The CANS-MCI for the Assessment of Alzheimer’s Risk

There is a critical and increasing research focus upon developing ways to identify high-risk MCI patients for early treatment. Such efforts depend upon knowing as much as possible about who is most likely to progress from MCI to Alzheimer’s disease.

A very recently published study^[1] of the most significant predictors of progression from MCI to Alzheimer’s indicates, albeit indirectly, that the CANS-MCI is a very powerful tool for the detection of those predictors. This conclusion is based upon multiple known relationships between the CANS-MCI and all of the measurements used in the study.^{[Table 1]} The Korolev study proposed a precise, extensive model to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment. However, identification of high risk MCI patients may require even earlier and more efficient intervention.

The CANS-MCI was designed to measure all of the cognitive domains known to be most predictive of Alzheimer’s disease (AD): immediate and delayed memory; attentional speed and flexibility; executive mental control; clock hand placement fluency; language fluency. The CANS-MCI is the only test battery that does not require the presence, much less training, of a test administrator. Years of CANS-MCI usability research resulted in its ability to be fully self-administered by elderly people with Mild Cognitive Impairment, regardless of computer experience, even by elderly people with Mild Cognitive Impairment without causing anxiety-based cognitive interference during testing.^[2] When the tests are complete, the data may be automatically sent to a central server where they are kept for longitudinal comparisons. Scoring and longitudinal comparison test analysis is performed by an independent neuropsychology technician who does not know the identity of person tested. Hardware, software, installation, support and confidential data storage are low single fee costs.

Recently, the “robust” consistency and validity of the CANS-MCI was confirmed despite changes in the images (all CANS-MCI versions are country-specific) and language used.^[3] The sensitivity of the CANS-MCI when discriminating MCI from normal functioning is as high as the hand-administered MoCA and much greater than the sensitivity of the MMSE.^[4]

All latency and accuracy measures on the CANS-MCI are scalable scores; longitudinal comparisons may be performed accurately and frequently because alternative correct items are presented automatically each time a person returns for re-testing, eliminating practice effects. The entire set of tests takes about 25 minutes.

The early studies of the CANS-MCI established significant correlations between its Memory Factor Score and the Wechsler Memory Scale Logical Memory II.^[8] Korolev et al^[1] defined MCI by: (a) a subjective memory complaint; (b) objective memory loss, as measured by age- and education-adjusted scores on Wechsler Memory Scale Logical Memory II, but without significant impairment in other cognitive domains. “MCI subjects recruited as part of ADNI-1 were diagnosed based on the original Petersen (Mayo Clinic) criteria for amnestic Mild Cognitive Impairment. Thus, MCI subjects were limited to those with memory-only impairments (without significant impairments in other cognitive domains), also termed single-domain amnestic MCI. However, even among those MCI patients diagnosed using this single-domain amnestic MCI definition, impairments in multiple cognitive domains in addition to memory were predictive of MCI-to-AD progression. This provides empirical support to the recently revised clinical criteria for MCI, where the concept of “MCI due to AD” is proposed to include “impairment in one or more cognitive domains”.^[5] The CANS-MCI tests abilities within these cognitive domains, found in previous studies^[6] to be associated with greater risk of progression to AD dementia than are people with isolated memory deficits.

Significant correlations have been found between the CANS-MCI and the predictors of progression from MCI to Alzheimer’s found by Korolev.^[1]

Even though the cognitive assessments proved to be the most accurate (76.1%) in predicting MCI-to-dementia progression, the imaging dimensions were also valuable predictors, particularly volume/cortical thickness of the left hippocampus. Our preliminary hippocampal imaging results suggest that CANS-MCI-Memory Factor and CANS-MCI-Executive Functions Factor differences accurately reflect differences in brain volume.^[7]

There are several other ways in which Korolev’s finding support the exceptional power of the CANS-MCI (compared to other computer-assisted test batteries). The CANS-MCI avoids the variability (noise) contributed by inter-tester differences, since it is administered entirely, without staff input or training, by the computer itself, using single finger touches only. Although most research, such as Korolev’s, repeatedly train technicians in test administration to maximize inter-tester reliability, such efforts cannot be expected in clinical situations. That is particularly true when a primary care practice is not specialized yet serves as the initial detection situation for most patients who will come to the attention of specialists. The outstanding reliability of the CANS-MCI has been previously documented.^[8] The exceptional user friendliness of the CANS-MCI has also been widely acknowledged.^[9]

Table 1: Known relationships between the CANS-MCI and Korolev study measurements

Footnotes

1. Igor O. Korolev · Laura L. Symonds · Andrea C. Bozoki redicting Progression from Mild Cognitive Impairment to Alzheimer’s Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification.  Feb 2016 · PLoS ONE.

2. Hill, E. and Hammond, KW Usability of Multimedia Automated Psychological Tests to Screen for
   Alzheimer’s Disease. Proceedings of the American Medical Informatics Association Symposium 2000; 1030.

3. Cláudia M. Memória, Mônica S. Yassuda, Eduardo Y. Nakano and Orestes V. Forlenza International Psychogeriatrics, Volume 26 / Issue 09 / September 2014, pp 1483-1491.

4. Samrah Ahmed, Celeste de Jager & Gordon Wilcock A Comparison of screening tools for the assessment of Mild Cognitive Impairment: Preliminary findings. Neurocase Volume 18, No. 4, 336-351, 2012.

5. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7: 270–279.).

6. Bozoki A, Giordani B, Heidebrink JL, Foster NL. Mild cognitive impairments predict dementia in non-demented elderly patients with memory loss. Arch Neurol. 2001;58: 411–416.

7. Frederick A Schmitt, PhD Department of Neurology, Alzheimer’s Disease Research Center, University of Kentucky Medical Center, Presentation at Screen, Inc. Annual Science Meeting, January, 2013.

8. Tornatore, JB, Emory Hill, E, Jo Anne Laboff, J, and Mary E. McGann, ME Self-Administered Screening for Mild Cognitive Impairment: Validation of a Computerized Test Battery. Journal of Neuropsychiatry and Clinical Neurosciences, Volume 17, No. 1, 98-105, 2005.

9. Wild, K., Howieson, D., Webbe, F., Seelye, A., Kaye, J. Status of computerized cognitive testing in aging: a systematic review. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, Volume 4, No. 6, 428-437, 2008.