Neurol Sci DOI 10.1007/s10072-015-2330-y
ORIGINAL ARTICLE
Normative data for the ACE-R in an Italian population sample Martina Pigliautile1 • Francesca Chiesi2 • Sonia Rossetti3 • Manuela Conestabile della Staffa1 • Monica Ricci4 • Stefano Federici5 • Dora Chiloiro3 • Caterina Primi2 • Patrizia Mecocci1
Received: 17 April 2015 / Accepted: 6 July 2015 Ó Springer-Verlag Italia 2015
Abstract The Addenbrooke’s Cognitive Examination Revised (ACE-R) is a brief cognitive screening instrument also proposed to detect mild cognitive impairment, a highrisk condition for Alzheimer’s disease and other forms of dementia. In this study, we report normative data on the ACE-R-Italian version, collected on a sample of 264 Italian healthy subjects aging between 60 and 93 years, and with a formal education from 1 to 19 years. The global normal cognition was established in accordance with the Italian version of the Mini–Mental State Examination score and with exclusion criteria derived by a consensus process. Linear regression analysis was performed to evaluate the effect of age, gender, and education on the ACE-R total performance score. We provide correction grids to adjust raw scores and equivalent scores with cut-off value to allow comparison between ACE-R performance and others neuropsychological test scores that can be administered to the same subject.
& Martina Pigliautile
[email protected] 1
Section of Gerontology and Geriatrics, Department of Medicine, University of Perugia, Perugia, Italy
2
Department of Neuroscience, Psychology, Drug Research, and Child’s Health (NEUROFARBA)-Section of Psychology, University of Florence, Florence, Italy
3
Unit of Clinical Psychology, Azienda Sanitaria Locale, Taranto, Italy
4
ARC Centre of Excellence in Cognition and its Disorders, Macquarie University, Sydney, Australia
5
Social and Human Sciences and Education, Department of Philosophy, University of Perugia, Perugia, Italy
Keywords Dementia
ACE-R Normative data Screening
Introduction The revised version of the Addenbrooke’s Cognitive Examination (ACE-R) [1] is a screening test that takes between 12 and 20 min to be administered and with a score specifically developed to improve sensibility and specificity of the Addenbrooke’s Cognitive Examination (ACE) [2] for detecting dementia. It consists by a 100 points scale assessing attention, memory, fluency, language, and visuospatial cognitive domains. The ACE-R has been proposed to detect mild cognitive impairment from cognitive changes of normal aging [1], to diagnose of different forms of dementia [3] and to differentiate diagnosis early dementias versus affective disorders [4]. The ACE-R has been validated into different languages [5], and a meta-analysis demonstrated its superior diagnostic accuracy respect to the Mini Mental State Examination (MMSE) [6]. The use of the ACE-R has been considered as a potentially useful tool by the Scottish Intercollegiate Guidelines Network [7], and it is recommended in both modest (general hospital and primary care) and high prevalence settings (memory clinics) [6]. In 2006, Pigliautile et al. [8] translated the test, instructions for administration and scoring into Italian. The Italian ACE-R has high sensitivity and specificity and it is indicated as a useful tool to detect mild dementia in the elderly population (young-old and old-old population) but to date Italian normative data are still lacking. The aim of the present study was to collect normative data for the ACE-R Italian version in a sample of
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cognitively healthy Italian subjects ranging from 60 to 93 years of age. Effects of age, gender, and education on performance were considered, and the raw scores were transformed into equivalent score (ES) so that scores achieved on the ACER can be easily comparable with those obtained in other tests, allowing, therefore, a better characterisation of patient profile.
Methods Subjects The study included 264 (147 woman, 117 men) cognitively healthy subjects aged 60–93 years (mean 72.91 ± 7.96). Years of schooling ranged from 1 to 19 years (mean 9.74 ± 4.80). Subjects were recruited from recreation centers for the elderly in Perugia, from community or among friends/spouses/relatives of subjects attending Section of Gerontology and Geriatrics, Department of Medicine, University of Perugia and Unit of Clinical Psychology, Azienda Sanitaria Locale of Taranto. Subjects were recruited on a voluntary basis from September 2011 to June 2014. The study was approved by the local ethics committees and performed in accordance with the Helsinki Declaration. Inclusion criteria were (a) a MMSE [9] score C24; (b) no history of traumatic brain injury; (c) no history of stroke; (d) no history of neurological disorders; (e) no history of psychiatric disorders; and (f) no clinical evidence or history of depression. The inclusion criteria (b)–(d) were assessed by a brief interview. In order to avoid selecting a sample of ‘‘supernormal’’ subjects, we did not exclude individuals with pharmacological well-compensated hypertension, diabetes, and anxious/depressive symptoms, and corrected sensory deficits were allowed. In order to ensure the normal cognitive functioning in the oldest old (subjects aging C80 years), a detailed neuropsychological assessment was carried out using an extensive battery of cognitive tests comprehensive of test with high ecological validity (see below). In this subsample, minimal supervision at the basic activities of daily living or minimal disabilities in instrumental activities of daily living due to physical problems were allowed. Materials and procedure All subjects were assessed by a trained psychologist in a quiet and comfortable room. After a clinical interview, the
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Italian version of the ACE-R [8], embedding the MMSE, was administered. The oldest old assessment was conducted by means of a neuropsychological battery administered according to standard instructions and procedures in order to obtain a cognitive profile in attention/executive function (Visual Search Test, Trail Making Test A and B, items of the Test of Everyday Attention), short-term memory (Digit Span Forward task), working memory (Digit Span Backward task), memory (Rey Auditory Verbal Learning Test, story recall, Rivermead Behavioral Memory Test—Third Edition), language (letter and category fluency task, Token Test and Communicative Abilities of Daily Living—Second Edition), visuospatial functions (copy drawing test), intelligence (Raven’s Colored Progressive Matrices and verbal task). Administration procedures and Italian normative data for score adjustment for age and education as well as normality cut-off scores are available [10]. Statistical methods The scores of control subjects were analyzed by means of simultaneous multiple regression, to check the influence of age, gender, and education. Before running the linear regression analyses, univariate distributions of the quantitative predictors and outcome variable were examined for assessment of normality. Normality was checked by means of skewness and kurtosis. If skewness and kurtosis indices fall within the range of -1 to ?1, the departures from normality can be considered not significant [11], and the linear regression model can be applied without the need of mathematical transformations. Otherwise, the better mathematical transformation (e.g., square root, log, inverse) should be applied to the data to more closely meet the assumptions of the normality of distributions for the linear regression model, and to select the most robust one. Then, for the ACE-R test, we evaluated the best fitting linear regression model that could be used to adjust the original scores according to the demographic variables. To this aim, we estimated a linear model able to calculate the score expected for a given subject on the basis of his/her age, gender, and education. The simultaneous regression included the variables that resulted significant when considered one at the time. At this stage, the effect of each predictor variable was studied partialling out the effect held in common with the other terms of the model. Taking this model as a basis, we calculated from the raw score an adjusted score, by adding or subtracting the contribution given by each significant concomitant variable in the final correction model. Following this approach, scores can be directly confronted across subjects of different demographic data.
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Adjusted scores were then ranked, and we used a nonparametric procedure to determine the lower (outer) and upper (inner) limit of the tolerance interval for test performance with a confidence level of 95 % [12]. Above the outer tolerance limit, one expects to find at least 95 % of the normal population (with 95 % confidence): hence, when a score is below the outer tolerance limit, the subject can be declared ‘‘not normal’’ with 95 % confidence. Above the inner tolerance limit, one expects to find at most 95 % of the population (with 95 % confidence): hence, when a score is above the inner tolerance limit, the subject can be declared ‘‘normal’’ with 95 % confidence. When a score falls between the outer and inner tolerance limits, no inferentially controlled judgment is possible. Given our sample size (N = 264), the outer limit, under which a performance may be considered abnormal, corresponds to the 8th scalar observation and the inner limit, above which a performance may be considered normal, to the 20th worst observation. To avoid errors due to the fixed upper limit of the test scores, no adjustment was made to scores of 100/100. The adjusted scores were classified into five categories (equivalent scores) endowed at least with an ordinal relationship: 0 = scores lower than the outer 5 % tolerance limits; 4 = scores higher than the median value of the sample; 1, 2, and 3 = intermediate scores between the central value and the pathology threshold on a quasiinterval scale (i.e., scores equal/lower than 10.2, 25.4, and 50 % of the normative sample distribution, respectively).
Examining the distribution of variables to check for nonnormally distributed variables, we found that skewness and kurtosis indices were within the acceptable range of -1 to ?1. Thus, departures from normality can be considered irrelevant [11], and the linear regression analyses were run without the need of mathematical transformations to the data. The results of the regression analyses are shown in Table 2. The influence of age and education was significant, whereas there was not an effect of gender. Table 3 reports the correction grid for the most frequent combinations of age and education. Intermediate values can be obtained by interpolation or using the original linear models reported in Table 2. Inner and outer tolerance limits are also reported: subjects with scores below the outer limit should be considered pathological, while those with scores above the inner limit can be considered normal. Subjects with scores included between the outer and the inner limits are better viewed as ‘‘borderline’’ cases. Finally, the values delimiting the Equivalent Scores (ES) are reported in Table 4. In order to illustrate the raw score adjustment, we took into account the case of a 65-year-old respondent with 13 years of schooling. The original raw score achieved on ACE-R was 82/100; the adjusted score becomes 82 ? (-5.36) = 76.64 so that the corresponding ES is 2.
Discussion Results Demographic distribution of the study population is reported in Table 1.
Table 1 Demographic distribution of the sample
Table 2 The effects of age, education (expressed as years of schooling), and gender within the linear regression model of the Italian ACE-R
Age and education
60–64
The ACE-R has had a large diffusion in the last years, since it is able to provide information on a wide range of cognitive domains. In addition, it has high sensitivity and specificity to differentiate people with and without
65–69
70–74
75–79
80–84
85?
TOT
\5
–
4
7
2
3
5
21
5
11
11
19
11
7
6
65
6–8
9
12
15
8
6
6
56
9–13
16
19
16
17
7
5
80
[13
6
10
9
5
8
4
42
TOT
42
56
66
43
31
26
264
Simple regression
Simultaneous regression
Age: F (1262) = 23.41, p \ 0.001
Age (education partialled out): F (1261) = 25.15, p \ 0.001
Education: F (1262) = 90.59, p \ 0.001
Education (age partialled out): F (1261) = 92.51, p \ 0.001
Sex: F (1262) = 0.757, ns Adjusted score: raw score ? 0.293 (age - 72.91) - 0.932 (education - 9.74)
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Neurol Sci Table 3 Adjustments to be added to, or subtracted from, the ACE-R raw scores according to age and education (expressed as years of schooling)
Table 4 Equivalent scores (ES) classification of adjusted scores of the Italian ACE-R
Education
Age 60
65
70
75
80
85
90
\5
1.60
3.02
4.49
5.95
7.42
8.88
5
0.64
2.10
3.57
5.03
6.50
7.96
9.43
8
-2.16
-0.70
0.77
2.23
3.70
5.16
6.63
10.35
13
-6.82
-5.36
-3.89
-2.43
-0.96
0.50
1.97
18
-11.48
-10.02
-8.55
-7.09
-5.62
-4.16
-2.69
Equivalent scores (ES)
Score interval
0
B66.92
1 2
Density
Cumulative frequency
8
8
66.93–73.94
19
27
73.95–79.86
40
67
3
79.87–84.93
65
132
4
[84.93
132
264
ES = 0 corresponds to an inferentially controlled judgement of being below the norm; 4 is equal or better than the 50th percentile; 1, 2, and 3 are intermediate between 0 and 4 on a quasi-interval scale
cognitive impairment [5]. The ACE-R is somewhat superior in diagnostic accuracy compared to the MMSE and it has been recommended in primary care, general hospital, and memory clinic [6]. In a recent review—dedicated to identify brief cognitive tests for people with suspected dementia, and determine their level and quality of evidence in clinical settings—the ACE-R has been associated to the best level of evidence [13]. However, recent studies have shown that a great caution is needed in using neuropsychological tests in subjects from cultural background different from the one that provided the normative data [14, 15]. In fact, the ACE-R performance varies according to age and education, and different classification criteria (cut-off) were found in different cultural contexts. In the Italian ACE-R validation study, two cut-offs have been established for young-old and old-old subjects, and these values are lower than those proposed for English-speaking populations (United Kingdom and Australia). For example, if we adopt the original English criteria [1], the 35 % (cut-off 82) or the 65 % (cut-off 88) of the Italian healthy sample [8] would have been classified as cognitively compromised. So, normative data represent an important aspect for the clinical use of any psychometric instrument, and this study provides the Italian ACE-R normative data. To our knowledge, the present study is the first to provide age and education adjusted scores and equivalent scores on the ACE-R. On the basis of this study, Italian clinicians will be able to use ACE-R taking into account the main demographic
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aspects by means of the proposed correction grid. Moreover, thanks to the equivalent scores, it will be possible to compare the performance on Italian ACE-R with other tests for which equivalent scores are available, allowing direct comparison of performance across the tests, independently of the difficulty of the single task. We propose a single cut-off point (66.92) to distinguish subjects with or without cognitive impairment. Similarly to the original study [1], gender does not affect the performance on the ACE-R total score. However, we found an effect of both age and education that confirms the results reported in recent publications on normative data [16–18]. In particular, accordingly to other studies, we found that younger participants showed a better total ACE-R score than older individuals, and those with higher education had better scores than those with lower education. As in the Brazilian normative study [16], the Italian normative data take into account the neuropsychological evaluation of oldest old subjects—a segment of population at higher risk of developing cognitive problems— which is estimated to increase quickly in the next future [19]. The new cut-off value (66.92) is lower than those proposed for the ACE-R (88, 82, and 75) [5], an aspect that may depend on the different socio-demographic variables in the Italian population. The main limit of our study is the relatively small size of the study sample. But, on the other hand, study subjects have been recruited in different Italian regions and are widely distributed across ages and education classes. Considering the neuropsychological value of the Italian ACE-R, it is noteworthy that it embeds the MMSE, still
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recently defined as the ‘‘benchmark against which all newer tools can be measured’’ [20]. In fact, despite its reported limits and disadvantages [13, 21], the MMSE remains the most widely used brief cognitive test, an aspect also related to the so-called ‘‘test inertia’’—the clinician’s reluctance to move beyond traditional tests [22]—that guarantees a long-lasting life to wellknown tools. After many years from its publication, most of the clinicians have familiarity with the MMSE administration, scoring, and interpretation. Moreover, MMSE score allows to classify dementia severity, to share information between clinicians and researchers, and to compare population data. With this perspective, incorporating the MMSE items, ACE-R represents an optimal approach to introduce innovation without breaking with the past. Unfortunately, the MMSE is protected by copyright in English-speaking countries [23], and significant costs are now associated with its use [13]. Recently, the Addenbrooke’s Cognitive Examination III (ACE III) [24] has been published. The ACE III is a revision of the ACE-R that does not embed the MMSE and contains different items, in order to improve the psychometric properties of the test maintaining 100 as maximum score evaluating the same cognitive domains. The ACE III allows higher psychometric properties than ACE-R and cuts down costs derived from the MMSE. However, until a new test will be able to replace the MMSE, the use of the ACE-R seems to remain the most accepted solution in research settings.
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9. 10. 11. 12.
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14.
Acknowledgments The authors acknowledge the control subjects and all the physicians at the Santa Maria della Misericordia Hospital in Perugia and the psychologists at the Clinical Psychology and Psychotherapy Adult and Developmental Ages in Taranto for their involvement in this study.
15.
Compliance with ethical standards
16.
Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
17.
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