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Real-world effectiveness, its predictors and onset of action of cholinesterase inhibitors and memantine in dementia: retrospective health record study.
BACKGROUND: The efficacy of acetylcholinesterase inhibitors and memantine in the symptomatic treatment of Alzheimer's disease is well-established. Randomised trials have shown them to be associated with a reduction in the rate of cognitive decline. AIMS: To investigate the real-world effectiveness of acetylcholinesterase inhibitors and memantine for dementia-causing diseases in the largest UK observational secondary care service data-set to date. METHOD: We extracted mentions of relevant medications and cognitive testing (Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores) from de-identified patient records from two National Health Service (NHS) trusts. The 10-year changes in cognitive performance were modelled using a combination of generalised additive and linear mixed-effects modelling. RESULTS: The initial decline in MMSE and MoCA scores occurs approximately 2 years before medication is initiated. Medication prescription stabilises cognitive performance for the ensuing 2-5 months. The effect is boosted in more cognitively impaired cases at the point of medication prescription and attenuated in those taking antipsychotics. Importantly, patients who are switched between agents at least once do not experience any beneficial cognitive effect from pharmacological treatment. CONCLUSIONS: This study presents one of the largest real-world examination of the efficacy of acetylcholinesterase inhibitors and memantine for symptomatic treatment of dementia. We found evidence that 68% of individuals respond to treatment with a period of cognitive stabilisation before continuing their decline at the pre-treatment rate.
Validation of Plasma Proteomic Biomarkers Relating to Brain Amyloid Burden in the EMIF-Alzheimer's Disease Multimodal Biomarker Discovery Cohort.
We have previously investigated, discovered, and replicated plasma protein biomarkers for use to triage potential trials participants for PET or cerebrospinal fluid measures of Alzheimer's disease (AD) pathology. This study sought to undertake validation of these candidate plasma biomarkers in a large, multi-center sample collection. Targeted plasma analyses of 34 proteins with prior evidence for prediction of in vivo pathology were conducted in up to 1,000 samples from cognitively healthy elderly individuals, people with mild cognitive impairment, and in patients with AD-type dementia, selected from the EMIF-AD catalogue. Proteins were measured using Luminex xMAP, ELISA, and Meso Scale Discovery assays. Seven proteins replicated in their ability to predict in vivo amyloid pathology. These proteins form a biomarker panel that, along with age, could significantly discriminate between individuals with high and low amyloid pathology with an area under the curve of 0.74. The performance of this biomarker panel remained consistent when tested in apolipoprotein E ɛ4 non-carrier individuals only. This blood-based panel is biologically relevant, measurable using practical immunocapture arrays, and could significantly reduce the cost incurred to clinical trials through screen failure.
Maximizing the use of social and behavioural information from secondary care mental health electronic health records.
PURPOSE: The contribution of social and behavioural factors in the development of mental health conditions and treatment effectiveness is widely supported, yet there are weak population level data sources on social and behavioural determinants of mental health. Enriching these data gaps will be crucial to accelerating precision medicine. Some have suggested the broader use of electronic health records (EHR) as a source of non-clinical determinants, although social and behavioural information are not systematically collected metrics in EHRs, internationally. OBJECTIVE: In this commentary, we highlight the nature and quality of key available structured and unstructured social and behavioural data using a case example of value counts from secondary mental health data available in the UK from the UK Clinical Record Interactive Search (CRIS) database; highlight the methodological challenges in the use of such data; and possible solutions and opportunities involving the use of natural language processing (NLP) of unstructured EHR text. CONCLUSIONS: Most structured non-clinical data fields within secondary care mental health EHR data have too much missing data for adequate use. The utility of other non-clinical fields reported semi-consistently (e.g., ethnicity and marital status) is entirely dependent on treating them appropriately in analyses, quantifying the many reasons behind missingness in consideration of selection biases. Advancements in NLP offer new opportunities in the exploitation of unstructured text from secondary care EHR data particularly given that clinical notes and attachments are available in large volumes of patients and are more routinely completed by clinicians. Tackling ways to re-use, harmonize, and improve our existing and future secondary care mental health data, leveraging advanced analytics such as NLP is worth the effort in an attempt to fill the data gap on social and behavioural contributors to mental health conditions and will be necessary to fulfill all of the domains needed to inform personalized interventions.
Natural language processing for structuring clinical text data on depression using UK-CRIS.
BACKGROUND: Utilisation of routinely collected electronic health records from secondary care offers unprecedented possibilities for medical science research but can also present difficulties. One key issue is that medical information is presented as free-form text and, therefore, requires time commitment from clinicians to manually extract salient information. Natural language processing (NLP) methods can be used to automatically extract clinically relevant information. OBJECTIVE: Our aim is to use natural language processing (NLP) to capture real-world data on individuals with depression from the Clinical Record Interactive Search (CRIS) clinical text to foster the use of electronic healthcare data in mental health research. METHODS: We used a combination of methods to extract salient information from electronic health records. First, clinical experts define the information of interest and subsequently build the training and testing corpora for statistical models. Second, we built and fine-tuned the statistical models using active learning procedures. FINDINGS: Results show a high degree of accuracy in the extraction of drug-related information. Contrastingly, a much lower degree of accuracy is demonstrated in relation to auxiliary variables. In combination with state-of-the-art active learning paradigms, the performance of the model increases considerably. CONCLUSIONS: This study illustrates the feasibility of using the natural language processing models and proposes a research pipeline to be used for accurately extracting information from electronic health records. CLINICAL IMPLICATIONS: Real-world, individual patient data are an invaluable source of information, which can be used to better personalise treatment.
Preventing intrusive memories after trauma via a brief intervention involving Tetris computer game play in the emergency department: a proof-of-concept randomized controlled trial.
After psychological trauma, recurrent intrusive visual memories may be distressing and disruptive. Preventive interventions post trauma are lacking. Here we test a behavioural intervention after real-life trauma derived from cognitive neuroscience. We hypothesized that intrusive memories would be significantly reduced in number by an intervention involving a computer game with high visuospatial demands (Tetris), via disrupting consolidation of sensory elements of trauma memory. The Tetris-based intervention (trauma memory reminder cue plus c. 20 min game play) vs attention-placebo control (written activity log for same duration) were both delivered in an emergency department within 6 h of a motor vehicle accident. The randomized controlled trial compared the impact on the number of intrusive trauma memories in the subsequent week (primary outcome). Results vindicated the efficacy of the Tetris-based intervention compared with the control condition: there were fewer intrusive memories overall, and time-series analyses showed that intrusion incidence declined more quickly. There were convergent findings on a measure of clinical post-trauma intrusion symptoms at 1 week, but not on other symptom clusters or at 1 month. Results of this proof-of-concept study suggest that a larger trial, powered to detect differences at 1 month, is warranted. Participants found the intervention easy, helpful and minimally distressing. By translating emerging neuroscientific insights and experimental research into the real world, we offer a promising new low-intensity psychiatric intervention that could prevent debilitating intrusive memories following trauma.
Sleep and intrusive memories immediately after a traumatic event in emergency department patients.
STUDY OBJECTIVES: Intrusive memories of psychological trauma are a core clinical feature of posttraumatic stress disorder (PTSD), and in the early period post-trauma may be a potential target for early intervention. Disrupted sleep in the weeks post-trauma is associated with later PTSD. The impact of sleep and intrusive memories immediately post-trauma, and their relation to later PTSD, is unknown. This study assessed the relationship between sleep duration on the first night following a real-life traumatic event and intrusive memories in the subsequent week, and how these might relate to PTSD symptoms at 2 months. METHODS: Patients (n = 87) recruited in the emergency department completed a sleep and intrusive memory diary from the day of their trauma and for the subsequent week, with optional actigraphy. PTSD, anxiety, and depression symptoms were assessed at 1 week and 2 months. RESULTS: A U-shaped relationship was observed between sleep duration on the first night and intrusive memories over the subsequent week: sleeping "too little" or "too much" was associated with more intrusive memories. Individuals who met Clinician-Administered PTSD Scale (CAPS) criteria for PTSD at 2 months had three times more intrusive memories in the first week immediately post-trauma than those who did not (M = 28.20 vs 9.96). Post hoc analysis showed that the absence of intrusive memories in the first week post-trauma was only observed in those who did not meet CAPS criteria for PTSD at 2 months. CONCLUSIONS: Monitoring intrusive memories and sleep in the first week post-trauma, using a simple diary, may help identify individuals more vulnerable to later psychopathology.
Imagery-Focused Cognitive Therapy (ImCT) for Mood Instability and Anxiety in a Small Sample of Patients with Bipolar Disorder: a Pilot Clinical Audit.
BACKGROUND: Despite the global impact of bipolar disorder (BD), treatment success is limited. Challenges include syndromal and subsyndromal mood instability, comorbid anxiety, and uncertainty around mechanisms to target. The Oxford Mood Action Psychology Programme (OxMAPP) offered a novel approach within a cognitive behavioural framework, via mental imagery-focused cognitive therapy (ImCT). AIMS: This clinical audit evaluated referral rates, clinical outcomes and patient satisfaction with the OxMAPP service. METHOD: Eleven outpatients with BD received ImCT in addition to standard psychiatric care. Mood data were collected weekly from 6 months pre-treatment to 6 months post-treatment via routine mood monitoring. Anxiety was measured weekly from start of treatment until 1 month post-treatment. Patient feedback was provided via questionnaire. RESULTS: Referral and treatment uptake rates indicated acceptability to referrers and patients. From pre- to post-treatment, there was (i) a significant reduction in the duration of depressive episode relapses, and (ii) a non-significant trend towards a reduction in the number of episodes, with small to medium effect size. There was a large effect size for the reduction in weekly anxiety symptoms from assessment to 1 month follow-up. Patient feedback indicated high levels of satisfaction with ImCT, and underscored the importance of the mental imagery focus. CONCLUSIONS: This clinical audit provides preliminary evidence that ImCT can help improve depressive and anxiety symptoms in BD as part of integrated clinical care, with high patient satisfaction and acceptability. Formal assessment designs are needed to further test the feasibility and efficacy of the new ImCT treatment on anxiety and mood instability.
The wh-questions of network meta-analyses
© 2019 The Author. Currently, network meta-analyses (NMAs) are the only technique that allow us to compare and rank numerous treatments across trials. Evidence produced by NMAs relies on pooled data from both direct and indirect comparisons within studies. Consequently, NMAs are invaluable tools for informing clinical guidelines.
Systemic and cerebrospinal fluid immune and complement activation in Ugandan children and adolescents with long-standing nodding syndrome: A case-control study
© 2021 The Authors. Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy Objective: Nodding syndrome is a poorly understood epileptic encephalopathy characterized by a unique seizure type—head nodding—and associated with Onchocerca volvulus infection. We hypothesized that altered immune activation in the cerebrospinal fluid (CSF) and plasma of children with nodding syndrome may yield insights into the pathophysiology and progression of this seizure disorder. Method: We conducted a case-control study of 154 children (8 years or older) with long-standing nodding syndrome and 154 healthy age-matched community controls in 3 districts of northern Uganda affected by nodding syndrome. Control CSF samples were obtained from Ugandan children in remission from hematological malignancy during routine follow-up. Markers of immune activation and inflammation (cytokines and chemokines) and complement activation (C5a) were measured in plasma and CSF using ELISA or Multiplex Luminex assays. O volvulus infection was assessed by serology for anti–OV-16 IgG levels. Results: The mean (SD) age of the population was 15.1 (SD: 1.9) years, and the mean duration of nodding syndrome from diagnosis to enrollment was 8.3 (SD: 2.7) years. The majority with nodding syndrome had been exposed to O volvulus (147/154 (95.4%)) compared with community children (86/154 (55.8%)), with an OR of 17.04 (95% CI: 7.33, 45.58), P <.001. C5a was elevated in CSF of children with nodding syndrome compared to controls (P <.0001). The levels of other CSF markers tested were comparable between cases and controls after adjusting for multiple comparisons. Children with nodding syndrome had lower plasma levels of IL-10, APRIL, CCL5 (RANTES), CCL2, CXCL13, and MMP-9 compared with community controls (P <.05 for all; multiple comparisons). Plasma CRP was elevated in children with nodding syndrome compared to community children and correlated with disease severity. Significance: Nodding syndrome is associated with exposure to O. volvulus. Compared to controls, children with long-standing symptoms of nodding syndrome show evidence of complement activation in CSF and altered immune activation in plasma.
Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome.
Tools from the field of graph signal processing, in particular the graph Laplacian operator, have recently been successfully applied to the investigation of structure-function relationships in the human brain. The eigenvectors of the human connectome graph Laplacian, dubbed "connectome harmonics", have been shown to relate to the functionally relevant resting-state networks. Whole-brain modelling of brain activity combines structural connectivity with local dynamical models to provide insight into the large-scale functional organization of the human brain. In this study, we employ the graph Laplacian and its properties to define and implement a large class of neural activity models directly on the human connectome. These models, consisting of systems of stochastic integrodifferential equations on graphs, are dubbed graph neural fields, in analogy with the well-established continuous neural fields. We obtain analytic predictions for harmonic and temporal power spectra, as well as functional connectivity and coherence matrices, of graph neural fields, with a technique dubbed CHAOSS (shorthand for Connectome-Harmonic Analysis Of Spatiotemporal Spectra). Combining graph neural fields with appropriate observation models allows for estimating model parameters from experimental data as obtained from electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI). As an example application, we study a stochastic Wilson-Cowan graph neural field model on a high-resolution connectome graph constructed from diffusion tensor imaging (DTI) and structural MRI data. We show that the model equilibrium fluctuations can reproduce the empirically observed harmonic power spectrum of resting-state fMRI data, and predict its functional connectivity, with a high level of detail. Graph neural fields natively allow the inclusion of important features of cortical anatomy and fast computations of observable quantities for comparison with multimodal empirical data. They thus appear particularly suitable for modelling whole-brain activity at mesoscopic scales, and opening new potential avenues for connectome-graph-based investigations of structure-function relationships.