Browsing by Author "Denney, David Austin"
Now showing 1 - 2 of 2
- Results Per Page
- Sort Options
Item Characterization and Differences Between Possible and Probable Mild Cognitive Impairment(2009-06-15) Denney, David Austin; Lacritz, Laura H.Mild Cognitive Impairment (MCI) is the period of subtle cognitive decline that occurs between normal aging and clinical Alzheimer's Disease (AD). Patients' subjective memory complaints (SMCs) are essential to the diagnosis of MCI. In cases where memory complaints are not verifiable by objective measures, patients are left without a formal diagnosis of cognitive impairment. The current proposal describes a study designed to compare the cognitive features and risk factors of AD in subgroups of patients with SMCs with (Probable MCI) and without (Possible MCI) objective memory deficits in relation to controls. It is predicted that the Probable MCI group will demonstrate lower performance and have a greater decline on neuropsychological measures than patients diagnosed with Possible MCI, who will demonstrate lower performance and have a greater decline on those measures than controls. Also, it is predicted that Probable MCI patients will have greater incidence of vascular risk factors and presence of the apolipoprotein element 4 (APOE-4) allele than the Possible MCI patients, who will have higher incidence of these variables than controls. There is also a demographic analysis designed to identify any differences in age, education, and gender between the groups. Implications of possible outcomes of the study are then discussed.Item Cognitive and Behavioral Predictors of Fall Risk in Parkinson Disease(2015-07-24) Denney, David Austin; Lacritz, Laura H.; Cullum, C. Munro; Hynan, Linda S.; Patel, Neepa; Ruchinskas, RobertFalls and their related injuries are a significant health issue for individuals with Parkinson disease (PD). Several factors have been identified that increase fall risk, including cognitive impairment, impulsiveness, and lower balance confidence, as well as PD-related characteristics. However, to date, no definitive predictor profile has been identified. As a result, there is a need to develop a comprehensive model incorporating elements from each of the areas known to have a relationship with fall behaviors in PD. Such information could result in improved identification and treatment of PD patients at higher risk of falls. This study used stepwise logistic regression analyses to identify predictors of retrospectively reported falls from four domains, which included separate cognitive, impulsiveness/impulsive-compulsive disorder (ICD) related behaviors, disease characteristics, and balance confidence models. Each stepwise logistic regression yielded significant results (p < .20), and all of the significant predictor variables were included in a fifth combined model. The combined stepwise logistic regression was significant for postural instability (odds ratio = 8.66), verbal learning (California Verbal Learning Test-2 Total Learning T Score [CVLT-II]) (odds ratio = 0.95), and self-reported behavioral impulsiveness (Barratt Impulsiveness Scale-11 [BIS-11]) (odds ratio = 1.10). Model comparisons using net reclassification improvement (NRI) and the Hanley and McNeil (1983) method were conducted to determine if the combined model was significantly better at predicting fall risk than the domain-specific models. The combined model had the highest rate of accurately predicting fall risk (83%); however, the combined model was not significantly better at predicting fall risk than the impulsiveness/ICD or balance confidence models. These results showed that postural instability was the best predictor of fall risk; however, incorporating cognitive and impulsiveness measures improved prediction of fall risk. In light of these findings, screening for impulsiveness and, when possible, verbal learning, could be incorporated into routine clinical PD evaluations for better identification of patients at higher risk of falls.