CANS-MCI compared to full professional neuropsychological evaluations for Mild Cognitive Impairment (MCI).1,2

The research reported here (as well as on our other webpages) was supported by the National Institute on Aging, National Institute of Health (1R43AG1865801 and 2R44AG18658-02).

To determine the ability of the CANS-MCI as an accurate screen for mild cognitive impairment (MCI) in primary care offices we compared scores on the CANS-MCI with the results of a full, independent neuropsychological examination. We used logistic regression models to predict the dichotomous outcomes of MCI vs normal cognitive functioning (as determined by the neuropsychological exam). Because education and associated cognitive reserve can affect scores on measures of cognitive impairment(3,4) we separated our sample into individuals with a high school degree or less (N=26) and those with schooling beyond high school (N=57). Gender and age were included in the model. We ran receiver operating characteristic (ROC) analyses to calculate the sensitivity (the proportion of persons who have MCI that are defined as having MCI) and specificity (proportion of persons who have normal cognitive functioning that are defined as having normal functioning) of the CANS-MCI.

The regression model statistics were very strong (see Table 1) indicating a good fit of the data to the model.(3,4) The CANS-MCI has extremely high levels of sensitivity and specificity (100%) in classifying those with an education up to a high school degree. The optimum sensitivity and specificity for those with 13+ years of education is lower (92%; 88%) but still excellent (see Table 2). These findings indicate the CANS-MCI can be a useful screening measure to determine if a person needs to be extensively assessed and monitored for cognitive impairments.

Table 1: CANS-MCI Logistic Regression1
2 Nagelkerke R2 Predicted Classification

Table 2: ROC Analyses1

We performed the same analyses using factor scores on the 74 subjects who returned a year later. Despite small numbers of subjects to date, these data indicate that the overall probability that a full neuropsychological evaluation will indicate MCI can be effectively predicted.
The regression model statistics for the 1-year follow-up evaluations were strong but limited by small sample size. The algorithms correctly classified 85% of participants with a high school degree or less (Chi-square = 11.7; Nagelkerke pseudo-R2 =.63 ) and 80% of those with at least some college (Chi-square = 31.4; Nagelkerke pseudo-R2 =.59) indicating a good fit of the data to the model (Table 3). The CANS-MCI has good levels of sensitivity and specificity in classifying those with an education up to a high school degree. The optimum sensitivity and specificity for those with 13+ years of education is lower but still excellent.
ROC curve analyses on the two educational levels revealed that cut-points lead to sensitivities/specificities of .93/.83 (<=12 yrs) and .84/.74 (13+ yrs). Areas under the curve were high (.917 for <= 12 yrs education and .888 for 13+ yrs) (Table 4).

Table 3: CANS-MCI Logistic Regression Analyses2

Table 4: CANS-MCI ROC Curve Analyses2

The results are impressive given that: 1) this study used a community sample with a lower incidence of MCI than the clinical samples often used; 2) the CANS-MCI screens for MCI which is much more difficult to detect than diagnosable AD (the standard used to validate many other tests); and 3) the CANS-MCI is meant to be used longitudinally, further increasing the sensitivity and specificity by comparing each person to his or her own previous performance. The CANS-MCI creates recommendations to primary care doctors concerning more extensive neuropsychological and/or imaging evaluations, even in those people who are still above average despite declines worthy of extra medical attention. As new treatments evolve (e.g Flurizan) that will stop but not reverse the progression of cognitive decline, the earliest possible identification of decline in primary care offices will become critical.

1. Jane B. Tornatore, PhD, Emory Hill, PhD, Jo A. Laboff, MSW, Brian Fogel
Preliminary Screening for Mild Cognitive Impairment (MCI): Using the CANS-MCI in Primary Care to Determine Need for Imaging.
9th International Conference on Alzheimer's Disease, Philadelphia, PA, July, 2004.

2. Jane B. Tornatore, PhD, Emory Hill, PhD, Jo A. Laboff, MSW, Brian Fogel
One year Follow-Up Analyses of Scoring Algorithms for a Mild Cognitive Impairment (MCI) Screen: The CANS-MCI Study. Alzheimer's Association International Conference on Prevention of Dementia: Early Diagnosis and Intervention, Washington, D.C., June, 2005.

3. Gifford DR, Cummings JL. Evaluating dementia screening tests: Methodological standards to rate their performance. Neurology 1999;52:224-227.

4. Lorentz WI, Scanlan JM, Borson S. Brief screening tests for dementia. Canadian Journal of Psychiatry 2002;47(8):723-732.