Background
Assisted living (AL) is typically defined as a setting that provides health and personal services within a secure residential environment for older adults not requiring the continuous monitoring and more intensive professional care of a long-term care facility [
1,
2]. It is a growing and in many ways attractive housing option for older persons often described as frail. AL residents are prone to a number of adverse outcomes in the short term. Over a year approximately a sixth will die with a similar proportion moving to a higher level of care [
3,
4]. Acute care hospitalizations also occur frequently among this group of older adults [
4,
5].
There is consensus that the core feature of frailty is a heightened vulnerability to stressors (for a given chronological age and sex), which leads to an increased risk for multiple adverse health-related outcomes [
6]. As a group, AL residents will have relatively higher levels of vulnerability than similarly aged community-dwelling individuals, but within the AL population there will be varying degrees of frailty. Gradients of frailty are found in long-term care populations with higher levels associated with an increased risk of mortality, cognitive decline, and new onset disability [
7]. Identifying AL residents at a higher risk for adverse health outcomes would offer opportunities to both maintain remaining independence and enhance quality of life if this could be linked to effective interventions.
Few studies have directly compared the competing approaches to identifying frailty in more vulnerable older populations. Two frequently used measures are the
frailty index, which is based on a count of accumulated deficits divided by the number of potential deficits [
8], and the
Cardiovascular Health Study (CHS) frailty criteria. With the CHS approach, the determination of frailty requires the presence of three or more characteristics felt to capture what has been termed the phenotype of frailty [
9].
Data arising from the implementation of the interRAI family of assessment instruments have increasingly been used for aging-related research, including recent work on frailty [
10‐
13]. For example, Armstrong and colleagues developed a 50-item frailty index based on items in the home care instrument [
10]. Embedded within each interRAI instrument are various scales. One of them, the Changes in Health, End-stage disease and Signs and Symptoms (CHESS) scale, is a measure of health stability designed to identify individuals at high risk for clinically significant decline [
11]. In nursing home populations, higher CHESS scores are predictive of mortality and hospitalization [
11,
12]. Some have suggested that the CHESS scale is a frailty measure [
11,
13].
The primary objective of this study was to compare the ability of two versions of a frailty index derived from interRAI assessment items and the CHS frailty criteria to accurately predict the occurrence of three outcomes relevant to an AL population (mortality, hospitalization, and transfer to a long-term care facility) over a year. This builds on work previously done by our group in trying to operationalize the assessment of frailty among older residents of AL facilities [
14]. A secondary objective was to compare these frailty measures to the CHESS scale in their ability to predict these three outcomes.
Results
The enrolled cohort was predominantly female (818/1066 or 76.7%) with an average age of 84.9 (standard deviation 7.3) years (see Table
1). Multiple morbidities were common. Most subjects (n = 632, 59.3%) had at least mild cognitive impairment, nearly a fifth (n = 203, 19%) had significant depressive symptoms, and over a third (n = 426, 40%) had limited or greater impairment in the performance of activities of daily living.
Table 1
Baseline characteristics for ACCES – AL Cohort
Age, mean ± SD | 84.9 ± 7.3 | 84.9 ± 7.3 |
Female | 818 (76.7) | 700 (75.5) |
Charlson co-morbidity index, mean ± SD | 1.8 ± 2.0 | 1.8 ± 2.0 |
interRAI-AL co-morbidity count, mean ± SD | 4.65 ± 2.0 | 4.65 ± 1.95 |
Cognitive Performance Scale (CPS) | | |
Intact/borderline intact (score 0–1) | 434 (40.7) | 399 (43.0) |
Mild impairment (score 2) | 336 (31.5) | 305 (32.9) |
Moderate impairment (score 3–4) | 183 (17.2) | 151 (16.3) |
Severe/very severe impairment (score 5–6) | 113 (10.6) | 72 (7.8) |
Significant depressive symptoms (DRS 3+) | 203 (19.0) | 158 (17.0) |
Activities of Daily Living (ADL) Scale | | |
Independent (score 0) | 454 (42.6) | 426 (46.0) |
Supervision required (score 1) | 186 (17.4) | 160 (17.3) |
Limited impairment (score 2) | 126 (11.8) | 106 (11.4) |
Extensive assistance required (score 3–4) | 243 (22.8) | 197 (21.2) |
Dependent (score 5–6) | 57 (5.4) | 38 (4.1) |
Armstrong Index
| | |
Not frail, score <; 0.2 | 99 (9.3) | |
Pre-frail, score ≥ 0.2 and ≤ 0.3 | 410 (38.5) | |
Frail, score > 0.3 | 557 (52.3) | |
Full Frailty Index
| | |
Not frail, score <;0.2 | 344 (32.3) | |
Pre-frail, score ≥ 0.2 and ≤ 0.3 | 416 (39.0) | |
Frail, score > 0.3 | 306 (28.7) | |
CHS Frailty Criteria
| | |
Not frail, score = 0 | | 32 (3.5) |
Pre-frail, score = 1,2 | | 451 (48.7) |
Frail, score = 3+ | | 444 (47.9) |
CHESS Scale
| | |
Low risk of decline, score = 0 | 496 (46.5) | |
Intermediate risk, score = 1 | 312 (29.3) | |
High risk, score = 2+ | 258 (24.2) | |
On the Armstrong Index, 9.3% (n = 99) were categorized as not frail, 38.5% (n = 410) pre-frail, and 52.3% (n = 557) as frail. The Full Frailty Index categorized 32.3% as not frail (n = 344), 39% pre-frail (n = 416), and 28.7% (n = 306) as frail. Using CHS criteria, 3.5% (n = 32) were categorized as not frail, 48.7% (n = 451) pre-frail and 47.9% (n = 444) as frail. On the CHESS scale, nearly half (46.5%, n = 496) were identified as being at a low risk for a serious decline, with 29.3% (n = 312) and 24.2% (n = 258) categorized as intermediate and high risk respectively.
A weighted kappa statistic showed the highest level of agreement between the Armstrong Index and the Full Frailty Index (kappa = 0.41, 95% CI 0.38-0.45) with just over half (51.7%) having identical categorizations as non-frail, pre-frail and frail on both criteria (see Table
2). The poorest agreement was between the CHS frailty measure and the CHESS scale (kappa = 0.11, 95% CI 0.08-0.14) with only 30.4% assigned to equivalent categories (non-frail/low risk, pre-frail/intermediate risk, and frail/high risk).
Table 2
Comparison and assessment of agreement among the frailty measures, based on 3-level risk categorization
Armstrong Index – Full Frailty Index | 51.7 | 0.41 | 0.38-0.45 |
Armstrong Index – CHS Frailty Criteria | 57.5 | 0.29 | 0.24-0.34 |
Armstrong Index – CHESS Scale | 34.4 | 0.15 | 0.12-0.19 |
Full Frailty Index – CHS Frailty Criteria | 38.2 | 0.17 | 0.13-0.20 |
Full Frailty Index – CHESS Scale | 51.7 | 0.36 | 0.31-0.40 |
CHS Frailty Criteria – CHESS Scale | 30.4 | 0.11 | 0.08-0.14 |
Over a year 15.9% (n = 170) of residents died, 39.8% (n = 424) were hospitalized at least once, and 19.1% (n = 204) moved to long-term care (see Table
3). Frail subjects defined by the three approaches as well as those at a high risk for decline on the CHESS scale showed a statistically significant increase in their risk of dying compared to those categorized as either not frail or at low risk for decline. The risk ratios in models adjusted for age, sex, and co-morbidity ranged from 1.74 (95% CI 1.07-2.81) with CHS frailty criteria to 2.35 (95% CI 1.56-3.54) for the Full Frailty Index. Only the Full Frailty Index was associated with a statistically significant increase in the risk of death for pre-frail subjects in our adjusted models (RR 2.00, 95% CI 1.33-3.00).
Table 3
One-year outcomes* associated with frailty measures, Risk Ratios (95% Confidence Intervals)
Armstrong Index
| Unadjusted | Prefrail | 1.23 (0.63-2.44) | 0.93 (0.69-1.24) |
2.25 (1.00-5.08)
|
N = 1,066
| | Frail |
2.27 (1.09-4.32)
| 1.23 (0.94-1.63) |
4.21 (1.91-9.25)
|
| Adjusted2
| Prefrail | 1.17 (0.59-2.29) | 0.91 (0.68-1.22) | 2.21 (0.98-4.99) |
| | Frail |
1.94 (1.02-3.70)
| 1.16 (0.87-1.53) |
4.14 (1.87-9.14)
|
Full Frailty Index
| Unadjusted | Prefrail |
2.22 (1.47-3.34)
|
1.44 (1.19-1.74)
|
1.85 (1.26-2.72)
|
N = 1,066
| | Frail |
2.69 (1.78-4.07)
|
1.33 (1.09-1.63)
|
3.30 (2.30-4.75)
|
| Adjusted2
| Prefrail |
2.00 (1.33-3.00)
|
1.37 (1.13-1.66)
|
1.87 (1.27-2.75)
|
| | Frail |
2.35 (1.56-3.54)
|
1.28 (1.04-1.57)
|
3.30 (2.29-4.76)
|
CHS Criteria
1
| Unadjusted | Prefrail | 1.04 (0.59-1.81) | 1.10 (0.84-1.43) | 1.54 (0.95-2.51) |
N = 927
| | Frail |
2.25 (1.40-3.61)
|
1.60 (1.27-2.02)
|
2.21 (1.41-3.46)
|
| Adjusted2
| Prefrail | 0.88 (0.51-1.54) | 1.06 (0.82-1.38) | 1.49 (0.91-2.43) |
| | Frail |
1.74 (1.07-2.81)
|
1.45 (1.15-1.83)
|
2.17 (1.38-3.41)
|
CHESS Scale
| Unadjusted | Intermediate |
1.48 (1.04-2.09)
|
1.31 (1.10-1.56)
| 1.33 (0.98-1.81) |
N = 1,066
| | High |
2.13 (1.53-2.96)
|
1.32 (1.10-1.58)
|
1.84 (1.38-2.47)
|
| Adjusted2
| Intermediate | 1.38 (0.98-1.94) |
1.27 (1.07-1.51)
| 1.33 (0.98-1.82) |
| | High |
1.87 (1.35-2.59)
|
1.25 (1.05-1.50)
|
1.87 (1.39-2.50)
|
Frail and pre-frail individuals as defined by the Armstrong Index had no statistically significant increase in their risk for hospitalization (please see Table
3). Those meeting CHS criteria for frailty were at greater risk for hospitalization (risk ratio in adjusted model was 1.45, 95% CI 1.15-1.83), but there was no statistically significant increase in the risk ratio for the pre-frail group. Risk ratios for hospitalization were significantly increased for both pre-frail and frail subjects on the Full Frailty Index, but no gradient was observed and confidence intervals for the risk ratios for pre-frail (1.37, 95% CI 1.13-1.66) and frail (1.28, 95% CI 1.04-1.57) subjects overlapped. A similar increased risk for hospitalization was observed for those categorized as intermediate or high risk for decline on the CHESS scale in adjusted analyses.
Residents categorized as frail on all approaches considered and the high risk group for decline on the CHESS scale showed a statistically significant greater risk of transfer to long-term care (please see Table
3). The adjusted risk ratio was strongest for the Armstrong Frailty Index (4.14, 95% CI 1.87-9.14) followed by the Full Frailty Index (3.30, 95% CI 2.29-4.76). Only the Full Frailty Index showed a statistically significant increased risk of long term care admission for pre-frail individuals (1.87, 95% CI 1.27-2.75) in adjusted analyses.
To compare the abilities of the three frailty approaches and the CHESS scale to predict our outcomes of interest, we examined areas under the ROC curve (AUC). As potential confounders we considered sex, age, and co-morbidity index scores (please see Table
4). The AUCs obtained for the frailty measures and the CHESS scale ranged from 0.683-0.701 for mortality, 0.609-0.629 for hospitalization, and 0.602-0.667 for admission to long-term care within a year. For death, the addition of frailty (however operationalized) or CHESS-defined risk for decline significantly improved on the AUC obtained with sex, age, and co-morbidity (p-values less than 0.03 for pair-wise comparisons), though the magnitude of improvement seen was relatively small (0.031-0.049). None of the AUCs with a frailty measure or the CHESS scale differed significantly from each other. As for hospitalization, only the CHS frailty criteria significantly improved on the model based on age, sex, and co-morbidity (AUC 0.629, 95% CI 0.592-0.665 versus AUC 0.598, 95% CI 0.561-0.635, p = 0.003 for the comparison). The magnitude of improvement was relatively small (0.031), and none of the AUCs with the addition of frailty or the CHESS scale differed significantly from each other. For transfer to long-term care, the addition of any of the frailty measures or the CHESS-defined risk of decline improved on the model with sex, age and co-morbidity (p-values less than 0.03 for pair-wise comparisons). The magnitude of improvement seen ranged from 0.048 to 0.113. The AUC for the Full Frailty Index (0.667) differed significantly from the AUCs for the CHESS scale (0.602, p = 0.016) and CHS frailty criteria (0.610, p = 0.003). The difference between the AUCs of the Full Frailty Index and the Armstrong Index (0.638) was of borderline significance (p = 0.087).
Table 4
Area under the ROC curve (95% Confidence Intervals) for models of one-year outcomes, comparing frailty measures
1 | Sex and age | 0.638 (0.592-0.683) | 0.531 (0.495-0.566) | 0.552 (0.510-0.593) |
2 | Sex, age, and co-morbidity | 0.652 (0.607-0.698) | 0.592 (0.558-0.627) | 0.554 (0.512-0.596) |
3 | Sex, age, co-morbidity, and Armstrong Index
| 0.683 (0.639-0.728) | 0.609 (0.575-0.643) | 0.638 (0.598-0.678) |
4 | Sex, age, co-morbidity, and Full Frailty Index
| 0.691 (0.648-0.733) | 0.610 (0.576-0.644) | 0.667 (0.625-0.707)3
|
5 | Sex, age, co-morbidity, and CHS Frailty Criteria
4
| 0.701 (0.655-0.747) | 0.629 (0.592-0.665) | 0.610 (0.564-0.656) |
6 | Sex, age, co-morbidity, and CHESS Scale
| 0.683 (0.640-0.725) | 0.610 (0.576-0.645) | 0.602 (0.558-0.646) |
As a sensitivity analysis, we looked also at two composite outcomes (hospitalizations + deaths and long-term care transfers + deaths) to see if censoring affected our results. As they were essentially unchanged, we have not reported the specific estimates for these composite outcomes.
Discussion
In this population of AL residents, both the presence of frailty defined by any of the three approaches examined and a higher CHESS score were associated with an increased risk of dying or being transferred to a long-term care facility. The magnitude of the ability of these measures to correctly classify subjects (relative to models including only age, sex and co-morbidity) was modest for mortality but better for long-term care placement. The AUCs obtained were in the 0.6 to 0.7 range. This generally indicates low accuracy in correctly differentiating risk [
27], but our objective was not to develop a comprehensive prediction model for these outcomes in our AL population. The current study comparing these three frailty measures and the CHESS score builds directly on our prior work examining frailty in the AL setting [
14].
The different approaches to detecting frailty were more similar than dissimilar with regard to predictive accuracy with a few exceptions. The Full Frailty Index performed significantly better in predicting a move to long-term care and was the only approach that showed higher mortality, hospitalization, and institutionalization risks among those categorized as pre-frail. Only the addition of CHS frailty criteria significantly improved on age, sex, and co-morbidity in predicting hospitalization. While our study did not directly address practicality, acceptability to practitioners, or clinical utility, a number of observations can be made. Both the Armstrong Index and CHS frailty criteria categorized relatively few AL residents as not frail and approximately half as frail. In comparison to the Full Frailty Index and the CHESS scale, they were less successful in creating a more circumscribed group of AL residents for potential interventions. This would be an important consideration when clinical resources are limited and have to be targeted. The Armstrong and Full Frailty Indices (and the CHESS scale) were constructed using data available on the interRAI-AL instrument, one of a suite of similar instruments being implemented across continuing care settings in Canada. While many items were included in the indices, their calculation could be automated. This could make the use of multi-item indices practical from a clinical standpoint. The CHS frailty criteria did not perform significantly better than the frailty indices (other than possibly in predicting hospitalization) and required the use of performance measures, which can be difficult to obtain on AL residents [
14,
28]. The latter issue raises major feasibility concerns with this approach in an AL population. The ability to derive the CHESS scale and two frailty indices from previously collected data highlights the practical utility of these measures while the inclusion of a wider set of potentially relevant domains in operationalizing frailty represents a conceptual strength of the two frailty indices [
6].
As shown in Table
2, the best agreement among the measures was between the Full Frailty and Armstrong indices. Although moderate agreement between these two indices was observed [
29], it is surprising that this was not greater given the degree of commonality in items (see Appendix A) and their similar operational approaches. Interestingly, Rockwood et al. found that random combinations of items making up frailty indices led to little overlap across quartiles [
30]. The lower than expected level of agreement between these two indices may also reflect the inclusion of several Instrumental ADLs (e.g., meal preparation, housework, managing medications) in the Armstrong (but not the Full Frailty) index. As these areas are generally managed or provided for residents by the AL facility (resulting in most residents being assessed as impaired in these areas) they would be considered poor criteria for a frailty measure [
16]. This may also explain the low proportion of residents categorized as not frail by the Armstrong index. Fair agreement was observed between the Full Frailty Index and the CHESS and between the Armstrong Frailty Index and the CHS. The remaining kappa statistics indicated only slight agreement between the measures. Armstrong et al. had previously reported a low correlation between the CHESS and their frailty index (r = 0.35) [
10].
Whether the CHESS scale is a frailty measure cannot be answered by our study. Compared with the other measures examined, the CHESS scale generally performed similarly in predicting our outcomes of interest, although associations were weaker for long term care placement. This was most evident for comparisons between CHESS and the two frailty indices. In settings where interRAI instruments are widely implemented, the CHESS scale offers the advantage of being relatively simple and easy to assess across care settings.
The reasons underlying the relatively weaker associations between frailty and the outcome of hospitalization are not entirely clear. When added to the model with age and sex, the Charlson co-morbidity index score improved the AUC for hospitalization (see Table
4) and has been previously shown to predict hospitalization (and mortality) in older institutionalized residents [
31]. The further addition of frailty and/or health instability may offer relatively less predictive gain for this outcome after the addition of a co-morbidity index. Another possibility is competing risk though we found no evidence of this in the analyses performed for this study. In earlier work we failed to observe a statistically significant increase in the risk of hospitalization among frail men that we felt was due to their high mortality rate during follow-up [
14]. Other considerations would include the inherent difficulty of predicting hospitalizations especially for catastrophic (e.g., fall with a hip fracture) changes in health, the modifying effects of factors such as advance planning and the resources available within AL facilities [
32], and the obscuring influence of variability in local hospitalization rates [
33].
There are some limitations in our study that would raise concerns about the generalizability of our findings to the entire AL population. First, over four hundred eligible residents did not enroll in the study [
14]. While the age and sex distribution of those not enrolled was comparable to our enrolled cohort, we do not have other information on them. Second, we restricted eligibility to residents of publicly-subsidized AL facilities in Alberta. Our findings may not apply to residents in private AL or AL-type facilities in other jurisdictions that may have different admission criteria, staffing, and institutional policies. Hospitalizations were determined using provincial data, and we may have missed the rare event that occurred outside Alberta. Further research examining these and other relevant outcomes (e.g., functional decline) over longer periods of time is also warranted. Nearly 40% of AL participants could not complete the CHS frailty assessment as originally intended. By using responses to observed items from the interRAI-AL assessment instrument we were able to reduce the proportion with missing data to 15%. As noted previously this raises questions about the feasibility of this particular approach in an AL population.
Appendix A
The following items were included in the cumulative deficit index meant to replicate that created by Armstrong et al. [
10], (Table
5). Three items which were not exactly replicable using the interRAI-AL were, ”severe malnutrition”, “problem chewing” and “head trauma”. Also “Alzheimer” and “Dementia other than Alzheimer’s” were combined into one category. “Hip fracture”, “other fractures”, and “osteoporosis” were combined into one category. Also, “Feeling of sadness” and “Sad, pained worried facial expressions” were combined into one category. This reduced the index from 50 items to 43 items.
The presence of each condition added “1” to the person’s Index score (unless otherwise indicated).
The following index was created using the criteria based on Searle et al. [
16] using information from the interRAI-AL, (Table
6). The presence of each condition added “1” to the person’s index score (unless otherwise indicated).
The following details the frailty criteria from the Cardiovascular Health Study (CHS) [9], (Table
7).
Table 5
Armstrong frailty index (43 items)
Mood
|
Persistent anger |
Unrealistic fears |
Repetitive health complaints |
Sad, pained, worried facial expressions |
Withdrawal from activities of interest |
Reduced social interactions |
Communication
|
Moderate/severe vision problems |
Functional status
|
Limited help (0.5), extensive help (1) with meal preparation |
Limited help (0.5), extensive help (1) with ordinary housework |
Limited help (0.5), extensive help (1) with managing finances |
Limited help (0.5), extensive help (1) with managing meds |
Limited help (0.5), extensive help (1) with stair climbing |
Limited help (0.5), extensive help (1) with shopping |
Limited help (0.5), extensive help (1) with bathing |
Limited help (0.5), extensive help (1) with personal hygiene |
Limited help (0.5), extensive help (1) with dressing upper body |
Limited help (0.5), extensive help (1) with dressing lower body |
Limited help (0.5), extensive help (1) with locomotion |
Limited help (0.5), extensive help (1) with transferring |
Minimal help (0.5), extensive help (1) with toilet use |
Minimal help (0.5), extensive help (1) with eating |
Incontinence
|
Some (0.5) or daily (1) bladder incontinence |
Disease diagnosis
|
Hip fracture, other fractures, osteoporosis |
Arthritis |
Alzheimer’s disease/dementia |
Hemiplegia |
Multiple sclerosis |
Parkinson's disease |
Stroke or CVA |
Hypertension |
Coronary heart disease |
Congestive heart failure |
Chronic Obstructive Pulmonary Disease (COPD)/Emphysema/Asthma |
Diabetes |
Renal disease |
Peripheral vascular disease |
Cardiac arrhythmias |
Thyroid disease |
Health conditions
|
Balance - unsteady gait |
Poor self-reported health |
Unstable health |
Nutrition/Medications
|
BMI 30–40 (0.5), BMI > 40 (1) |
Weight loss 5% or more in last 30 days or 10% in last 180 days |
Table 6
Full frailty index (83 items)
Psychosocial well-being
|
Not close to someone in facility |
No strong supportive relationship with family |
Infrequent participation in longstanding social activities |
Infrequent visits from friends/family |
Infrequent interaction with friends/family |
Mood
|
Makes negative statements |
Persistent anger |
Unrealistic fears |
Repetitive health complaints |
Repetitive anxiety |
Sad, pained, worried facial expressions |
Crying, tearfulness |
Withdrawal from activities of interest |
Reduced social interactions |
Lack of pleasure in life |
Cognition
|
Minimally impaired (0.5) or moderate/severely impaired (1) decision-making skills |
Short-term memory problems |
Procedural memory problems |
Situational memory problems |
Easily distracted |
Episodes of disorganized speech |
Declined decision-making last 90 days |
Communication
|
At least some difficulty to make self understood |
At least some difficulty understanding |
Moderate/severe hearing problems |
Moderate/severe vision problems |
Functional status
|
Limited help (0.5), extensive help (1) with phone use |
Limited help (0.5), extensive help (1) with stair climbing |
Limited help (0.5), extensive help (1) with shopping |
Limited help (0.5), extensive help (1) with bathing |
Limited help (0.5), extensive help (1) with personal hygiene |
Limited help (0.5), extensive help (1) with dressing upper body |
Limited help (0.5), extensive help (1) with dressing lower body |
Limited help (0.5), extensive help (1) with walking |
Limited help (0.5), extensive help (1) with locomotion |
Limited help (0.5), extensive help (1) with transferring |
Minimal help (0.5), extensive help (1) with toilet use |
Minimal help (0.5), extensive help (1) with bed mobility |
Minimal help (0.5), extensive help (1) with eating |
Less than 1 hour of physical activity in last 3 days |
Did not go out within a 3 day period |
Declined in ADL over last 90 days |
Incontinence
|
Some (0.5), daily (1) bladder incontinence |
Some (0.5), daily (1) bowel incontinence |
Disease diagnosis
|
Hip fracture, other fractures, osteoporosis |
Arthritis |
Alzheimer’s disease/dementia |
Hemiplegia |
Multiple sclerosis |
Paraplegia/quadriplegia |
Parkinson's disease |
Stroke or CVA |
Hypertension |
Coronary heart disease |
Congestive heart failure |
COPD/Emphysema/Asthma |
Cancer |
Diabetes |
Renal disease |
Peripheral vascular disease |
Cardiac arrhythmias |
Thyroid disease |
Health conditions
|
At least 1 fall in last 30 days |
Balance - turning around |
Balance - dizziness |
Balance - unsteady gait |
Chest pain |
Abnormal thought process |
Delusions |
Hallucinations |
Aphasia |
Vomiting |
Non-restful sleep/insomnia |
Too much sleep |
Peripheral edema |
Shortness of breath |
Fatigue - cannot complete day-to-day activities |
Pain present |
Poor self-reported health |
Nutrition/Medications
|
BMI 30–40 (0.5), BMI > 40 (1) |
Weight loss 5% or more in last 30 days or 10% in last 180 days |
Ten or more medications |
Allergy to any drug |
Table 7
Cardiovascular Health Study Frailty Criteria1
Slow gait
| Determined by taking the better of two timed 3-meter walks. | ≥7 seconds2, men ≤ 173 cm ≥ 7 seconds, women ≤ 159 cm ≥ 6 seconds, men > 173 cm ≥ 6 seconds, women > 159 cm |
Muscle weakness
| Average of three grip strength readings using a handheld dynamometer.3
| BMI-specific thresholds: ≤ 29-32 kg, men ≤ 17-21 kg, women |
Low physical activity
| Reported minutes over two weeks per activity type - from the interRAI-AL “Exercise or Leisure Activities” 4
| Activities were mapped to Minnesota Leisure Time Activity Questionnaire [ 34]. Kcals per week calculated based on the intensity codes: < 383 Kcals/week, men < 270 Kcals/week, women |
Unintentional weight loss
| Answer to question: “In the past year have you lost more than 10 pounds unintentionally” 5
| Response of “Yes” |
Exhaustion
| Answers to 3 questions: “In the past month, on average, have you been: 1) Feeling unusually tired during the day?; 2) Feeling unusually weak?; and/or, 3) Feeling an unusually low energy level?”6
| Response of Yes to any of the 3 questions |
Appendix B
CHESS Scale
CHESS stands for Changes in Health, End-stage disease and Symptoms and Signs of medical problems [
11]. It provides a measure of instability in health (which may be a consequence of frailty) and is believed to be a marker of imminent decline in health. The score is based on the following:
Symptoms: A summary count is first derived from the following symptoms (coded as 0 = no symptoms present; 1 = 1 symptom present; 2 = 2+ symptoms present).
To this summary count variable, 1 point is added for “worsening of decision making over previous 90 days”, 1 point for “decline in activities of daily living over previous 90 days”, and 1 point for “end-stage disease”.
The range of values for the CHESS is 0 to 5, where 0 represents stability, and 5 represents highly unstable health.
*Note that two items from the original CHESS, were unavailable on the interRAI-AL form, and were not included in the calculation of symptoms:
Acknowledgements
Special thanks are given to Deanna Wanless, Anna Charlton, Cheri Komar (Study Coordinators), Dr. Misha Eliasziw (Study Advisor), our research staff, and the facilities, residents and their family members who participated in ACCES.
Funding
This study was funded by the Alberta Heritage Foundation for Medical Research (#200400893), the Canadian Institutes of Health Research (CIHR) (MOP81216) and CIHR-Institute of Aging Northern and Rural Health Research Initiative (HAS-63179).
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
DH, EF and CM were responsible for the conception and design of the frailty study and for the initial interpretation of the data. DH drafted the initial manuscript and EF was responsible for the main analysis. LS and CM were responsible for the conception and design of the parent ACCES study, directed the acquisition of data and made substantial contributions to the analysis and interpretation of the data. SP, HS and DR provided significant input regarding the original frailty study design and clinical interpretation of the data. All authors were involved in revising the manuscript critically for important intellectual content and have given final approval of the version to be published.