Identifying mild cognitive impairment in older adults
| dc.contributor.author | Ritchie, Lesley Jane | |
| dc.contributor.supervisor | Tuokko, Holly A. | |
| dc.date.accessioned | 2009-01-20T00:00:43Z | |
| dc.date.available | 2009-01-20T00:00:43Z | |
| dc.date.copyright | 2008 | en_US |
| dc.date.issued | 2009-01-20T00:00:43Z | |
| dc.degree.department | Department of Psychology | |
| dc.degree.level | Doctor of Philosophy Ph.D. | en_US |
| dc.description.abstract | The absence of gold standard criteria for mild cognitive impairment (MCI) impedes the comparison of research findings and the development of primary and secondary prevention strategies addressing the possible conversion to dementia. The objective of Study 1 was to compare the predictive ability of different MCI models as markers for incipient dementia in a longitudinal population-based Canadian sample. The utility of well-documented MCI criteria using data from persons who underwent a clinical examination in the second wave of the Canadian Study of Health and Aging (CSHA) was examined. Demographic characteristics, average neuropsychological test performance, and prevalence and conversion rates were calculated for each classification. Receiver operating characteristic (ROC) analyses were employed to assess the predictive power of each cognitive classification. The highest prevalence and conversion rates were associated with case definitions of multiple-domain MCI. The only diagnostic criteria to significantly predict dementia five years later was the Cognitive Impairment, No Dementia (CIND) Type 2 case definition. It is estimated that more restrictive MCI case definitions fail to address the varying temporal increases in decline across different cognitive domains in the progression from normal cognitive functioning to dementia. Using data from the CSHA, the objective of Study 2 was to elucidate the clinical correlates that best differentiate between cognitive classifications. A machine learning algorithm was used to identify the symptoms that best discriminated between: 1) not cognitively impaired (NCI) and CIND; 2) CIND & demented; and 3) converting and non-converting CIND participants. Poor retrieval was consistently a significant predictor of greater cognitive impairment across all three questions. While interactions with other predictors were noted when differentiating CIND from NCI and demented from non-demented participants, retrieval was the sole predictor of conversion to dementia over five years. Importantly, the limited specificity and predictive values of the respective algorithms caution against their use as clinical markers of CIND, dementia, or conversion. Rather, it is recommended that the predictors serve as markers for ongoing monitoring and assessment. Overall, the results of both studies suggest that the architecture of pathological cognitive decline to dementia may not be captured by a single set of diagnostic criteria. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/1336 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Mild Cognitive Impairment | en_US |
| dc.subject | Cognitive Impairment No Dementia | en_US |
| dc.subject | Neuropsychological Assessment | en_US |
| dc.subject | Predictive Algorithms | en_US |
| dc.subject | Classification Trees | en_US |
| dc.subject.lcsh | UVic Subject Index::Humanities and Social Sciences::Psychology::Clinical psychology | en_US |
| dc.title | Identifying mild cognitive impairment in older adults | en_US |
| dc.type | Thesis | en_US |