Study of mirtazapine for agitated behaviours in dementia (SYMBAD): a randomised, double-blind, placebo-controlled trial.

BACKGROUND: Agitation is common in people with dementia and negatively affects the quality of life of both people with dementia and carers. Non-drug patient-centred care is the first-line treatment, but there is a need for other treatment when this care is not effective. Current evidence is sparse on safer and effective alternatives to antipsychotics. We assessed the efficacy and safety of mirtazapine, an antidepressant prescribed for agitation in dementia. METHODS: This parallel-group, double-blind, placebo-controlled trial-the Study of Mirtazapine for Agitated Behaviours in Dementia trial (SYMBAD)-was done in 26 UK centres. Participants had probable or possible Alzheimer's disease, agitation unresponsive to non-drug treatment, and a Cohen-Mansfield Agitation Inventory (CMAI) score of 45 or more. They were randomly assigned (1:1) to receive either mirtazapine (titrated to 45 mg) or placebo. The primary outcome was reduction in CMAI score at 12 weeks. This trial is registered with ClinicalTrials.gov, NCT03031184, and ISRCTN17411897. FINDINGS: Between Jan 26, 2017, and March 6, 2020, 204 participants were recruited and randomised. Mean CMAI scores at 12 weeks were not significantly different between participants receiving mirtazapine and participants receiving placebo (adjusted mean difference -1·74, 95% CI -7·17 to 3·69; p=0·53). The number of controls with adverse events (65 [64%] of 102 controls) was similar to that in the mirtazapine group (67 [66%] of 102 participants receiving mirtazapine). However, there were more deaths in the mirtazapine group (n=7) by week 16 than in the control group (n=1), with post-hoc analysis suggesting this difference was of marginal statistical significance (p=0·065). INTERPRETATION: This trial found no benefit of mirtazapine compared with placebo, and we observed a potentially higher mortality with use of mirtazapine. The data from this study do not support using mirtazapine as a treatment for agitation in dementia. FUNDING: UK National Institute for Health Research Health Technology Assessment Programme.

Cost-effectiveness of mirtazapine for agitated behaviors in dementia: findings from a randomized controlled trial.

OBJECTIVES: To examine the costs and cost-effectiveness of mirtazapine compared to placebo over 12-week follow-up. DESIGN: Economic evaluation in a double-blind randomized controlled trial of mirtazapine vs. placebo. SETTING: Community settings and care homes in 26 UK centers. PARTICIPANTS: People with probable or possible Alzheimer's disease and agitation. MEASUREMENTS: Primary outcome included incremental cost of participants' health and social care per 6-point difference in CMAI score at 12 weeks. Secondary cost-utility analyses examined participants' and unpaid carers' gain in quality-adjusted life years (derived from EQ-5D-5L, DEMQOL-Proxy-U, and DEMQOL-U) from the health and social care and societal perspectives. RESULTS: One hundred and two participants were allocated to each group; 81 mirtazapine and 90 placebo participants completed a 12-week assessment (87 and 95, respectively, completed a 6-week assessment). Mirtazapine and placebo groups did not differ on mean CMAI scores or health and social care costs over the study period, before or after adjustment for center and living arrangement (independent living/care home). On the primary outcome, neither mirtazapine nor placebo could be considered a cost-effective strategy with a high level of confidence. Groups did not differ in terms of participant self- or proxy-rated or carer self-rated quality of life scores, health and social care or societal costs, before or after adjustment. CONCLUSIONS: On cost-effectiveness grounds, the use of mirtazapine cannot be recommended for agitated behaviors in people living with dementia. Effective and cost-effective medications for agitation in dementia remain to be identified in cases where non-pharmacological strategies for managing agitation have been unsuccessful.

Ethical considerations in design and implementation of home-based smart care for dementia.

It has now become a realistic prospect for smart care to be provided at home for those living with long-term conditions such as dementia. In the contemporary smart care scenario, homes are fitted with an array of sensors for remote monitoring providing data that feed into intelligent systems developed to highlight concerning patterns of behaviour or physiological measurements and to alert healthcare professionals to the need for action. This paper explores some ethical issues that may arise within such smart care systems, focusing on the extent to which ethical issues can be addressed at the system design stage. Artificial intelligence has been widely portrayed as an ethically risky technology, posing challenges for privacy and human autonomy and with the potential to introduce and exacerbate bias and inequality. While broad principles for ethical artificial intelligence have become established, the mechanisms for governing ethical artificial intelligence are still evolving. In healthcare settings the implementation of smart technologies falls within the existing frameworks for ethical review and governance. Feeding into this ethical review there are many practical steps that designers can take to build ethical considerations into the technology. After exploring the pre-emptive steps that can be taken in design and governance to provide for an ethical smart care system, the paper reviews the potential for further ethical challenges to arise within the everyday implementation of smart care systems in the context of dementia, despite the best efforts of all concerned to pre-empt them. The paper concludes with an exploration of the dilemmas that may thus face healthcare professionals involved in implementing this kind of smart care and with a call for further research to explore ethical dimensions of smart care both in terms of general principles and lived experience.

A comparative study of the effect of the Time for Dementia programme on medical students.

BACKGROUND: Traditional healthcare education typically focuses on short block clinical placements based on acute care, investigations and technical aspects of diagnosis and treatment. It may therefore fail to build the understanding, compassion and person-centred empathy needed to help those with long-term conditions, like dementia. Time for Dementia was developed to address this. METHOD: Parallel group comparison of two cohorts of UK medical students from universities, one participating in Time for Dementia (intervention group) and one not (control group). In Time for Dementia students visit a person with dementia and their family in pairs for 2 hours three times a year for 2 years, the control group received their normal curriculum. RESULTS: In an adjusted multilevel model (intervention group n = 274, control n = 112), there was strong evidence supporting improvements for Time for Dementia participants in: total Approaches to Dementia Questionnaire score (coefficient: 2.19, p = 0.003) and its person-centredness subscale (1.32, p = 0.006) and weaker evidence in its hopefulness subscale (0.78, p = 0.070). There was also strong evidence of improvement in the Dementia Knowledge Questionnaire (1.63, p < 0.001) and Dementia Attitudes Scale (total score: 6.55, p < 0.001; social comfort subscale: 4.15, p < 0.001; dementia knowledge subscale: 3.38, p = 0.001) scores. No differences were observed on the Alzheimer's Disease Knowledge Scale, the Medical Condition Regard Scale or the Jefferson Scale of Empathy. DISCUSSION: Time for Dementia may help improve the attitudes of medical students towards dementia promoting a person-centred approach and increasing social comfort. Such patient-focused programmes may be a useful complement to traditional medical education.

'No more routine outpatient appointments in the NHS': it is time to shift to data-driven appointment.

Currently, outpatient care in the UK is expensive and needs improvement, with traditional systems having been identified as no longer fit for purpose. Making sustainable changes to outpatient appointment systems is vital in order to meet increasing demands and cost. Shifting to data and technology-driven outpatient care may be one way to tackle these demands. As technology becomes more diverse and accessible, its implementation into healthcare systems can make services more efficient and help with transitioning from outdated practices to more effective protocols. Patient Recorded Outcome Measures (PROMs) and home-monitoring devices could be the key step in identifying which patients require input and help shift to more data-driven appointment scheduling based on clinical need, rather than at regular intervals of time. Virtual care and technology-driven service provision could also revolutionise outpatient systems, maintaining high quality care while improving accessibility to patients. Patient involvement and empowerment while making these changes will assist shared decision making surrounding their care and allow them to be champions of their own health, helping clinicians to provide a patient-centred service. Understanding how these may be implemented will help clinicians take an active role in the development of these practices.

Home monitoring of daily living activities and prediction of agitation risk in a cohort of people living with dementia.

BACKGROUND: People living with dementia (PLWD) have an increased susceptibility to developing adverse physical and psychological events. Internet of Things (IoT) technologies provides new ways to remotely monitor patients within the comfort of their homes, particularly important for the timely delivery of appropriate healthcare. Presented here is data collated as part of the on-going UK Dementia Research Institute's Care Research and Technology Centre cohort and Technology Integrated Health Management (TIHM) study. There are two main aims to this work: first, to investigate the effect of the COVID-19 quarantine on the performance of daily living activities of PLWD, on which there is currently little research; and second, to create a simple classification model capable of effectively predicting agitation risk in PLWD, allowing for the generation of alerts with actionable information by which to prevent such outcomes. METHOD: A within-subject, date-matched study was conducted on daily living activity data using the first COVID-19 quarantine as a natural experiment. Supervised machine learning approaches were then applied to combined physiological and environmental data to create two simple classification models: a single marker model trained using ambient temperature as a feature, and a multi-marker model using ambient temperature, body temperature, movement, and entropy as features. RESULT: There are 102 PLWD total included in the dataset, with all patients having an established diagnosis of dementia, but with ranging types and severity. The COVID-19 study was carried out on a sub-group of 21 patient households. In 2020, PLWD had a significant increase in daily household activity (p = 1.40e-08), one-way repeated measures ANOVA). Moreover, there was a significant interaction between the pandemic quarantine and patient gender on night-time bed-occupancy duration (p = 3.00e-02, two-way mixed-effect ANOVA). On evaluating the models using 10-fold cross validation, both the single and multi-marker model were shown to balance precision and recall well, having F1-scores of 0.80 and 0.66, respectively. CONCLUSION: Remote monitoring technologies provide a continuous and reliable way of monitoring patient day-to-day wellbeing. The application of statistical analyses and machine learning algorithms to combined physiological and environmental data has huge potential to positively impact the delivery of healthcare for PLWD.

Paper spray screening and liquid chromatography/mass spectrometry confirmation for medication adherence testing: A two-step process.

RATIONALE: Paper spray offers a rapid screening test without the need for sample preparation. The incomplete extraction of paper spray allows for further testing using more robust, selective and sensitive techniques such as liquid chromatography/mass spectrometry (LC/MS). Here we develop a two-step process of paper spray followed by LC/MS to (1) rapidly screen a large number of samples and (2) confirm any disputed results. This demonstrates the applicability for testing medication adherence from a fingerprint. METHODS: Following paper spray analysis, drugs of abuse samples were analysed using LC/MS. All analyses were completed using a Q Exactive™ Plus Orbitrap™ mass spectrometer. This two-step procedure was applied to fingerprints collected from patients on a maintained dose of the antipsychotic drug quetiapine. RESULTS: The extraction efficiency of paper spray for two drugs of abuse and metabolites was found to be between 15 and 35% (analyte dependent). For short acquisition times, the extraction efficiency was found to vary between replicates by less than 30%, enabling subsequent analysis by LC/MS. This two-step process was then applied to fingerprints collected from two patients taking the antipsychotic drug quetiapine, which demonstrates how a negative screening result from paper spray can be resolved using LC/MS. CONCLUSIONS: We have shown for the first time the sequential analysis of the same sample using paper spray and LC/MS, as well as the detection of an antipsychotic drug from a fingerprint. We propose that this workflow may also be applied to any type of sample compatible with paper spray, and will be especially convenient where only one sample is available for analysis.

Technology integrated health management for dementia.

Pioneering advances have been made in Internet of Things technologies (IoT) in healthcare. This article describes the development and testing of a bespoke IoT system for dementia care. Technology integrated health management (TIHM) for dementia is part of the NHS England National Test Bed Programme and has involved trailing the deployment of network enabled devices combined with artificial intelligence to improve outcomes for people with dementia and their carers. TIHM uses machine learning and complex algorithms to detect and predict early signs of ill health. The premise is if changes in a person's health or routine can be identified early on, support can be targeted at the point of need to prevent the development of more serious complications.

Comparison of service utilisation and costs of working age adults and older adults receiving treatment for psychosis and severe non-psychotic conditions in England: implications for commissioning.

OBJECTIVE: Current plans in the English National Health Service are to replace block contracts for mental health providers with a single tariff for each 'cluster' of conditions. A single tariff will not take into account the potential additional complexity and costs inherent in caring for older people. To examine the basis for a uniform tariff, differences in service utilisation and costs between working age adults and older adults in two populous clusters (non-psychotic, psychotic) were investigated across five mental health healthcare providers in and around London. METHODS: Retrospective review of records over 3 months assessing service utilisation and costs using the Client Services Receipt Inventory. RESULTS: Records of 362 patients were reviewed, 179 older adults (90 non-psychotic, 89 psychotic) and 183 adults of working age (83 non-psychotic, 100 psychotic). Older adults in both clusters had more tests, assessments and home visits. Overall costs of care of older adults were significantly higher in the non-psychotic cluster (£5634, vs £4405 psychotic, p = 0.044). CONCLUSIONS: An appropriate age-related tariff is required for each cluster. Copyright © 2016 John Wiley & Sons, Ltd.

How do we enhance undergraduate healthcare education in dementia? A review of the role of innovative approaches and development of the Time for Dementia Programme.

OBJECTIVES: Traditional healthcare education, delivered through a series of time-limited clinical placements, often fails to deliver an understanding of the experiences of those with long-term conditions, a growing issue for healthcare systems. Responses include longitudinal integrated clerkships and senior mentor programmes allowing students' longer placements, continuity of contact and opportunities to learn about chronic illness and patient experience. We review their development and delivery in dementia and present the Time for Dementia (TFD) Programme, a novel 2-year interdisciplinary educational programme. DESIGN: The study design involves a scoping review of enhanced placements in dementia for healthcare professionals in training including longitudinal integrated clerkships and senior mentor programmes and a case study of the development of TFD and its evaluation. RESULTS: Eight enhanced programmes in dementia were identified and seven in the USA. None were compulsory and all lasted 12 months. All reported positive impact from case study designs but data quality was weak. Building on these, TFD was developed in partnership between the Alzheimer's Society, universities and NHS and made a core part of the curriculum for medical, nursing and paramedic students. Students visit a person with dementia and their family in pairs for 2 h every 3 months for 2 years. They follow a semi-structured interaction guide focusing on experiences of illness and services and complete reflective appraisals. CONCLUSIONS: We need interprofessional undergraduate healthcare education that enables future healthcare professionals to be able to understand and manage the people with the long-term conditions who current systems often fail. TFD is designed to help address this need. © 2016 The Authors. International Journal of Geriatric Psychiatry Published by John Wiley & Sons Ltd.

The natural course of anxiety disorders in the elderly: a systematic review of longitudinal trials.

BACKGROUND: The anxiety disorders are a prevalent mental health problem in older age with a considerable impact on quality of life. Until recently there have been few longitudinal studies on anxiety in this age group, consequently most of the evidence to date has been cross-sectional in nature. METHODS: We undertook a literature search of Medline, PsycINFO, the Cochrane trials database and the TRIP medical database to identify longitudinal studies which would help elucidate natural history and prognosis of anxiety disorders in the elderly. RESULTS: We identified 12 papers of 10 longitudinal studies in our Review. This represented 34,691 older age participants with 5,199 with anxiety disorders including anxious depression and 3,532 individuals with depression without anxiety. Relapse rates of anxiety disorders are high over 6 year follow-up with considerable migration to mixed anxiety-depression and pure depressive mood episodes. Mixed anxiety-depression appears to be a poorer prognostic state than pure anxiety or pure depression with higher relapse rates across studies. In community settings treatment rates are low with 7-44% of the anxious elderly treated on antidepressant medications. CONCLUSIONS: To our knowledge this is the first Systematic Review of longitudinal trials of anxiety disorders in older people. Major longitudinal studies of the anxious elderly are establishing the high risk of relapse and persistence alongside the progression to depression and anxiety depression states. There remains considerable under-treatment in community studies. Specialist assessment and treatment and major public health awareness of the challenges of anxiety disorders in the elderly are required.

Digital remote monitoring for screening and early detection of urinary tract infections.

Urinary Tract Infections (UTIs) are one of the most prevalent bacterial infections in older adults and a significant contributor to unplanned hospital admissions in People Living with Dementia (PLWD), with early detection being crucial due to the predicament of reporting symptoms and limited help-seeking behaviour. The most common diagnostic tool is urine sample analysis, which can be time-consuming and is only employed where UTI clinical suspicion exists. In this method development and proof-of-concept study, participants living with dementia were monitored via low-cost devices in the home that passively measure activity, sleep, and nocturnal physiology. Using 27828 person-days of remote monitoring data (from 117 participants), we engineered features representing symptoms used for diagnosing a UTI. We then evaluate explainable machine learning techniques in passively calculating UTI risk and perform stratification on scores to support clinical translation and allow control over the balance between alert rate and sensitivity and specificity. The proposed UTI algorithm achieves a sensitivity of 65.3% (95% Confidence Interval (CI) = 64.3-66.2) and specificity of 70.9% (68.6-73.1) when predicting UTIs on unseen participants and after risk stratification, a sensitivity of 74.7% (67.9-81.5) and specificity of 87.9% (85.0-90.9). In addition, feature importance methods reveal that the largest contributions to the predictions were bathroom visit statistics, night-time respiratory rate, and the number of previous UTI events, aligning with the literature. Our machine learning method alerts clinicians of UTI risk in subjects, enabling earlier detection and enhanced screening when considering treatment.

Quantification of neurofilament light and glial fibrillary acidic protein in finger-prick blood.

An accurate diagnosis of neurodegenerative disease and traumatic brain injury is important for prognostication and treatment. Neurofilament light and glial fibrillary acidic protein (GFAP) are leading biomarkers for neurodegeneration and glial activation that are detectable in blood. Yet, current recommendations require rapid centrifugation and ultra-low temperature storage post-venepuncture. Here, we investigated if these markers can be accurately measured in finger-prick blood using dried plasma spot cards. Fifty patients (46 with dementia; 4 with traumatic brain injury) and 19 healthy volunteers underwent finger-prick and venous sampling using dried plasma spot cards and aligned plasma sampling. Neurofilament light and GFAP were quantified using a Single molecule array assay and correlations between plasma and dried plasma spot cards assessed. Biomarker concentrations in plasma and finger-prick dried plasma spot samples were significantly positively correlated (neurofilament light ρ = 0.57; GFAP ρ = 0.58, P < 0.001). Finger-prick neurofilament light and GFAP were significantly elevated after acute traumatic brain injury with non-significant group-level increases in dementia (91% having Alzheimer's disease dementia). In conclusion, we present preliminary evidence that quantifying GFAP and neurofilament light using finger-prick blood collection is viable, with samples stored at room temperature using dried plasma spot cards. This has potential to expand and promote equitable testing access, including in settings where trained personnel are unavailable to perform venepuncture.

A Protocol for Evaluating Digital Technology for Monitoring Sleep and Circadian Rhythms in Older People and People Living with Dementia in the Community.

Sleep and circadian rhythm disturbance are predictors of poor physical and mental health, including dementia. Long-term digital technology-enabled monitoring of sleep and circadian rhythms in the community has great potential for early diagnosis, monitoring of disease progression, and assessing the effectiveness of interventions. Before novel digital technology-based monitoring can be implemented at scale, its performance and acceptability need to be evaluated and compared to gold-standard methodology in relevant populations. Here, we describe our protocol for the evaluation of novel sleep and circadian technology which we have applied in cognitively intact older adults and are currently using in people living with dementia (PLWD). In this protocol, we test a range of technologies simultaneously at home (7-14 days) and subsequently in a clinical research facility in which gold standard methodology for assessing sleep and circadian physiology is implemented. We emphasize the importance of assessing both nocturnal and diurnal sleep (naps), valid markers of circadian physiology, and that evaluation of technology is best achieved in protocols in which sleep is mildly disturbed and in populations that are relevant to the intended use-case. We provide details on the design, implementation, challenges, and advantages of this protocol, along with examples of datasets.

Early Clinical Experiences of Esketamine Nasal Spray in the UK in Adults with Treatment-Resistant Major Depressive Disorder: Advisory Panel Recommendations.

Purpose: Treatment-resistant depression (TRD) is associated with profound morbidity for patients, placing a significant burden on those affected, the health service and wider society. Despite this, TRD remains chronically underserved in terms of viable treatment options. To address this gap, an advisory panel of psychiatrists and clinical researchers with experience in managing TRD convened to develop best practice statements on the use of esketamine nasal spray, one of the first TRD treatments to be licensed in 30 years. Methods: During a virtual meeting held on 12th November 2020, the advisory panel shared their experiences of using esketamine nasal spray in their clinical practice. The meeting focused on developing and refining recommendations for setting up and running an efficient esketamine nasal spray clinic for patients living with TRD. At the conclusion of the meeting, agreement was reached on all recommendation statements. Results: In setting up an esketamine nasal spray clinic, it is important to consider the logistical requirements involved and put measures in place to ensure it runs as efficiently as possible. Educating patients about the treatment and maintaining their well-being is paramount for preventing discontinuation. Putting in place checklists can be a useful strategy for ensuring treatment appointments run smoothly and safely. Conclusion: Providing additional treatment options for the management of TRD, such as esketamine nasal spray, is likely to be key to improving the long-term outcomes of this underserved patient population.

TIHM: An open dataset for remote healthcare monitoring in dementia.

Dementia is a progressive condition that affects cognitive and functional abilities. There is a need for reliable and continuous health monitoring of People Living with Dementia (PLWD) to improve their quality of life and support their independent living. Healthcare services often focus on addressing and treating already established health conditions that affect PLWD. Managing these conditions continuously can inform better decision-making earlier for higher-quality care management for PLWD. The Technology Integrated Health Management (TIHM) project developed a new digital platform to routinely collect longitudinal, observational, and measurement data, within the home and apply machine learning and analytical models for the detection and prediction of adverse health events affecting the well-being of PLWD. This work describes the TIHM dataset collected during the second phase (i.e., feasibility study) of the TIHM project. The data was collected from homes of 56 PLWD and associated with events and clinical observations (daily activity, physiological monitoring, and labels for health-related conditions). The study recorded an average of 50 days of data per participant, totalling 2803 days.

Remote Monitoring of Physiology in People Living With Dementia: An Observational Cohort Study.

BACKGROUND: Internet of Things (IoT) technology enables physiological measurements to be recorded at home from people living with dementia and monitored remotely. However, measurements from people with dementia in this context have not been previously studied. We report on the distribution of physiological measurements from 82 people with dementia over approximately 2 years. OBJECTIVE: Our objective was to characterize the physiology of people with dementia when measured in the context of their own homes. We also wanted to explore the possible use of an alerts-based system for detecting health deterioration and discuss the potential applications and limitations of this kind of system. METHODS: We performed a longitudinal community-based cohort study of people with dementia using "Minder," our IoT remote monitoring platform. All people with dementia received a blood pressure machine for systolic and diastolic blood pressure, a pulse oximeter measuring oxygen saturation and heart rate, body weight scales, and a thermometer, and were asked to use each device once a day at any time. Timings, distributions, and abnormalities in measurements were examined, including the rate of significant abnormalities ("alerts") defined by various standardized criteria. We used our own study criteria for alerts and compared them with the National Early Warning Score 2 criteria. RESULTS: A total of 82 people with dementia, with a mean age of 80.4 (SD 7.8) years, recorded 147,203 measurements over 958,000 participant-hours. The median percentage of days when any participant took any measurements (ie, any device) was 56.2% (IQR 33.2%-83.7%, range 2.3%-100%). Reassuringly, engagement of people with dementia with the system did not wane with time, reflected in there being no change in the weekly number of measurements with respect to time (1-sample t-test on slopes of linear fit, P=.45). A total of 45% of people with dementia met criteria for hypertension. People with dementia with α-synuclein-related dementia had lower systolic blood pressure; 30% had clinically significant weight loss. Depending on the criteria used, 3.03%-9.46% of measurements generated alerts, at 0.066-0.233 per day per person with dementia. We also report 4 case studies, highlighting the potential benefits and challenges of remote physiological monitoring in people with dementia. These include case studies of people with dementia developing acute infections and one of a person with dementia developing symptomatic bradycardia while taking donepezil. CONCLUSIONS: We present findings from a study of the physiology of people with dementia recorded remotely on a large scale. People with dementia and their carers showed acceptable compliance throughout, supporting the feasibility of the system. Our findings inform the development of technologies, care pathways, and policies for IoT-based remote monitoring. We show how IoT-based monitoring could improve the management of acute and chronic comorbidities in this clinically vulnerable group. Future randomized trials are required to establish if a system like this has measurable long-term benefits on health and quality of life outcomes.

A pragmatic, multicentre, double-blind, placebo-controlled randomised trial to assess the safety, clinical and cost-effectiveness of mirtazapine and carbamazepine in people with Alzheimer's disease and agitated behaviours: the HTA-SYMBAD trial.

Background: Agitation is common and impacts negatively on people with dementia and carers. Non-drug patient-centred care is first-line treatment, but we need other treatment when this fails. Current evidence is sparse on safer and effective alternatives to antipsychotics. Objectives: To assess clinical and cost-effectiveness and safety of mirtazapine and carbamazepine in treating agitation in dementia. Design: Pragmatic, phase III, multicentre, double-blind, superiority, randomised, placebo-controlled trial of the clinical effectiveness of mirtazapine over 12 weeks (carbamazepine arm discontinued). Setting: Twenty-six UK secondary care centres. Participants: Eligibility: probable or possible Alzheimer's disease, agitation unresponsive to non-drug treatment, Cohen-Mansfield Agitation Inventory score ≥ 45. Interventions: Mirtazapine (target 45 mg), carbamazepine (target 300 mg) and placebo. Outcome measures: Primary: Cohen-Mansfield Agitation Inventory score 12 weeks post randomisation. Main economic outcome evaluation: incremental cost per six-point difference in Cohen-Mansfield Agitation Inventory score at 12 weeks, from health and social care system perspective. Data from participants and informants at baseline, 6 and 12 weeks. Long-term follow-up Cohen-Mansfield Agitation Inventory data collected by telephone from informants at 6 and 12 months. Randomisation and blinding: Participants allocated 1 : 1 : 1 ratio (to discontinuation of the carbamazepine arm, 1 : 1 thereafter) to receive placebo or carbamazepine or mirtazapine, with treatment as usual. Random allocation was block stratified by centre and residence type with random block lengths of three or six (after discontinuation of carbamazepine, two or four). Double-blind, with drug and placebo identically encapsulated. Referring clinicians, participants, trial management team and research workers who did assessments were masked to group allocation. Results: Two hundred and forty-four participants recruited and randomised (102 mirtazapine, 102 placebo, 40 carbamazepine). The carbamazepine arm was discontinued due to slow overall recruitment; carbamazepine/placebo analyses are therefore statistically underpowered and not detailed in the abstract. Mean difference placebo-mirtazapine (-1.74, 95% confidence interval -7.17 to 3.69; p = 0.53). Harms: The number of controls with adverse events (65/102, 64%) was similar to the mirtazapine group (67/102, 66%). However, there were more deaths in the mirtazapine group (n = 7) by week 16 than in the control group (n = 1). Post hoc analysis suggests this was of marginal statistical significance (p = 0.065); this difference did not persist at 6- and 12-month assessments. At 12 weeks, the costs of unpaid care by the dyadic carer were significantly higher in the mirtazapine than placebo group [difference: £1120 (95% confidence interval £56 to £2184)]. In the cost-effectiveness analyses, mean raw and adjusted outcome scores and costs of the complete cases samples showed no differences between groups. Limitations: Our study has four important potential limitations: (1) we dropped the proposed carbamazepine group; (2) the trial was not powered to investigate a mortality difference between the groups; (3) recruitment beyond February 2020, was constrained by the COVID-19 pandemic; and (4) generalisability is limited by recruitment of participants from old-age psychiatry services and care homes. Conclusions: The data suggest mirtazapine is not clinically or cost-effective (compared to placebo) for agitation in dementia. There is little reason to recommend mirtazapine for people with dementia with agitation. Future work: Effective and cost-effective management strategies for agitation in dementia are needed where non-pharmacological approaches are unsuccessful. Study registration: This trial is registered as ISRCTN17411897/NCT03031184. Funding: This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 27, No. 23. See the NIHR Journals Library website for further project information.

It is common for people with Alzheimer's disease to experience agitation, for example feeling restless or unsettled. If left untreated, agitation can lead to poorer quality of life and increased hospitalisation and strain for family carers. Often these symptoms are treated with medications that are usually used to manage psychosis (antipsychotic drugs), but such medication has limited effectiveness and can cause serious adverse effects to patients, including risk of increased death. Two medications that are already commonly prescribed for other health issues, mirtazapine (an antidepressant) and carbamazepine (a drug used to treat epilepsy), had been identified as a possible alternative way of treating agitation in Alzheimer's disease that might not have the harms associated with antipsychotic medication. In this study, we compared the effects of giving mirtazapine or carbamazepine with a dummy drug (placebo) in people with Alzheimer's disease who were experiencing agitation. The results of the study showed that neither medication was any more effective than the placebo in reducing agitation over 12 weeks in terms of improving symptoms, or in economic terms. Mirtazapine may lead to additional carer costs as compared to placebo. The study findings are stronger for mirtazapine than carbamazepine because the carbamazepine arm was stopped when it had recruited less than half the numbers needed. That was done because the study was not recruiting quickly enough to support both the mirtazapine and the carbamazepine arms. The findings from this study show that mirtazapine should not be recommended to treat agitation in Alzheimer's disease. More work is needed to formulate effective ways and to test new drug and non-drug treatments for agitation in dementia.

An Unsupervised Data-Driven Anomaly Detection Approach for Adverse Health Conditions in People Living With Dementia: Cohort Study.

BACKGROUND: Sensor-based remote health monitoring can be used for the timely detection of health deterioration in people living with dementia with minimal impact on their day-to-day living. Anomaly detection approaches have been widely applied in various domains, including remote health monitoring. However, current approaches are challenged by noisy, multivariate data and low generalizability. OBJECTIVE: This study aims to develop an online, lightweight unsupervised learning-based approach to detect anomalies representing adverse health conditions using activity changes in people living with dementia. We demonstrated its effectiveness over state-of-the-art methods on a real-world data set of 9363 days collected from 15 participant households by the UK Dementia Research Institute between August 2019 and July 2021. Our approach was applied to household movement data to detect urinary tract infections (UTIs) and hospitalizations. METHODS: We propose and evaluate a solution based on Contextual Matrix Profile (CMP), an exact, ultrafast distance-based anomaly detection algorithm. Using daily aggregated household movement data collected via passive infrared sensors, we generated CMPs for location-wise sensor counts, duration, and change in hourly movement patterns for each patient. We computed a normalized anomaly score in 2 ways: by combining univariate CMPs and by developing a multidimensional CMP. The performance of our method was evaluated relative to Angle-Based Outlier Detection, Copula-Based Outlier Detection, and Lightweight Online Detector of Anomalies. We used the multidimensional CMP to discover and present the important features associated with adverse health conditions in people living with dementia. RESULTS: The multidimensional CMP yielded, on average, 84.3% recall with 32.1 alerts, or a 5.1% alert rate, offering the best balance of recall and relative precision compared with Copula-Based and Angle-Based Outlier Detection and Lightweight Online Detector of Anomalies when evaluated for UTI and hospitalization. Midnight to 6 AM bathroom activity was shown to be the most important cross-patient digital biomarker of anomalies indicative of UTI, contributing approximately 30% to the anomaly score. We also demonstrated how CMP-based anomaly scoring can be used for a cross-patient view of anomaly patterns. CONCLUSIONS: To the best of our knowledge, this is the first real-world study to adapt the CMP to continuous anomaly detection in a health care scenario. The CMP inherits the speed, accuracy, and simplicity of the Matrix Profile, providing configurability, the ability to denoise and detect patterns, and explainability to clinical practitioners. We addressed the need for anomaly scoring in multivariate time series health care data by developing the multidimensional CMP. With high sensitivity, a low alert rate, better overall performance than state-of-the-art methods, and the ability to discover digital biomarkers of anomalies, the CMP is a clinically meaningful unsupervised anomaly detection technique extensible to multimodal data for dementia and other health care scenarios.

Perspectives and challenges in patient stratification in Alzheimer's disease.

BACKGROUND: Patient stratification is the division of a patient population into distinct subgroups based on the presence or absence of particular disease characteristics. As patient stratification can be used to account for the underlying pathology of a disease, it can help physicians to tailor therapeutic interventions to individuals and optimize their care management and treatment regime. Alzheimer's disease, the most common form of dementia, is a heterogeneous disease and its management benefits from patient stratification in clinical trials, and the development of personalized care and treatment strategies for people living with the disease. MAIN BODY: In this review, we discuss the importance of the stratification of people living with Alzheimer's disease, the challenges associated with early diagnosis and patient stratification, and the evolution of patient stratification once disease-modifying therapies become widely available. CONCLUSION: Patient stratification plays an important role in drug development in clinical trials and may play an even larger role in clinical practice. A timely diagnosis and stratification of people living with Alzheimer's disease is paramount in determining people who are at risk of progressing from mild cognitive impairment to Alzheimer's dementia. There are key issues associated with stratifying patients which include the heterogeneity and complex neurobiology behind Alzheimer's disease, our inadequately prepared healthcare systems, and the cultural perceptions of Alzheimer's disease. Stratifying people living with Alzheimer's disease may be the key in establishing precision and personalized medicine in the field, optimizing disease prevention and pharmaceutical treatment to slow or stop cognitive decline, while minimizing adverse effects.