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Background: Early differential diagnosis of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) is important for treatment and disease management, but it remains challenging. Although computer-based drawing analysis may help differentiate AD and DLB, it has not been studied.Objective: We aimed to identify the differences in features characterizing the drawing process between AD, DLB, and cognitively normal (CN) individuals, and to evaluate the validity of using these features to identify and differentiate AD and DLB.Methods: We collected drawing data with a digitizing tablet and pen from 123 community-dwelling older adults in three clinical diagnostic groups of mild cognitive impairment or dementia due to AD (n = 47) or Lewy body disease (LBD; n = 27), and CN (n = 49), matched for their age, sex, and years of education. We then investigated drawing features in terms of the drawing speed, pressure, and pauses.Results: Reduced speed and reduced smoothness in speed and pressure were observed particularly in the LBD group, while increased pauses and total durations were observed in both the AD and LBD groups. Machine-learning models using these features achieved an area under the receiver operating characteristic curve (AUC) of 0.80 for AD versus CN, 0.88 for LBD versus CN, and 0.77 for AD versus LBD.Conclusion: Our results indicate how different types of drawing features were particularly discriminative between the diagnostic groups, and how the combination of these features can facilitate the identification and differentiation of AD and DLB. [ABSTRACT FROM AUTHOR]

Background: Besides cognitive and psychiatric abnormalities, motor symptoms are the most prominent in Huntington's disease. The manifest disease is preceded by a prodromal phase with subtle changes such as fine motor disturbances or concentration problems. Objective: Movement disorders show a high variation in their clinical manifestation depending on condition and external influences. Therefore, devices for continuous measurements, which patients use in their daily life and which can monitor motor abnormalities, in addition to the medical examination, might be useful. The aim of current scientific efforts is to find markers that reflect the prodromal phase in gene carriers. This is important for future interventional studies, as future therapies should be applied at the stage of neuronal dysfunction, i.e., before the clinical manifestation. Methods: We performed a software-supported, continuous monitoring of keyboard typing on the participants' own computer to evaluate this method as a tool to assess the motor phenotype in HD. We included 40 participants and obtained sufficient data from 25 participants, 12 of whom were manifest HD patients, 7 HD gene expansion carriers (HDGEC) and 6 healthy controls. Results: In a cross-sectional analysis we found statistically significant higher typing inconsistency in HD patients compared to controls. Typing inconsistency compared between HDGEC and healthy controls showed a trend to higher inconsistency levels in HDGEC. We found correlations between typing cadence and clinical scores: the UHDRS finger tapping item, the composite UHDRS and the CAP score. Conclusion: The typing cadence inconsistency is an appropriate marker to evaluate fine motor skills of HD patients and HDGEC and is correlated to established clinical measurements. [ABSTRACT FROM AUTHOR]

Sepsis is a highly heterogenous syndrome with variable causes and outcomes. As part of the SPHN/PHRT funding program, we aim to build a highly interoperable, interconnected network for data collection, exchange and analysis of patients on intensive care units in order to predict sepsis onset and mortality earlier. All five University Hospitals, Universities, the Swiss Institute of Bioinformatics and ETH Zurich are involved in this multi-disciplinary project. With two prospective clinical observational studies, we test our infrastructure setup and improve the framework gradually and generate relevant data for research. [ABSTRACT FROM AUTHOR]