Simon McBride

A multicentre, randomized, double-blinded, placebo-controlled phase III study to investigate EXtending the time for Thrombolysis in Emergency Neurological Deficits (EXTEND)

Background and hypothesis Thrombolytic therapy with tissue plasminogen activator is effective for acute ischaemic stroke within 4·5 h of onset. Patients who wake up with stroke are generally ineligible for stroke thrombolysis. We hypothesized that ischaemic stroke patients with significant penumbral mismatch on either magnetic resonance imaging or computer tomography at three- (or 4·5 depending on local guidelines) to nine-hours from stroke onset, or patients with wake-up stroke within nine-hours from midpoint of sleep duration, would have improved clinical outcomes when given tissue plasminogen activator compared to placebo. Study design EXtending the time for Thrombolysis in Emergency Neurological Deficits is an investigator-driven, Phase III, randomized, multicentre, double-blind, placebo-controlled study. Ischaemic stroke patients presenting after the three- or 4·5-h treatment window for tissue plasminogen activator and within nine-hours of stroke onset or with wake-up stroke within nine-hours from the midpoint of sleep duration, who fulfil clinical (National Institutes of Health Stroke Score ≥4–26 and prestroke modified Rankin Scale <2) will undergo magnetic resonance imaging or computer tomography. Patients who also meet imaging criteria (infarct core volume <70 ml, perfusion lesion : infarct core mismatch ratio >1·2, and absolute mismatch >10 ml) will be randomized to either tissue plasminogen activator or placebo. Study outcome The primary outcome measure will be modified Rankin Scale 0–1 at day 90. Clinical secondary outcomes include categorical shift in modified Rankin Scale at 90 days, reduction in the National Institutes of Health Stroke Score by 8 or more points or reaching 0–1 at day 90, recurrent stroke, or death. Imaging secondary outcomes will include symptomatic intracranial haemorrhage, reperfusion and or recanalization at 24 h and infarct growth at day 90.

Measuring the Lifespace of People With Parkinson’s Disease Using Smartphones: Proof of Principle

Background Lifespace is a multidimensional construct that describes the geographic area in which a person lives and conducts their activities, and reflects mobility, health, and well-being. Traditionally, it has been measured by asking older people to self-report the length and frequency of trips taken and assistance required. Global Positioning System (GPS) sensors on smartphones have been used to measure Lifespace of older people, but not with people with Parkinson’s disease (PD). Objective The objective of this study was to investigate whether GPS data collected via smartphones could be used to indicate the Lifespace of people with PD. Methods The dataset was supplied via the Michael J Fox Foundation Data Challenge and included 9 people with PD and 7 approximately matched controls. Participants carried smartphones with GPS sensors over two months. Data analysis compared the PD group and the control group. The impact of symptom severity on Lifespace was also investigated. Results Visualization methods for comparing Lifespace were developed including scatterplots and heatmaps. Lifespace metrics for comparison included average daily distance, percentage of time spent at home, and number of trips into the community. There were no significant differences between the PD and the control groups on Lifespace metrics. Visual representations of Lifespace were organized based on the self-reported severity of symptoms, suggesting a trend of decreasing Lifespace with increasing PD symptoms. Conclusions Lifespace measured by GPS-enabled smartphones may be a useful concept to measure the progression of PD and the impact of various therapies and rehabilitation programs. Directions for future use of GPS-based Lifespace are provided.

Rates of diagnostic transition and cognitive change at 18-month follow-up among 1,112 participants in the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing (AIBL)

BACKGROUND The Australian Imaging, Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing is a prospective study of 1,112 individuals (211 with Alzheimers disease (AD), 133 with mild cognitive impairment (MCI), and 768 healthy controls (HCs)). Here we report diagnostic and cognitive findings at the first (18-month) follow-up of the cohort. The first aim was to compute rates of transition from HC to MCI, and MCI to AD. The second aim was to characterize the cognitive profiles of individuals who transitioned to a more severe disease stage compared with those who did not. METHODS Eighteen months after baseline, participants underwent comprehensive cognitive testing and diagnostic review, provided an 80 ml blood sample, and completed health and lifestyle questionnaires. A subgroup also underwent amyloid PET and MRI neuroimaging. RESULTS The diagnostic status of 89.9% of the cohorts was determined (972 were reassessed, 28 had died, and 112 did not return for reassessment). The 18-month cohort comprised 692 HCs, 82 MCI cases, 197 AD patients, and one Parkinsons disease dementia case. The transition rate from HC to MCI was 2.5%, and cognitive decline in HCs who transitioned to MCI was greatest in memory and naming domains compared to HCs who remained stable. The transition rate from MCI to AD was 30.5%. CONCLUSION There was a high retention rate after 18 months. Rates of transition from healthy aging to MCI, and MCI to AD, were consistent with established estimates. Follow-up of this cohort over longer periods will elucidate robust predictors of future cognitive decline.

Design of a multi-site multi-state clinical trial of home monitoring of chronic disease in the community in Australia

BackgroundTelehealth services based on at-home monitoring of vital signs and the administration of clinical questionnaires are being increasingly used to manage chronic disease in the community, but few statistically robust studies are available in Australia to evaluate a wide range of health and socio-economic outcomes. The objectives of this study are to use robust statistical methods to research the impact of at home telemonitoring on health care outcomes, acceptability of telemonitoring to patients, carers and clinicians and to identify workplace cultural factors and capacity for organisational change management that will impact on large scale national deployment of telehealth services. Additionally, to develop advanced modelling and data analytics tools to risk stratify patients on a daily basis to automatically identify exacerbations of their chronic conditions.Methods/DesignA clinical trial is proposed at five locations in five states and territories along the Eastern Seaboard of Australia. Each site will have 25 Test patients and 50 case matched control patients. All participants will be selected based on clinical criteria of at least two hospitalisations in the previous year or four or more admissions over the last five years for a range of one or more chronic conditions. Control patients are matched according to age, sex, major diagnosis and their Socio-Economic Indexes for Areas (SEIFA). The Trial Design is an Intervention control study based on the Before-After-Control-Impact (BACI) design.DiscussionOur preliminary data indicates that most outcome variables before and after the intervention are not stationary, and accordingly we model this behaviour using linear mixed-effects (lme) models which can flexibly model within-group correlation often present in longitudinal data with repeated measures. We expect reduced incidence of unscheduled hospitalisation as well as improvement in the management of chronically ill patients, leading to better and more cost effective care. Advanced data analytics together with clinical decision support will allow telehealth to be deployed in very large numbers nationally without placing an excessive workload on the monitoring facility or the patients own clinicians.Trial registrationRegistered with Australian New Zealand Clinical Trial Registry on 1st April 2013. Trial ID: ACTRN12613000635763

STroke imAging pRevention and treatment (START): A longitudinal stroke cohort study: Clinical trials protocol

Rationale Stroke and poststroke depression are common and have a profound and ongoing impact on an individuals quality of life. However, reliable biological correlates of poststroke depression and functional outcome have not been well established in humans. Aims Our aim is to identify biological factors, molecular and imaging, associated with poststroke depression and recovery that may be used to guide more targeted interventions. Design In a longitudinal cohort study of 200 stroke survivors, the START – STroke imAging pRevention and Treatment cohort, we will examine the relationship between gene expression, regulator proteins, depression, and functional outcome. Stroke survivors will be investigated at baseline, 24 h, three-days, three-months, and 12 months poststroke for blood-based biological associates and at days 3–7, three-months, and 12 months for depression and functional outcomes. A sub-group (n = 100), the PrePARE: Prediction and Prevention to Achieve optimal Recovery Endpoints after stroke cohort, will also be investigated for functional and structural changes in putative depression-related brain networks and for additional cognition and activity participation outcomes. Stroke severity, diet, and lifestyle factors that may influence depression will be monitored. The impact of depression on stroke outcomes and participation in previous life activities will be quantified. Study Outcomes Clinical significance lies in the identification of biological factors associated with functional outcome to guide prevention and inform personalized and targeted treatments. Evidence of associations between depression, gene expression and regulator proteins, functional and structural brain changes, lifestyle and functional outcome will provide new insights for mechanism-based models of poststroke depression.

Towards quantifying the impact of Parkinson's disease using GPS and lifespace assessment

A lifespace assessment comprising of metrics and clustering algorithms is applied to a GPS data-set released by the Michael J. Fox Foundation. Seven participants of the study who had been diagnosed with Parkinsons disease of various levels carried a smart-phone which recorded GPS data every second. Metrics indicated a relationship between lifespace measured using GPS and the severity of symptoms due to Parkinsons disease. This assessment has potential future application in clinical monitoring of symptom severity and treatment efficacy, and in broadly monitoring population time use and community mobility/transportation.

A technological evaluation of the Microsoft Kinect for automated behavioural mapping at bed rest

Behavioural mapping (BM) is a long established method of structured observational study used to understand where patients are and what they are doing within a hospital setting. BM is prominent in stroke rehabilitation research, where that research indicates patients spend most of their time at bed rest. We evaluate the technical feasibility of using the Microsoft Kinect to automate patient physical activity classification at bed rest.

Adaptive Multi-Rate Compression Effects on Vowel Analysis

Signal processing on digitally sampled vowel sounds for the detection of pathological voices has been firmly established. This work examines compression artifacts on vowel speech samples that have been compressed using the adaptive multi-rate codec at various bit-rates. Whereas previous work has used the sensitivity of machine learning algorithm to test for accuracy, this work examines the changes in the extracted speech features themselves and thus report new findings on the usefulness of a particular feature. We believe this work will have potential impact for future research on remote monitoring as the identification and exclusion of an ill-defined speech feature that has been hitherto used, will ultimately increase the robustness of the system.

Hello harlie: Enabling speech monitoring through chat-bot conversations

People with neurological conditions such as Parkinsons disease and dementia are known to have difficulties in language and communication. This paper presents initial testing of an artificial conversational agent, called Harlie. Harlie runs on a smartphone and is able to converse with the user on a variety of topics. A description of the application and a sample dialog are provided to illustrate the various roles chat-bots can play in the management of neurological conditions. Harlie can be used for measuring voice and communication outcomes during the daily life of the user, and for gaining information about challenges encountered. Moreover, it is anticipated that she may also have an educational and support role.

Improving the Use, Analysis and Integration of Patient Health Data

Health Information Technologies (HIT) are being deployed world- wide to improve access to individual patient information. Primarily this is through the development of electronic health records (EHR) and electronic medical records (EMR). While the proper collection of this data has reached a high level of maturity, the use and analysis of it is only in its infancy. This data contains information which can potentially improve treatment for the individual patient and for the cohort of patients suffering a similar disease. The data can also provide valuable information for broader research purposes. In this paper we discuss the research contributions we are making in improving the use and analysis of patient data. Our projects include the analysis of physiological data, the extraction of information from multi-modal data types, the linking of data stored in heterogeneous data sources and the semantic integration of data. Through these projects we are providing new ways of using health data to improve health care delivery and provide support for medical research.