with its content validity tested by a panel of geriatricians) summarised as a Geriatrician’s Clinical Impression of Frailty (GCIF) [10]. The GCIF (Appendix: available online at http://ageing.oxfordjournals.org) included nine items for pre-morbid geriatric syndromes that threaten future independence and six items about acute atypical disease presentations. Finally, using each of the four different definitions of frailty, subjects were rated on a scale from 0 (not frail) to 5 (maximal frailty). These included definitions based on physical frailty (e.g. muscle wasting and weakness), physiological frailty (organ system-based vulnerability), frailty as disability (impairment in functional independence) and dynamic frailty (functional instability inclusive of social reserve). The GCIF thus has a score ranging from 0 (none) to 35 (maximal) frailty, which was correlated with the EFS score. In a randomly selected subset, the EFS was re-administered within 24 h by a geriatric assessment nurse blinded to the first score and to the CGA. For additional convergent construct validity, other subsets of participants were similarly requested to complete the Mini-Mental State Examination (MMSE) [11] and the Barthel Index [12]. To estimate the sample size for construct validity using a Pearson correlation, we reckoned a modest correlation of 0.30 and an expected correlation of 0.70, with α = 0.05 and β = 0.90 showing a need for 60 subjects [13]. This was doubled to allow for multiple testing. To test inter-rater reliability of the EFS using the kappa coefficient (κ) and assuming that excellent agreement was indicated by a value of ≥0.80, we calculated a requirement of 23 subjects [12]. The EFS score was also correlated with age, gender, number of medications, MMSE and Barthel Index. We used t-tests to compare mean scores by residence and consultation site. Crohnbach’s α was employed to test internal reliability. The health research ethics board of the University of Alberta approved the project. During the 8-week period of enrolment, 364 individuals were considered to be eligible. Of these, 163 were excluded because of the unavailability of the patient or the assessor before the geriatric assessment. No systematic information was available on non-responders. Another 34 declined participation, leaving 158 participants (43% of those eligible). The patients’ mean age was 80.4 years (SD = 6.8); 53% were women and 59% were unmarried (including widowed). The average number of medications was 5.4 (SD = 3.3). Most patients came from the community (43% home without help and 42% home with help compared with 14% in assisted living and 1% in a nursing home). Assessments were conducted in both outpatient (56% in specialty clinics, 4% in day hospitals) and inpatient settings (22% acute care units, 18% geriatric rehabilitation units). The EFS was normally distributed (mean score 7.6, SD = 3.0, range = 0–16), as was the GCIF (mean score of 15.7, SD = 6.95, range = 0–32). The EFS correlated significantly with the GCIF, age and medication count but not with sex (Table 2). Inpatients had higher scores than outpatients (9.9 ± 2.5 versus 6.2 ± 2.5; t = –8.9, P<0.001) and those who lived with assistance had higher scores than those who lived independently (8.1 SD = 2.8 versus 7.0 SD = 2.8; t = –2.29, P = 0.02). In the construct validation sub-samples, the correlation with the Barthel Index was statistically significant (r = –0.58, P = 0.006, n = 21), but the correlation with the MMSE was not (r = –0.05, P = 0.801, n = 30). Correlation between the Edmonton Frail Scale and patient characteristics The EFS showed good inter-rater reliability (κ = 0.77, P = 0.0001, n = 18). The internal consistency of the EFS using Crohnbach’s α was 0.62. The EFS required <5 min to administer and was reported to be acceptable to the operators and study participants. In this community-based referral sample, the EFS was a valid measure of frailty compared to the clinical impression of geriatric specialists after their more comprehensive assessment. The EFS also had good construct validity, good reliability and acceptable internal consistency. A unique characteristic of the EFS as a clinical frailty instrument is its inclusion of the domain of social support, suggesting an endorsement of the dynamic model of frailty [1]. Of note, the EFS was validated in the hands of non-specialists who had no formal training in geriatric care. Thus, the EFS has the potential as a practical and clinically meaningful measure of frailty in a variety of settings. Although the GCIF has a good face validity and includes most current concepts of frailty (physical, physiological, functional and dynamic), it is no ‘gold standard’. Still, it reflects the blinded, systematic judgement of geriatric specialists about individual patients after completing a CGA and thus captures the clinical essence of frailty. Ours was a select population referred for CGA. Caution should therefore be exercised in generalising these findings to either an unscreened community population or, conversely, a more narrow research population in whom social support, health attitudes, mood, cognition and functional dependence are potentially much less relevant to the frailty phenotype. Moreover, the identification of someone as frail needs to be used to alert health care providers to their special needs, not to consign them to inferior care. Indeed, interest is building around the frailty phenotype model [14] defined as any three of weight loss, self-reported exhaustion, low activity levels, low walking speed and low grip strength. The precision and clarity in definition of this tool is attractive, and it appears to correlate with specific physiological alterations, particularly enhanced inflammation and coagulation [15], as have other measures [16, 17]. Still, this may be less useful in the care of everyday patients in whom health-related vulnerabilities cannot be so easily separated from cognition, mood and social support [18]. The EFS is shorter than another recent clinical proposal [18] that focused on change; further studies are needed to test the responsiveness of the EFS. Another new judgement-based measure, the Canadian Study of Health and Aging (CSHA) Clinical Frailty Scale [4], is short but was validated only after a CGA was carried out. Similarly, the FI-CGA [19] still requires a CGA, which also limits its potential for routine application. Still, each of these CGA-dependent tools correlates highly with a validated standard [20], and both predict death and institutionalisation in tightly graded patterns. In summary, the EFS appears to be valid, reliable and feasible for routine use by non-geriatricians. The need for cross-validation in other settings and evaluation of responsiveness is motivating further enquiries by our group. What is known: Frailty is broadly used and measurable, but the need for a brief tool that can be used by non-specialist geriatrician remains. What this study adds: The EFS is a brief, valid and reliable tool that can be completed by people without special training in geriatric medicine. The authors each declare no conflict of interest. This study was supported by the Division of Geriatric Medicine, University of Alberta. Funding for additional analyses came from CIHR grant MOP 62823. K.R. receives career support from the Canadian Institutes of Health Research through a Career Investigator Award and from the Dalhousie University Medical Research Foundation as the Kathryn Allen Weldon Professor of Alzheimer Research. S.R.M. receives career support from the Alberta Heritage Foundation for Medical Research (AHFMR) and from the Canadian Institutes of Health Research (New Investigator). R.T.T. is supported by the Merck Frosst Chair in Patient Health Management at the University of Alberta. The authors are grateful to Yan Wang, MSc of the Geriatric Medicine Research Unit, Dalhousie University for assistance with the analysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.043
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.247
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1,621
Published2006
Admission routes3
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