A patient flow simulator for healthcare management education

Simulation and analysis of patient flow can contribute to the safe and efficient functioning of a healthcare system, yet it is rarely incorporated into routine healthcare management, partially due to the technical training required. This paper introduces a free and open source patient flow simulation software tool that enables training and experimentation with healthcare management decisions and their impact on patient flow. Users manage their simulated hospital with a simple web-based graphical interface. The model is a stochastic discrete event simulation in which patients are transferred between wards of a hospital according to their treatment needs. Entry to each ward is managed by queues, with different policies for queue management and patient prioritisation per ward. Users can manage a simulated hospital, distribute resources between wards and decide how those resources should be prioritised. Simulation results are immediately available for analysis in-browser, including performance against targets, patient flow networks and ward occupancy. The patient flow simulator, freely available at https://khp-informatics.github.io/patient-flow-simulator, is an interactive educational tool that allows healthcare students and professionals to learn important concepts of patient flow and healthcare management

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials

Responsibility modelling for civil emergency planning

This paper presents a new approach to analysing and understanding civil emergency planning based on the notion of responsibility modelling combined with HAZOPS-style analysis of information requirements. Our goal is to represent complex contingency plans so that they can be more readily understood, so that inconsistencies can be highlighted and vulnerabilities discovered. In this paper, we outline the framework for contingency planning in the United Kingdom and introduce the notion of responsibility models as a means of representing the key features of contingency plans. Using a case study of a flooding emergency, we illustrate our approach to responsibility modelling and suggest how it adds value to current textual contingency plans

Ticks in the wrong boxes: assessing error in blanket-drag studies due to occasional sampling

BACKGROUND The risk posed by ticks as vectors of disease is typically assessed by blanket-drag sampling of host-seeking individuals. Comparisons of peak abundance between plots - either in order to establish their relative risk or to identify environmental correlates - are often carried out by sampling on one or two occasions during the period of assumed peak tick activity. METHODS This paper simulates this practice by 're-sampling' from model datasets derived from an empirical field study. Re-sample dates for each plot are guided by either the previous year's peak at the plot, or the previous year's peak at a similar, nearby plot. Results from single, double and three-weekly sampling regimes are compared. RESULTS Sampling on single dates within a two-month window of assumed peak activity has the potential to introduce profound errors; sampling on two dates (double sampling) offers greater precision, but three-weekly sampling is the least biased. CONCLUSIONS The common practice of sampling for the abundance of host-seeking ticks on single dates in each plot-year should be strenuously avoided; it is recommended that field acarologists employ regular sampling throughout the year at intervals no greater than three weeks, for a variety of epidemiological studies

Cross infection control measures and the treatment of patients at risk of Creutzfeldt Jakob disease in UK general dental practice

AIMS: To determine the suitability of key infection control measures currently employed in UK dental practice for delivery of dental care to patients at risk of prion diseases. MATERIALS AND METHODS: Subjects: Five hundred dental surgeons currently registered with the General Dental Council of the UK. Data collection: Structured postal questionnaire. Analysis: Frequencies, cross-tabulations and chi-squared analysis. RESULTS: The valid response rate to the questionnaire was 69%. 33% of practices had no policy on general disinfection and sterilisation procedures. Only 10 of the 327 responding practices (3%) possessed a vacuum autoclave. 49% of dentists reported using the BDA medical history form but less than 25% asked the specific questions recommended by the BDA to identify patients at risk of iatrogenic or familial CJD. However, 63% of practitioners would refer such patients, if identified, to a secondary care facility. Of the 107 practitioners who were prepared to provide dental treatment, 75 (70%) would do so using routine infection control procedures. CONCLUSIONS: Most of the dental practices surveyed were not actively seeking to identify patients at risk of prion diseases. In many cases, recommended procedures for providing safe dental care for such patients were not in place

ACE-inhibitors and Angiotensin-2 Receptor Blockers are not associated with severe SARS-COVID19 infection in a multi-site UK acute Hospital Trust

Aims: The SARS‐CoV‐2 virus binds to the angiotensin‐converting enzyme 2 (ACE2) receptor for cell entry. It has been suggested that angiotensin‐converting enzyme inhibitors (ACEi) and angiotensin II receptor blockers (ARB), which are commonly used in patients with hypertension or diabetes and may raise tissue ACE2 levels, could increase the risk of severe COVID‐19 infection. Methods and results: We evaluated this hypothesis in a consecutive cohort of 1200 acute inpatients with COVID‐19 at two hospitals with a multi‐ethnic catchment population in London (UK). The mean age was 68 ± 17 years (57% male) and 74% of patients had at least one comorbidity. Overall, 415 patients (34.6%) reached the primary endpoint of death or transfer to a critical care unit for organ support within 21 days of symptom onset. A total of 399 patients (33.3%) were taking ACEi or ARB. Patients on ACEi/ARB were significantly older and had more comorbidities. The odds ratio for the primary endpoint in patients on ACEi and ARB, after adjustment for age, sex and co‐morbidities, was 0.63 (95% confidence interval 0.47–0.84, P < 0.01). Conclusions: There was no evidence for increased severity of COVID‐19 in hospitalised patients on chronic treatment with ACEi or ARB. A trend towards a beneficial effect of ACEi/ARB requires further evaluation in larger meta‐analyses and randomised clinical trials

Cerebellar Integrity in the Amyotrophic Lateral Sclerosis - Frontotemporal Dementia Continuum

Amyotrophic lateral sclerosis (ALS) and behavioural variant frontotemporal dementia (bvFTD) are multisystem neurodegenerative disorders that manifest overlapping cognitive, neuropsychiatric and motor features. The cerebellum has long been known to be crucial for intact motor function although emerging evidence over the past decade has attributed cognitive and neuropsychiatric processes to this structure. The current study set out i) to establish the integrity of cerebellar subregions in the amyotrophic lateral sclerosis-behavioural variant frontotemporal dementia spectrum (ALS-bvFTD) and ii) determine whether specific cerebellar atrophy regions are associated with cognitive, neuropsychiatric and motor symptoms in the patients. Seventy-eight patients diagnosed with ALS, ALS-bvFTD, behavioural variant frontotemporal dementia (bvFTD), most without C9ORF72 gene abnormalities, and healthy controls were investigated. Participants underwent cognitive, neuropsychiatric and functional evaluation as well as structural imaging using voxel-based morphometry (VBM) to examine the grey matter subregions of the cerebellar lobules, vermis and crus. VBM analyses revealed: i) significant grey matter atrophy in the cerebellum across the whole ALS-bvFTD continuum; ii) atrophy predominantly of the superior cerebellum and crus in bvFTD patients, atrophy of the inferior cerebellum and vermis in ALS patients, while ALS-bvFTD patients had both patterns of atrophy. Post-hoc covariance analyses revealed that cognitive and neuropsychiatric symptoms were particularly associated with atrophy of the crus and superior lobule, while motor symptoms were more associated with atrophy of the inferior lobules. Taken together, these findings indicate an important role of the cerebellum in the ALS-bvFTD disease spectrum, with all three clinical phenotypes demonstrating specific patterns of subregional atrophy that associated with different symptomology

Efficacy of exposure versus cognitive therapy in anxiety disorders: systematic review and meta-analysis

<p>Abstract</p> <p>Background</p> <p>There is growing evidence of the effectiveness of Cognitive Behavioural Therapy (CBT) for a wide range of psychological disorders. There is a continued controversy about whether challenging maladaptive thoughts rather than use of behavioural interventions alone is associated with the greatest efficacy. However little is known about the relative efficacy of various components of CBT. This review aims to compare the relative efficacy of Cognitive Therapy (CT) versus Exposure (E) for a range of anxiety disorders using the most clinically relevant outcome measures and estimating the summary relative efficacy by combining the studies in a meta-analysis.</p> <p>Methods</p> <p>Psych INFO, MEDLINE and EMBASE were searched from the first available year to May 2010. All randomised controlled studies comparing the efficacy of exposure with cognitive therapy were included. Odds ratios (OR) or standardised means' differences (Hedges' g) for the most clinically relevant primary outcomes were calculated. Outcomes of the studies were grouped according to specific disorders and were combined in meta-analyses exploring short-term and long-term outcomes.</p> <p>Results</p> <p>20 Randomised Controlled Trials with (n = 1,308) directly comparing the efficacy of CT and E in anxiety disorders were included in the meta-analysis. No statistically significant difference in the relative efficacy of CT and E was revealed in Post Traumatic Stress Disorder (PTSD), in Obsessive Compulsive Disorder (OCD) and in Panic Disorder (PD). There was a statistically significant difference favouring CT versus E in Social Phobia both in the short-term (Z = 3.72, p = 0.0002) and the long-term (Z = 3.28, p = 0.001) outcomes.</p> <p>Conclusions</p> <p>On the basis of extant literature, there appears to be no evidence of differential efficacy between cognitive therapy and exposure in PD, PTSD and OCD and strong evidence of superior efficacy of cognitive therapy in social phobia</p

Prediction of peptide and protein propensity for amyloid formation

Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔGº values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation