Establishment of predictive and descriptive data to assist with the diagnosis of mild cognitive impairment

There has been a great deal of interest generated concerning the topic of a boundary or transitional state between what is considered to be associated with normal aging and a more marked deterioration such as a dementia. The current thought is that all individuals who are afflicted with a deteriorating memory beyond what is expected during normal aging will progressively worsen over time and evolve into DAT. This boundary state has received several descriptors over the past few years, the most common of which is mild cognitive impairment (MCI). The vast majority of individuals with MCI have primary memory impairment with relative sparing of other cognitive functions. If an individual is diagnosed with MCI they are at an increased risk for developing DAT because they are already exhibiting a deterioration that is not age appropriate. There have been several studies conducted specifically to look at the deterioration rate of these patients. The latest statistics from the Mayo Clinic regarding this particular subject pool is a conversion rate from MCI to DAT of approximately 12% per year over the past four years (Petersen et al. 1999).; The purpose of this research is to review the existing body of literature on MCI and synthesize current thoughts pertaining to this population in such a way that clarifies and summarizes all of the current literature. There is a great deal of speculation as to what causes MCI, who is at risk and how to identify MCI. The existing database on the MCI population is very limited and not very descriptive. It is imperative that more information is collected on this population so that MCI can be correctly identified and possibly treated at the earliest time-point.; The subject pool for this study consisted of one hundred and sixty-two elderly individuals who have been diagnosed with Mild Cognitive Impairment. Demographic data from their initial visit to the office was gathered as archival data for analysis in this study to assist in establishing predictive and descriptive data for this unique population.; The results of this study showed significant relationships between years of education and onset age of MCI. Another finding was with the number of years since retirement and onset age. The remainder of the results that were significant were found using Oneway ANOVAS with onset age, once again, as the dependent variable. There were significant differences within ethnicity, retirement status, marital status, nicotine usage and family history of DAT. The results of this study have added to the establishment of a demographic and predictive database for the MCI population.