Depression in Mild Cognitive Impairment is associated with Progression to Alzheimer's Disease:A Longitudinal Study

Background: Behavioral and psychological signs and symptoms of dementia (BPSD) belong to the core symptoms of dementia and are also common in mild cognitive impairment (MCI).Objective: This study would like to contribute to the understanding of the prognostic role of BPSD in MCI for the progression to dementia due to Alzheimer's disease (AD).Methods: Data were generated through an ongoing prospective longitudinal study on BPSD. Assessment was performed by means of the Middelheim Frontality Score, Behave-AD, Cohen-Mansfield Agitation Inventory, Cornell Scale for Depression in Dementia (CSDD), and Geriatric Depression Scale 30-questions (GDS-30). Cox proportional hazard models were used to test the hypothesis that certain BPSD in MCI are predictors of developing AD.Results: The study population consisted of 183 MCI patients at baseline. At follow-up, 74 patients were stable and 109 patients progressed to AD. The presence of significant depressive symptoms in MCI as measured by the CSDD (HR: 2.06; 95% CI: 1.23-3.44; p = 0.011) and the GDS-30 (HR: 1.77; 95% CI: 1.10-2.85; p = 0.025) were associated with progression to AD. The severity of depressive symptoms as measured by the GDS-30 was a predictor for progression too (HR: 1.06; 95% CI: 1.01-1.11; p = 0.020). Furthermore, the severity of agitated behavior, especially verbal agitation and the presence of purposeless activity, was also associated with progression, whereas diurnal rhythm disturbances were associated with no progression to AD.Conclusion: Depressive symptoms in MCI appear to be predictors for progression to AD.</p

Prevalence and prognosis of Alzheimer's disease at the mild cognitive impairment stage

Vos et al. compare the prevalence and prognosis of Alzheimer's disease at the mild cognitive impairment stage based on the IWG-1, IWG-2 and NIA-AA criteria. All three aid identification of early Alzheimer's disease, but combining amyloid and neuronal injury markers according to the NIA-AA criteria offers the most accurate prognosi

Combining business process and data discovery techniques for analyzing and improving integrated care pathways

Hospitals increasingly use process models for structuring their care processes. Activities performed to patients are logged to a database but these data are rarely used for managing and improving the efficiency of care processes and quality of care. In this paper, we propose a synergy of process mining with data discovery techniques. In particular, we analyze a dataset consisting of the activities performed to 148 patients during hospitalization for breast cancer treatment in a hospital in Belgium. We expose multiple quality of care issues that will be resolved in the near future, discover process variations and best practices and we discover issues with the data registration system. For example, 25 % of patients receiving breast-conserving therapy did not receive the key intervention "revalidation’’. We found this was caused by lowering the length of stay in the hospital over the years without modifying the care process. Whereas the process representations offered by Hidden Markov Models are easier to use than those offered by Formal Concept Analysis, this data discovery technique has proven to be very useful for analyzing process anomalies and exceptions in detail.status: publishe