ABSTRACT Background There were between 84,891 and 113,139 all-cause excess deaths in the United States (US) from February 1 st to 25 th May 2020. These deaths are widely attributed directly and indirectly to the COVID-19 pandemic. This surge in death necessitates a matched health system response to relieve serious health related suffering at the end of life (EoL) and achieve a dignified death, through timely and appropriate expertise, medication and equipment. Identifying the human and material resource needed relies on modelling resource and understanding anticipated surges in demand. Methods A Discrete Event Simulation model designed in collaboration with health service funders, health providers, clinicians and modellers in the South West of England was created to estimate the resources required during the COVID-19 pandemic to care for deaths from COVID-19 in the community for a geographical area of nearly 1 million people. While our analysis focused on the UK setting, the model is flexible to changes in demand and setting. Results The model predicts that a mean of 11.97 hours (0.18 hours Standard Error (SE), up to a max of 28 hours) of additional community nurse time, up to 33 hours of care assistant time (mean 9.17 hours, 0.23 hours SE), and up to 30 hours additional care from care assistant night-sits (mean of 5.74 hours per day, 0.22 hours SE) will be required per day as a result of out of hospital COVID-19 deaths. Specialist palliative care demand is predicted to increase up to 19 hours per day (mean of 9.32 hours per day, 0.12 hours SE). An additional 286 anticipatory medicine bundles or ‘just in case’ prescriptions per month will be necessary to alleviate physical symptoms at the EoL care for patients with COVID-19: an average additional 10.21 bundles (0.06 SE) of anticipatory medication per day. An average additional 9.35 syringe pumps (0.11 SE) could be needed to be in use per day (between 1 and 20 syringe pumps). Conclusion Modelling provides essential data to prepare, plan and deliver a palliative care pandemic response tailored to local work patterns and resource. The analysis for a large region in the South West of England shows the significant additional physical and human resource required to relieve suffering at the EoL as part of a pandemic response. Why Was This Study Done? The resource required for the relief of suffering at the EoL in the community setting has been poorly described. The stark mortality resulting from the COVID-19 pandemic has highlighted the essential requirement to better understand the demand and available supply of EoL resource to prepare, plan and deliver a palliative care pandemic response. What Did the Researchers Do and Find? This manuscript describes the first open access model to describe EoL resource need during COVID-19 and presents an analysis based on a UK population of nearly 1 million people. The model identified a large increase in need for staff time, including registered community nurses, health care assistants and specialist palliative care nurses and doctors, as well as pressure on resources including syringe pumps and anticipatory medication (such as opioids) used at the EoL for symptom relief from breathlessness and delirium. What Do These Findings Mean? The model findings are critical in planning for a second wave of COVID-19. The open-access nature of the model allows researchers to tailor their analysis to low and middle income or high-income settings worldwide. The model ensures that EoL care is not an afterthought in pandemic planning, but an opportunity to ensure that the relief of suffering at the EoL is available to all.
Objectives Post acquired brain injury (ABI) depression has been implicated in different patient outcomes such as prospective cognition, cognitive impairment, rehabilitation outcome, and quality of life. However, there have been no studies identified in the literature, investigating post ABI insight into depression across varied cognitive abilities. Here we looked at ABI patient insight into their depression across a range of cognitive abilities and compared this to an observed or an objective measure of depression. Methods A retrospective cohort of 24 individuals with ABI (depressed and non-depressed) seen in a neuropsychiatry outpatient clinic between 2019 and 2020 completed a Patient Health Questionnaire-9 (PHQ-9), self-reported depression scale and had a Neuropsychiatry Inventory Questionnaire(NPI-Q), an observer assessment with a depression domain. The patients also underwent a formal cognitive examination using the Montreal Cognitive Assessment (MoCA). Results Non-depressed ABI and depressed ABI individuals with a wide range of cognitive abilities demonstrated good insight into their depression when matched to the observer rating. Chi-Square Test showed little variation between the PHQ-9 and NPI-Q Depression data sets; Wilcoxon Signed Ranks Test: Z Test -4.08, p<0.001, Effect Size 0.87 and Spearman’s rho showed positive correlation between the two data sets (Correlation Coefficient 0.527, P<0.008). Therefore, there was a statistically significant agreement between the subjective measure (PHQ-9) and the observed (objective) measure NPIQD and that there was a positive correlation between the two measurement scales for patients with ABI regardless of cognition (as measured by MoCAz score; range -6 to 2.21, mean: -1.17) Conclusions These findings indicate (1) self-reported measures of depression in ABI are consistent with observed (objective measures) thus can be used to assess depression in this cohort and (2) ABI patients with a wide range of cognitive abilities would appear to have good insight into their depression.
Background and aim 5-10% of emergency department (ED) presentations are primarily neurological. We investigated the impact of the introduction of an acute neurology service to the ED, using the same day emergency care (SDEC) model. Methods We performed a retrospective review of consecutive referrals to a consultant-led service at University College London Hospital during weekday afternoons from 5th May 2021 to 20th Jan 2022. Results Of 664 Neurology referrals, female sex was more common than male (60% vs 35.8%, p<0.0001, Fig. 1). Most referrals were from ED Majors (30%, Fig. 2). The most common presenting complaints were headache(n=187), weakness(n=34), dizziness(n=28), and numbness(n=26). Referrers’ working diagnoses included no diagnosis (n=69), unspecified headaches(n=62), migraines(n=42), and stroke(n=23) (Fig. 3). The most common diagnoses after Neurology review were migraines(n=160), vascular events(n=21), functional(n=16), and seizures(n=12) (Fig 4). Following review, working diagnosis changed in 307(46.2%), and the following planned actions were cancelled: hospital admission in 204(30.7%); stroke referral in 190(28.6%); imaging in 45 and lumbar puncture in 33. 170(25%) cases were fully managed in SDEC which would otherwise have followed the urgent 2-week-wait pathway. Conclusions Acute Neurology input in the ED had major impacts on working diagnoses, hospital admis- sions, urgent outpatient referrals, and emergency investigations.
An assessment was made of general symptoms in patients with psychogenic nonepileptic seizures (PNES), comparing those who do versus those who do not accept the diagnosis.A questionnaire pilot study of newly diagnosed psychogenic nonepileptic seizure patients confirmed by video electroencephalography (EEG) was carried out, using a 59-item general symptom questionnaire, with frequency (score) ranging from never (0) to every day (5). Subsequent blinded assessment of patient's acceptance of diagnosis was made.Of 13 patients studied, over a 5-month period, 8 accepted the diagnosis, and 5 did not. Acceptance of diagnosis was associated with a lower total symptom score (p < .001) and significantly lower symptom scores in 7 of the 10 symptom subscales.The underlying symptomatology of psychogenic nonepileptic seizure patients differs between those who do versus those who do not accept the diagnosis. The complexity of additional symptoms may contribute to poorer outcomes in those that do not accept the psychogenic nonepileptic seizure diagnosis.
Windowing is a common technique in EEG machine learning classification and other time series tasks. However, a challenge arises when employing this technique: computational expense inhibits learning global relationships across an entire recording or set of recordings. Furthermore, the labels inherited by windows from their parent recordings may not accurately reflect the content of that window in isolation. To resolve these issues, we introduce a multi-stage model architecture, incorporating meta-learning principles tailored to time-windowed data aggregation. We further tested two distinct strategies to alleviate these issues: lengthening the window and utilizing overlapping to augment data. Our methods, when tested on the Temple University Hospital Abnormal EEG Corpus (TUAB), dramatically boosted the benchmark accuracy from 89.8 percent to 99.0 percent. This breakthrough performance surpasses prior performance projections for this dataset and paves the way for clinical applications of machine learning solutions to EEG interpretation challenges. On a broader and more varied dataset from the Temple University Hospital EEG Corpus (TUEG), we attained an accuracy of 86.7%, nearing the assumed performance ceiling set by variable inter-rater agreement on such datasets.
Objectives The WHO estimates that the COVID-19 pandemic has led to more than 1.3 million deaths (1 377 395) globally (as of November 2020). This surge in death necessitates identification of resource needs and relies on modelling resource and understanding anticipated surges in demand. Our aim was to develop a generic computer model that could estimate resources required for end-of-life (EoL) care delivery during the pandemic. Setting A discrete event simulation model was developed and used to estimate resourcing needs for a geographical area in the South West of England. While our analysis focused on the UK setting, the model is flexible to changes in demand and setting. Participants We used the model to estimate resourcing needs for a population of around 1 million people. Primary and secondary outcome measures The model predicts the per-day ‘staff’ and ‘stuff’ resourcing required to meet a given level of incoming EoL care activity. Results A mean of 11.97 hours of additional community nurse time, up to 33 hours of care assistant time and up to 30 hours additional care from care assistant night sits will be required per day as a result of out of hospital COVID-19 deaths based on the model prediction. Specialist palliative care demand is predicted to increase up to 19 hours per day. An additional 286 anticipatory medicine bundles per month will be necessary to alleviate physical symptoms at the EoL care for patients with COVID-19: an average additional 10.21 bundles of anticipatory medication per day. An average additional 9.35 syringe pumps could be needed to be in use per day. Conclusions The analysis for a large region in the South West of England shows the significant additional physical and human resource required to relieve suffering at the EoL as part of a pandemic response.
Abstract The heartbeat-evoked potential (HEP), a cortical response time-locked to each heartbeat, is suggested as an implicit electrophysiological marker reflecting the cortical processing of heartbeats, and more broadly interoceptive processing. An increasing number of studies suggest that HEP may be a meaningful clinical measure. However, on the scalp, HEP are low amplitude signals that are mixed with direct cardiac field artefacts. Therefore, signal processing pipelines that separate the cortical HEP from cardiac field artefacts are required. With a view to establishing optimal and standardised HEP pipelines, this review aims to evaluate the current approaches to this analysis used within the literature, address gaps and inconsistencies in HEP pipelines, and highlight the impact of crucial parameter choices. To do this, a scoping review investigated current HEP processing methods and parameters used in EEG and MEG studies. Testing these processing methods on Temple University’s normal scalp EEG data (Obeid and Picone, 2016), the effect of different methods/parameters (e.g. HEP window, electrodes, filters, independent component analysis (ICA) and artefact subspace reconstruction (ASR)) on HEP extraction was explored. EEG studies (N=101) demonstrated greater parameter variability and heterogeneity than MEG studies (N=10), although significantly more studies used EEG. ICA without cardiac field artefact and ASR at threshold 20 exhibit similar results for artefact removal. Statistical analysis revealed that the RR interval, the start and end of the HEP window and the start of the baseline correction window significantly affect HEP values in pre-frontal, frontal and centrotemporal electrodes. Publications should report critical values for reliable HEP extraction, emphasising the need for standardised methods to enhance study comparison and reproducibility and establish a gold standard in the field.
Aims Comorbid anxiety and mood disorders occur in 30% and 60% of individuals post-ABI (acquired brain injury), respectively (Juengst et al, 2014). The presence of psychiatric symptoms correlate to poorer outcomes in post-stroke rehabilitation, worsened quality of life (QoL), and deficits in memory, attention, and processing speed that persists years following the index event. Despite this, it is unclear whether to what degree anxiety impacts cognition. Furthermore, the literature on this topic is inconsistent when comparing subjective and clinician measurements. This study seeks to ameliorate this gap in literature by analyzing how clinicians’ measures of anxiety and cognitive performance correlate with subjective assessments of patient's own anxiety symptoms. Method Individuals with an ABI who were seen in a clinical neuropsychiatry outpatient clinic between 2019 and 2020 completed a GAD-7 (Generalized Anxiety Disorder-7) questionnaire (patient's self-report of the severity of anxiety symptoms) and an observer completed a Neuropsychiatric Inventory Questionnaire (NPIQ) including a subscale for anxiety (NPIQ-A). Participants also underwent a formal cognitive examination with the Montreal Cognitive Assessment (MoCA). A total of 24 ABI patients (depressed ABI and non-depressed ABI) were analyzed for variation, statistical agreement and correlation. Here, total anxiety scores (using GAD-7 scores), anxiety severity (correlating category based on total GAD-7 score) were compared against the objective measures for anxiety (NPI-QA) and cognition (MoCA). In order to standardize MoCA scores, z scores were used in the statistical analysis. Result The patient's subjective raw scores of anxiety were statistically significantly different from the corresponding scores from objective observers on Wilcoxon-Rank Sum tests (p < 0.01), however, there was a statistical correlation between GAD (categorized by severity level) and NPI-QA (p = 0.75). Spearman Rank Correlation did show positive, but, statistically insignificant correlation between dyads of these independent variables (including GAD7/NPIQ-A, GAD 7 categorised/NPIQ-A, GAD7/MoCA, GAD 7 categorised/MoCA). Conclusion These findings indicate (1) self-reported measures of anxiety (GAD7) in ABI were inconsistent with objective measures of anxiety in this cohort, (2) anxiety measures did not demonstrate significant correlation when compared to objective measures for cognitive function, and (3) ABI patients did not display good insight into the severity of their anxiety symptoms as measured by the GAD7. Further research should focus on utilizing other subjective measurement tools for anxiety and/or clinician evaluation tools with NPIQ-A.
Machine learning classifiers for detection of abnormal clinical electroencephalography (EEG) signals have advanced signficantly in recent years, largely supported by the carefully curated Temple University Hospital Abnormal EEG Corpus (TUAB). Further progress towards clinically useful tools is likely to require larger volumes of data. In this study, we explore the viability and benefits of fully automated labelling of clinical EEG recordings based on the text in the clinical report, to efficiently exploit larger existing databases. We apply a machine learning classifier to the text reports in the Temple University Hospital EEG Corpus (TUEG) in order to label individual recordings. We show that training a deep convolutional neural network against the resulting dataset yields advantages in the resulting classification performance, namely increased area under the receiver operating characteristic curve and state-of-the-art specificity, albeit with a notable reduction in sensitivity. By demonstrating the viability of automatic report-based labelling, this paper opens the prospect of efficiently utilising the huge amount of historical EEG data in global medical archives to enhance the training of machine learning classifiers, either for enhanced general performance or bespoke training/evaluation for local populations.
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