IntroductionObservable dementia symptoms are hardly studied in people with severe/profound intellectual (and multiple) disabilities (SPI(M)D). Insight in symptomatology is needed for timely signaling/diagnosis. This study aimed to identify practice-based observations of dementia symptoms in this population.MethodsCare professionals and family members were invited to complete a survey about symptoms. Quantitatively analyzed survey data were further deepened through semi-structured interviews with care professionals having vast experience in signaling/diagnosing dementia in this population. Symptoms were categorized using a symptom matrix.ResultsSurvey respondents and interviewees frequently observed a decline in activities of daily living (ADL) functioning and behavioral and psychological changes, like increased irritability, anxiety, apathy and decreased eating/drinking behavior. Cognitive symptoms were particularly recognized in persons with verbal communication and/or walking skills. To lesser extent motor changes and medical comorbidities were reported.ConclusionIncreased insight in dementia symptoms contributes to developing a dedicated screening instrument for dementia in people with SPI(M)D.
Background: Medically unexplained symptoms (MUS) are highly prevalent and pose a burden both on patients and on health care. In a pilot study psychosomatic therapy delivered by specialised therapists for patients with MUS showed promising results with regard to patient’s acceptability, feasibility and effects on symptoms. The aim of this study is to establish whether psychosomatic therapy by specialised psychosomatic exercise therapists is costeffective in decreasing symptoms and improving functioning in patients who frequently consult their general practitioner (GP) with MUS. Methods: A randomised effectiveness trial with an economic evaluation in primary care with 158 patients aged 18 years and older who are frequently consulting their GP with MUS. Patients will be assigned to psychosomatic therapy in addition to usual care or usual care only. Psychosomatic therapy is a multi-component and tailored intervention, aiming to empower patients by applying psycho-education, relaxation techniques, mindfulness, cognitive approaches and/or graded activity. Patients assigned to the psychosomatic therapy receive 6 to 12 sessions of psychosomatic therapy, of 30–45 min each, delivered by a specialised exercise or physical therapist. Primary outcome measure is patient-specific functioning and disability, measured with the Patient-Specific Functional Scale (PSFS). Secondary outcome measures are symptom severity, consultation frequency and referrals to secondary care, patient satisfaction, quality of life and costs. Assessments will be carried out at baseline, and after 4 and 12 months. An economic evaluation alongside the trial will be conducted from a societal perspective, with quality-adjusted life years (QALYs) as outcome measure. Furthermore, a mixed-methods process evaluation will be conducted. Discussion: We expect that psychosomatic therapy in primary care for patients who frequently attend the GP for MUS will improve symptoms and daily functioning and disability, while reducing consultation frequency and referrals to secondary care. We expect that the psychosomatic therapy provides value for money for patients with MUS.Trial registration: Netherlands Trial Register, ID: NL7157 (NTR7356). Registered 13 July 2018.Keywords: Psychosomatic therapy, Study protocol, Primary care, Randomised controlled trial, Medically unexplained symptoms, Cost-effectiveness
MULTIFILE
Aim Life expectancy of people with severe or profound intellectual disability (SPID) increases, which contributes to the risk of developing dementia. However, early detection and diagnosing dementia is complex, because of their low-level baseline functioning. Therefore, the aim is to identify observable dementia symptoms in adults with SPID in available literature. Method A systematic literature search, in line with PRISMA guidelines, was conducted in PubMed, PsycINFO and Web of Science using a combination of search terms for SPID, dementia/aging and aged population.Results In total, fifteen studies met inclusion criteria. Cognitive, behavioral and psychological symptoms (BPSD) and a decline in the ability to perform activities of daily living as well as neurological and physical changes were found. This presentation gives an overview of reported symptoms of (possible) dementia-related symptoms in SPID. Conclusions Despite growing attention for dementia in people with ID in literature, only very few studies have studied dementia symptoms in SPID. Given the complexity of signaling and diagnosing dementia in SPID, dedicated studies are required to unravel the natural history of dementia in SPID, specifically focusing on observable symptoms for caregivers of (early) dementia in this population.
Observationele studie
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
