Multi-dimensional relationships among dementia, depression and prescribed drugs in England and Wales hospitals.

BACKGROUND: Dementia is a group of symptoms that largely affects older people. The majority of patients face behavioural and psychological symptoms (BPSD) during the course of their illness. Alzheimer's disease (AD) and vascular dementia (VaD) are two of the most prevalent types of dementia. Available medications provide symptomatic benefits and provide relief from BPSD and associated health issues. However, it is unclear how specific dementia, antidepressant, antipsychotic, antianxiety, and mood stabiliser drugs, used in the treatment of depression and dementia subtypes are prescribed in hospital admission, during hospital stay, and at the time of discharge. To address this, we apply multi-dimensional data analytical approaches to understand drug prescribing practices within hospitals in England and Wales. METHODS: We made use of the UK National Audit of Dementia (NAD) dataset and pre-processed the dataset. We evaluated the pairwise Pearson correlation of the dataset and selected key data features which are highly correlated with dementia subtypes. After that, we selected drug prescribing behaviours (e.g. specific medications at the time of admission, during the hospital stay, and upon discharge), drugs and disorders. Then to shed light on the relations across multiple features or dimensions, we carried out multiple regression analyses, considering the number of dementia, antidepressant, antipsychotic, antianxiety, mood stabiliser, and antiepileptic/anticonvulsant drug prescriptions as dependent variables, and the prescription of other drugs, number of patients with dementia subtypes (AD/VaD), and depression as independent variables. RESULTS: In terms of antidepressant drugs prescribed in hospital admission, during stay and discharge, the number of sertraline and venlafaxine prescriptions were associated with the number of VaD patients whilst the number of mirtazapine prescriptions was associated with frontotemporal dementia patients. During admission, the number of lamotrigine prescriptions was associated with frontotemporal dementia patients, and with the number of valproate and dosulepin prescriptions. During discharge, the number of mirtazapine prescriptions was associated with the number of donepezil prescriptions in conjunction with frontotemporal dementia patients. Finally, the number of prescriptions of donepezil/memantine at admission, during hospital stay and at discharge exhibited positive association with AD patients. CONCLUSION: Our analyses reveal a complex, multifaceted set of interactions among prescribed drug types, dementia subtypes, and depression.

Multiple Cost Optimisation for Alzheimer's Disease Diagnosis.

Current machine learning techniques for dementia diagnosis often do not take into account real-world practical constraints, which may include, for example, the cost of diagnostic assessment time and financial budgets. In this work, we built on previous cost-sensitive feature selection approaches by generalising to multiple cost types, while taking into consideration that stakeholders attempting to optimise the dementia care pathway might face multiple non-fungible budget constraints. Our new optimisation algorithm involved the searching of cost-weighting hyperparameters while constrained by total budgets. We then provided a proof of concept using both assessment time cost and financial budget cost. We showed that budget constraints could control the feature selection process in an intuitive and practical manner, while adjusting the hyperparameter increased the range of solutions selected by feature selection. We further showed that our budget-constrained cost optimisation framework could be implemented in a user-friendly graphical user interface sandbox tool to encourage non-technical users and stakeholders to adopt and to further explore and audit the model - a humans-in-the-loop approach. Overall, we suggest that setting budget constraints initially and then fine tuning the cost-weighting hyperparameters can be an effective way to perform feature selection where multiple cost constraints exist, which will in turn lead to more realistic optimising and redesigning of dementia diagnostic assessments. Clinical Relevance-By optimising diagnostic accuracy against various costs (e.g. assessment administration time and financial budget) predictive yet practical dementia diagnostic assessments can be redesigned to suit clinical use.

Alzheimer's Disease Assessments Optimized for Diagnostic Accuracy and Administration Time.

OBJECTIVE: Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the time taken to administer diagnostic assessment has yet to be taken into account in feature-selection based optimisation for dementia diagnosis. We address these issues by considering the impact of assessment time as a practical constraint for feature selection of cognitive and functional assessments in Alzheimer's disease diagnosis. METHODS: We use three different feature selection algorithms to select informative subsets of dementia assessment items from a large open-source dementia dataset. We use cost-sensitive feature selection to optimise our feature selection results for assessment time as well as diagnostic accuracy. To encourage clinical adoption and further evaluation of our proposed accuracy-vs-cost optimisation algorithms, we also implement a sandbox-like toolbox with graphical user interface to evaluate user-chosen subsets of assessment items. RESULTS: We find that there are subsets of accuracy-cost optimised assessment items that can perform better in terms of diagnostic accuracy and/or total assessment time than most other standard assessments. DISCUSSION: Overall, our analysis and accompanying sandbox tool can facilitate clinical users and other stakeholders to apply their own domain knowledge to analyse and decide which dementia diagnostic assessment items are useful, and aid the redesigning of dementia diagnostic assessments. Clinical Impact (Clinical Research): By optimising diagnostic accuracy and assessment time, we redesign predictive and efficient dementia diagnostic assessments and develop a sandbox interface to facilitate evaluation and testing by clinicians and non-specialists.

The impact of hearing impairment and hearing aid use on progression to mild cognitive impairment in cognitively healthy adults: An observational cohort study.

INTRODUCTION: We assessed the association of self-reported hearing impairment and hearing aid use with cognitive decline and progression to mild cognitive impairment (MCI). METHODS: We used a large referral-based cohort of 4358 participants obtained from the National Alzheimer's Coordinating Center. The standard covariate-adjusted Cox proportional hazards model, the marginal structural Cox model with inverse probability weighting, standardized Kaplan-Meier curves, and linear mixed-effects models were applied to test the hypotheses. RESULTS: Hearing impairment was associated with increased risk of MCI (standardized hazard ratio [HR] 2.58, 95% confidence interval [CI: 1.73 to 3.84], P = .004) and an accelerated rate of cognitive decline (P < .001). Hearing aid users were less likely to develop MCI than hearing-impaired individuals who did not use a hearing aid (HR 0.47, 95% CI [0.29 to 0.74], P = .001). No difference in risk of MCI was observed between individuals with normal hearing and hearing-impaired adults using hearing aids (HR 0.86, 95% CI [0.56 to 1.34], P = .51). DISCUSSION: Use of hearing aids may help mitigate cognitive decline associated with hearing loss.

Practical Strategies for Extreme Missing Data Imputation in Dementia Diagnosis.

Accurate computational models for clinical decision support systems require clean and reliable data but, in clinical practice, data are often incomplete. Hence, missing data could arise not only from training datasets but also test datasets which could consist of a single undiagnosed case, an individual. This work addresses the problem of extreme missingness in both training and test data by evaluating multiple imputation and classification workflows based on both diagnostic classification accuracy and computational cost. Extreme missingness is defined as having ∼50% of the total data missing in more than half the data features. In particular, we focus on dementia diagnosis due to long time delays, high variability, high attrition rates and lack of practical data imputation strategies in its diagnostic pathway. We identified and replicated the extreme missingness structure of data from a real-world memory clinic on a larger open dataset, with the original complete data acting as ground truth. Overall, we found that computational cost, but not accuracy, varies widely for various imputation and classification approaches. Particularly, we found that iterative imputation on the training dataset combined with a reduced-feature classification model provides the best approach, in terms of speed and accuracy. Taken together, this work has elucidated important factors to be considered when developing a predictive model for a dementia diagnostic support system.

Service evaluation comparing Acute Care at Home for older people service and conventional service within an acute hospital care of elderly ward.

AIMS AND OBJECTIVES: This study evaluated the impact of a consultant-led Acute Care at Home service in comparison with conventional hospital admission to a care of elderly ward. BACKGROUND: Globally, there has been an increased demand for healthcare services caused by population growth and a rise in chronic conditions and an ageing population. Acute Care at Home services offer acute, hospital-level care in a person's own home. Five services have been commissioned across Northern Ireland since 2014 with limited research investigating their feasibility and effectiveness. DESIGN: Quantitative design, using service evaluation methodology. METHODS: A 1-year retrospective chart review was undertaken exploring admission demographics and post-discharge clinical outcomes of patients admitted to a Northern Ireland, Care of the Elderly ward (n = 191) and a consultant-led Acute Care at Home Service (n = 314) between April 2018-March 2019. Data were analysed using descriptive and inferential data analysis methods including frequencies, independent t tests and chi-square analysis. Outcome measurements included length of stay, 30-day, 3- and 6-month readmission and mortality rates, functional ability and residence on discharge. STROBE checklist was used in reporting this study. RESULTS: Acute Care at Home services are associated with higher readmission and mortality rates at 30 days, 3 and 6 months. Fewer patients die while under Acute Care at Home care. Patients admitted to the Acute Care at Home services experience a reduced length of stay and decreased escalation in domiciliary care packages and are less likely to require subacute rehabilitation on discharge. There is no difference in gender, age and early warnings score between the two cohorts. CONCLUSION: The Acute Care at Home service is a viable alternative to hospital for older patients. It prevents functional decline and the need for domiciliary care or nursing home placement. It is likely that the Acute Care at Home service has higher mortality and readmissions rates due to treating a higher proportion of dependent, frail older adults. RELEVANCE TO CLINICAL PRACTICE: Acute Care at Home services continue to evolve worldwide. This service evaluation has confirmed that Acute Care at Home services are safe and cost-effective alternatives to traditional older people hospital services. Such services offer patient choice, reduce length of stay and costs and prevent functional decline of older adults. This study accentuates the need to expand Acute Care at Home provision and capacity throughout Northern Ireland.

Pharmacological treatment of hypertension in people without prior cerebrovascular disease for the prevention of cognitive impairment and dementia.

BACKGROUND: This is an update of a Cochrane Review first published in 2006 (McGuinness 2006), and previously updated in 2009 (McGuinness 2009). Hypertension is a risk factor for dementia. Observational studies suggest antihypertensive treatment is associated with lower incidences of cognitive impairment and dementia. There is already clear evidence to support the treatment of hypertension after stroke. OBJECTIVES: To assess whether pharmacological treatment of hypertension can prevent cognitive impairment or dementia in people who have no history of cerebrovascular disease. SEARCH METHODS: We searched the Specialised Register of the Cochrane Dementia and Cognitive Improvement Group, CENTRAL, MEDLINE, Embase, three other databases, as well as many trials registries and grey literature sources, most recently on 7 July 2020. SELECTION CRITERIA: We included randomised controlled trials (RCTs) in which pharmacological interventions to treat hypertension were given for at least 12 months. We excluded trials of pharmacological interventions to lower blood pressure in non-hypertensive participants. We also excluded trials conducted solely in people with stroke. DATA COLLECTION AND ANALYSIS: Two authors independently assessed trial quality and extracted data. We contacted study authors for additional information. We collected information regarding incidence of dementia, cognitive decline, change in blood pressure, adverse effects and quality of life. We assessed the certainty of evidence using GRADE. MAIN RESULTS: We included 12 studies, totaling 30,412 participants, in this review. Eight studies compared active treatment with placebo. Of the four non-placebo-controlled studies, two compared intensive versus standard blood pressure reduction. The two final included studies compared different classes of antihypertensive drug. Study durations varied from one to five years. The combined result of four placebo-controlled trials that reported incident dementia indicated no evidence of a difference in the risk of dementia between the antihypertensive treatment group and the placebo group (236/7767 versus 259/7660, odds ratio (OR) 0.89, 95% confidence interval (CI) 0.72 to 1.09; very low certainty evidence, downgraded due to study limitations and indirectness). The combined results from five placebo-controlled trials that reported change in Mini-Mental State Examination (MMSE) may indicate a modest benefit from antihypertensive treatment (mean difference (MD) 0.20, 95% CI 0.10 to 0.29; very low certainty evidence, downgraded due to study limitations, indirectness and imprecision). The certainty of evidence for both cognitive outcomes was downgraded on the basis of study limitations and indirectness. Study durations were too short, overall, to expect a significant difference in dementia rates between groups. Dementia and cognitive decline were secondary outcomes for most studies. Additional sources of bias include: the use of antihypertensive medication by the placebo group in the placebo-controlled trials; failure to reach recruitment targets; and early termination of studies on safety grounds. Meta-analysis of the placebo-controlled trials reporting results found a mean change in systolic blood pressure of -9.25 mmHg (95% CI -9.73, -8.78) between treatment (n = 8973) and placebo (n = 8820) groups, and a mean change in diastolic blood pressure of -2.47 mmHg (95% CI -2.70, -2.24) between treatment (n = 7700) and placebo (n = 7509) groups (both low certainty evidence downgraded on the basis of study limitations and inconsistency). Three trials - SHEP 1991, LOMIR MCT IL 1996 and MRC 1996 - reported more withdrawals due to adverse events in active treatment groups than placebo groups. Participants on active treatment in Syst Eur 1998 were less likely to discontinue treatment due to side effects, and participants on active treatment in HYVET 2008 reported fewer 'serious adverse events' than in the placebo group. There was no evidence of a difference in withdrawals rates between groups in SCOPE 2003, and results were unclear for Perez Stable 2000 and Zhang 2018. Heterogeneity precluded meta-analysis. Five of the placebo-controlled trials provided quality of life (QOL) data. Heterogeneity again precluded meta-analysis. SHEP 1991, Syst Eur 1998 and HYVET 2008 reported no evidence of a difference in QOL measures between active treatment and placebo groups over time. The SCOPE 2003 sub-study (Degl'Innocenti 2004) showed a smaller drop in QOL measures in the active treatment compared to the placebo group. LOMIR MCT IL 1996 reported an improvement in a QOL measure at twelve months in one active treatment group and deterioration in another. AUTHORS' CONCLUSIONS: High certainty randomised controlled trial evidence regarding the effect of hypertension treatment on dementia and cognitive decline does not yet exist. The studies included in this review provide low certainty evidence (downgraded primarily due to study limitations and indirectness) that pharmacological treatment of hypertension, in people without prior cerebrovascular disease, leads to less cognitive decline compared to controls. This difference is below the level considered clinically significant. The studies included in this review also provide very low certainty evidence that pharmacological treatment of hypertension, in people without prior cerebrovascular disease, prevents dementia.

An Early Stage Researcher's Primer on Systems Medicine Terminology.

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

Association of the use of hearing aids with the conversion from mild cognitive impairment to dementia and progression of dementia: A longitudinal retrospective study.

INTRODUCTION: Hearing aid usage has been linked to improvements in cognition, communication, and socialization, but the extent to which it can affect the incidence and progression of dementia is unknown. Such research is vital given the high prevalence of dementia and hearing impairment in older adults, and the fact that both conditions often coexist. In this study, we examined for the first time the effect of the use of hearing aids on the conversion from mild cognitive impairment (MCI) to dementia and progression of dementia. METHODS: We used a large referral-based cohort of 2114 hearing-impaired patients obtained from the National Alzheimer's Coordinating Center. Survival analyses using multivariable Cox proportional hazards regression model and weighted Cox regression model with censored data were performed to assess the effect of hearing aid use on the risk of conversion from MCI to dementia and risk of death in hearing-impaired participants. Disease progression was assessed with Clinical Dementia Rating Sum of Boxes (CDR-SB) scores. Three types of sensitivity analyses were performed to validate the robustness of the results. RESULTS: MCI participants that used hearing aids were at significantly lower risk of developing all-cause dementia compared to those not using hearing aids (hazard ratio [HR] 0.73, 95% confidence interval [CI], 0.61 to 0.89; false discovery rate [FDR] P = 0.004). The mean annual rate of change (standard deviation) in CDR-SB scores for hearing aid users with MCI was 1.3 (1.45) points and significantly lower than for individuals not wearing hearing aids with a 1.7 (1.95) point increase in CDR-SB per year (P = 0.02). No association between hearing aid use and risk of death was observed. Our findings were robust subject to sensitivity analyses. DISCUSSION: Among hearing-impaired adults, hearing aid use was independently associated with reduced dementia risk. The causality between hearing aid use and incident dementia should be further tested.

CSynth: an interactive modelling and visualization tool for 3D chromatin structure.

MOTIVATION: The 3D structure of chromatin in the nucleus is important for gene expression and regulation. Chromosome conformation capture techniques, such as Hi-C, generate large amounts of data showing interaction points on the genome but these are hard to interpret using standard tools. RESULTS: We have developed CSynth, an interactive 3D genome browser and real-time chromatin restraint-based modeller to visualize models of any chromosome conformation capture (3C) data. Unlike other modelling systems, CSynth allows dynamic interaction with the modelling parameters to allow experimentation and effects on the model. It also allows comparison of models generated from data in different tissues/cell states and the results of third-party 3D modelling outputs. In addition, we include an option to view and manipulate these complicated structures using Virtual Reality (VR) so scientists can immerse themselves in the models for further understanding. This VR component has also proven to be a valuable teaching and a public engagement tool. AVAILABILITYAND IMPLEMENTATION: CSynth is web based and available to use at csynth.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

High-dimensional brain-wide functional connectivity mapping in magnetoencephalography.

BACKGROUND: Brain functional connectivity (FC) analyses based on magneto/electroencephalography (M/EEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, with the latter leading to conservative hypothesis testing. NEW METHOD: We removed such constraint by estimating high-dimensional source-based M/EEG-FC using cluster-permutation statistic (CPS) and demonstrated the feasibility of this approach by identifying resting-state changes in mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease. Particularly, we proposed a unified framework for CPS analysis together with a novel neighbourhood measure to estimate more compact and neurophysiological plausible neural communication. As clusters could more confidently reveal interregional communication, we proposed and tested a cluster-strength index to demonstrate other advantages of CPS analysis. RESULTS: We found clusters of increased communication or hypersynchronization in MCI compared to healthy controls in delta (1-4 Hz) and higher-theta (6-8 Hz) bands oscillations. These mainly consisted of interactions between occipitofrontal and occipitotemporal regions in the left hemisphere, which may be critically affected in the early stages of Alzheimer's disease. CONCLUSIONS: Our approach could be important to create high-resolution FC maps from neuroimaging studies in general, allowing the multimodal analysis of neural communication across multiple spatial scales. Particularly, FC clusters more robustly represent the interregional communication by identifying dense bundles of connections that are less sensitive to inter-individual anatomical and functional variability. Overall, this approach could help to better understand neural information processing in healthy and disease conditions as needed for developing biomarker research.

Shaping a data-driven era in dementia care pathway through computational neurology approaches.

BACKGROUND: Dementia is caused by a variety of neurodegenerative diseases and is associated with a decline in memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The complexity of dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly in clinical decision-making. MAIN BODY: Despite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment. However, the dementia care pathway is currently suboptimal. We propose that through computational approaches, understanding of dementia aetiology could be improved, and dementia assessments could be more standardised, objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of data-driven computational neurology approaches and the development of practical clinical decision support systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany such implementations. CONCLUSION: The data-driven era for dementia research has arrived with the potential to transform the healthcare system, creating a more efficient, transparent and personalised service for dementia.

Multi-time-point data preparation robustly reveals MCI and dementia risk factors.

INTRODUCTION: Conflicting results on dementia risk factors have been reported across studies. We hypothesize that variation in data preparation methods may partially contribute to this issue. METHODS: We propose a comprehensive data preparation approach comparing individuals with stable diagnosis over time to those who progress to mild cognitive impairment (MCI)/dementia. This was compared to the often-used "baseline" analysis. Multivariate logistic regression was used to evaluate both methods. RESULTS: The results obtained from sensitivity analyses were consistent with those from our multi-time-point data preparation approach, exhibiting its robustness. Compared to analysis using only baseline data, the number of significant risk factors identified in progression analyses was substantially lower. Additionally, we found that moderate depression increased healthy-to-MCI/dementia risk, while hypertension reduced MCI-to-dementia risk. DISCUSSION: Overall, multi-time-point-based data preparation approaches may pave the way for a better understanding of dementia risk factors, and address some of the reproducibility issues in the field.

Acceptability and use of a patient-held communication tool for people living with dementia: a longitudinal qualitative study.

OBJECTIVES: To assess the acceptability and use of a low-cost patient-held communication tool. DESIGN: Longitudinal qualitative interviews at three time points over 18 months and document content analysis. SETTING: Primary and community services. PARTICIPANTS: Twenty-eight dyads: People living with dementia in Northern Ireland and their informal carers. INTERVENTIONS: A patient-held healthcare 'passport' for people living with dementia. PRIMARY AND SECONDARY OUTCOMES: Acceptability and use of the passport-barriers and facilitators to successful engagement. RESULTS: There was a qualified appreciation of the healthcare passport and a much more nuanced, individualistic or personalised approach to its desirability and use. How people perceive it and what they actually do with it are strongly determined by individual contexts, dementia stage and other health problems, social and family needs and capacities. We noted concerns about privacy and ambivalence about engaging with health professionals. CONCLUSION: Such tools may be of use but there is a need for demanding, thoughtful and nuanced programme delivery for future implementation in dementia care. The incentivisation and commitment of general practitioners is crucial. Altering the asymmetrical relationship between professionals and patients requires more extensive attention.

Functional Imaging of the Outer Retinal Complex using High Fidelity Imaging Retinal Densitometry.

We describe a new technique, high fidelity Imaging Retinal Densitometry (IRD), which probes the functional integrity of the outer retinal complex. We demonstrate the ability of the technique to map visual pigment optical density and synthesis rates in eyes with and without macular disease. A multispectral retinal imaging device obtained precise measurements of retinal reflectance over space and time. Data obtained from healthy controls and 5 patients with intermediate AMD, before and after photopigment bleaching, were used to quantify visual pigment metrics. Heat maps were plotted to summarise the topography of rod and cone pigment kinetics and descriptive statistics conducted to highlight differences between those with and without AMD. Rod and cone visual pigment synthesis rates in those with AMD (v = 0.043 SD 0.019 min-1 and v = 0.119 SD 0.046 min-1, respectively) were approximately half those observed in healthy controls (v = 0.079 SD 0.024 min-1 for rods and v = 0.206 SD 0.069 min-1 for cones). By mapping visual pigment kinetics across the central retina, high fidelity IRD provides a unique insight into outer retinal complex function. This new technique will improve the phenotypic characterisation, diagnosis and treatment monitoring of various ocular pathologies, including AMD.

A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual.

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R 2 = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.

The simplest supramolecular helix.

Diethylamine is the smallest and simplest molecule that features a supramolecular helix as its lowest energy aggregate. Structural studies and large scale sampling simulations show that the helical arrangement is more stable than cyclic structures, which are the dominant species for other small hydrogen bonding molecules.

Views of Caregivers on the Ethics of Assistive Technology Used for Home Surveillance of People Living with Dementia.

This paper examines the ethics of using assistive technology such as video surveillance in the homes of people living with dementia. Ideation and concept elaboration around the introduction of a camera-based surveillance service in the homes of people with dementia, typically living alone, is explored. The paper reviews relevant literature on surveillance of people living with dementia, and summarises the findings from ideation and concept elaboration workshops, designed to capture the views of those involved in the care of people living with dementia at home. The research question relates to the ethical considerations of using assistive technologies that include video surveillance in the homes of people living with dementia, and the implications for a person living with dementia whenever video surveillance is used in their home and access to the camera is given to the person's family. The review of related work indicated that such video surveillance may result in loss of autonomy or freedom for the person with dementia. The workshops reflected the findings from the related work, and revealed useful information to inform the service design, in particular in fine-tuning the service to find the best relationship between privacy and usefulness. Those who took part in the workshops supported the concept of the use of camera in the homes of people living with dementia, with some significant caveats around privacy. The research carried out in this work is small in scale but points towards an acceptance by many caregivers of people living with dementia of surveillance technologies. This paper indicates that those who care for people living with dementia at home are willing to make use of camera technology and therefore the value of this work is to help shed light on the direction for future research.

A pilot study on geometrical uncertainties for intra ocular cancers in radiotherapy.

A system for stabilising and monitoring eye movements for linac-based stereotactic radiotherapy associated with the mobile eye, the Eye Tracker, was developed. Whilst the Eye Tracker design is based on a previously reported system, the purpose of this study was to confirm that the modified version can be used with clinically acceptable treatment margins. We report the estimates of the margin required to account for inter- and intra-fraction eye motion based on data from 12 consecutive patients treated with the Eye Tracker system in place. Patients were immobilised in a head and neck mask and were required to fixate on a light source. A camera system monitored eye movements relative to CT simulation baseline measurements. The Exactrac system (Brainlab, Feldkirchen, Germany) combined with the Varian TrueBeamSTx (Varian Medical Systems, Palo Alto, CA) confirmed pre- and intra-treatment setup of the head position. Displacement/rotation of the image of the pupil/iris was determined in the lateral and superior-inferior directions using a video display. A standard margin equation was applied to estimate the margin required to account for inter- and intra-fraction eye movement. The average displacement in both directions was 0.1-0.2 mm (0.36 mm SD). All patients maintained a position within 1 mm of the intended position during treatment. Based on a Bayesian estimation of the systematic and treatment errors, accounting for displacements in two-planes and a standard deviation of the penumbral width of 1.3 mm, the estimated margins to achieve coverage of the GTV with the 95% isodose in 90% of patients was found to be less than 1 mm. Small random and systematic uncertainties due to inter- and intra-fraction movement of the eye were achieved with the Eye Tracker. Whilst the estimated margins are small (<1 mm) they need to be considered in addition to contouring and treatment delivery uncertainties.