GPs' views on emergency care treatment plans: an online survey.

BACKGROUND: A holistic approach to emergency care treatment planning is needed to ensure that patients' preferences are considered should their clinical condition deteriorate. To address this, emergency care and treatment plans (ECTPs) have been introduced. Little is known about their use in general practice. AIM: To find out GPs' experiences of, and views on, using ECTPs. DESIGN & SETTING: Online survey of GPs practising in England. METHOD: A total of 841 GPs were surveyed using the monthly online survey provided by medeConnect, a market research company. RESULTS: Forty-one per cent of responders' practices used Recommended Summary Plan for Emergency Care and Treatment (ReSPECT) plans for ECTP, 8% used other ECTPs, and 51% used Do Not Attempt Cardiopulmonary Resuscitation (DNACPR) forms. GPs were the predominant professional group completing ECTPs in the community. There was broad support for a wider range of community-based health and social care professionals being able to complete ECTPs. There was no system for reviewing ECTPs in 20% of responders' practices. When compared with using a DNACPR form, GPs using a ReSPECT form for ECTP were more comfortable having conversations about emergency care treatment with patients (odds ratio [OR] = 1.72, 95% confidence interval [CI] = 1.1 to 2.69) and family members (OR =1.85, 95% CI = 1.19 to 2.87). CONCLUSION: The potential benefits and challenges of widening the pool of health and social care professionals initiating and/or completing the ECTP process needs consideration. ReSPECT plans appear to make GPs more comfortable with ECTP discussions, supporting their implementation. Practice-based systems for reviewing ECTP decisions should be strengthened.

A statistical algorithm for outbreak detection in multisite settings: an application to sick leave monitoring.

Motivation: Public health authorities monitor cases of health-related problems over time using surveillance algorithms that detect unusually high increases in the number of cases, namely aberrations. Statistical aberrations signal outbreaks when further investigation reveals epidemiological significance. The increasing availability and diversity of epidemiological data and the most recent epidemic threats call for more accurate surveillance algorithms that not just detect aberration times but also detect locations. Sick leave data, for instance, can be monitored across companies to identify companies-related aberrations. In this context, we develop an extension to multisite surveillance of a routinely used aberration detection algorithm, the quasi-Poisson regression Farrington Flexible algorithm. The new algorithm consists of a negative-binomial mixed effects regression model with a random effects term for sites and a new reweighting procedure reducing the effect of past aberrations. Results: A wide range of simulations shows that, compared with Farrington Flexible, the new algorithm produces better false positive rates and similar probabilities of detecting genuine outbreaks, for case counts that exceed historical baselines by 3 SD. As expected, higher surges lead to lower false positive rates and higher probabilities of detecting true outbreaks. The new algorithm provides better detection of true outbreaks, reaching 100%, when cases exceed eight baseline standard deviations. We apply our algorithm to sick leave rates in the context of COVID-19 and find that it detects the pandemic effect. The new algorithm is easily implementable over a range of contrasting data scenarios, providing good overall performance and new perspectives for multisite surveillance. Availability and implementation: All the analyses are performed in the R statistical software using the package glmmTMB. The code for performing the analyses and for generating the simulations can be found online at the following link: https://github.com/TomDuchemin/mixed_surveillance. Contact: a.noufaily@warwick.ac.uk.

Study of capsule endoscopy delivery at scale through enhanced artificial intelligence-enabled analysis (the CESCAIL study).

AIM: Lower gastrointestinal (GI) diagnostics have been facing relentless capacity constraints for many years, even before the COVID-19 era. Restrictions from the COVID pandemic have resulted in a significant backlog in lower GI diagnostics. Given recent developments in deep neural networks (DNNs) and the application of artificial intelligence (AI) in endoscopy, automating capsule video analysis is now within reach. Comparable to the efficiency and accuracy of AI applications in small bowel capsule endoscopy, AI in colon capsule analysis will also improve the efficiency of video reading and address the relentless demand on lower GI services. The aim of the CESCAIL study is to determine the feasibility, accuracy and productivity of AI-enabled analysis tools (AiSPEED) for polyp detection compared with the 'gold standard': a conventional care pathway with clinician analysis. METHOD: This multi-centre, diagnostic accuracy study aims to recruit 674 participants retrospectively and prospectively from centres conducting colon capsule endoscopy (CCE) as part of their standard care pathway. After the study participants have undergone CCE, the colon capsule videos will be uploaded onto two different pathways: AI-enabled video analysis and the gold standard conventional clinician analysis pathway. The reports generated from both pathways will be compared for accuracy (sensitivity and specificity). The reading time can only be compared in the prospective cohort. In addition to validating the AI tool, this study will also provide observational data concerning its use to assess the pathway execution in real-world performance. RESULTS: The study is currently recruiting participants at multiple centres within the United Kingdom and is at the stage of collecting data. CONCLUSION: This standard diagnostic accuracy study carries no additional risk to patients as it does not affect the standard care pathway, and hence patient care remains unaffected.

Prospective, randomised, parallel-group, open-label study to evaluate the effectiveness and safety of IMU-838, in combination with oseltamivir, in adults with COVID-19: the IONIC trial protocol.

BACKGROUND: Globally, there is a scarcity of effective treatments for SARS-CoV-2 infections (causing COVID-19). Repurposing existing medications may offer the best hope for treating patients with COVID-19 to curb the pandemic. IMU-838 is a dihydroorotate dehydrogenase inhibitor, which is an effective mechanism for antiviral effects against respiratory viruses. When used synergistically with oseltamivir, therapeutic effects have been observed against influenza and SARS-CoV-2 in rodents. The IMU-838 and Oseltamivir in the Treatment of COVID-19 (IONIC) trial is a randomised controlled trial that will investigate whether time to clinical improvement in patients with COVID-19 is improved following a 14-day course of IMU-838+oseltamivir versus oseltamivir alone. METHODS: IONIC trial is an open-label study in which participants will be randomised 1:1 in two parallel arms: the intervention arm (IMU-838+oseltamivir) and the control arm (oseltamivir only). The primary outcome is time to clinical improvement; defined as the time from randomisation to a two-point improvement on WHO ordinal scale; discharge from hospital, or death (whichever occurs first). The study is sponsored by the University Hospitals Coventry and Warwickshire NHS Trust and funded by LifeArc. DISCUSSION: The IONIC protocol describes an overarching trial design to provide reliable evidence on the effectiveness of IMU-838 (vidofludimus calcium) when delivered in combination with an antiviral therapy (oseltamivir) (IONIC intervention) for confirmed or suspected COVID-19 infection in adult patients receiving usual standard of care. ETHICS AND DISSEMINATION: This study has been independently reviewed and approved by Wales Research Ethics Committee. In addition, required regulatory approvals were received from Medicines and Healthcare products Regulatory Agency. TRIAL REGISTRATION NUMBER: EudraCT 2020-001805-21, ISRCTN53038326, NCT04516915.

Evaluating the relationship between moral values and vaccine hesitancy in Great Britain during the COVID-19 pandemic: A cross-sectional survey.

RATIONAL/OBJECTIVE: Mandating vaccinations can harm public trust, and informational interventions can backfire. An alternative approach could align pro-vaccination messages with the automatic moral values and intuitions that vaccine-hesitant people endorse. The current study evaluates the relationships between six automatic moral intuitions and vaccine hesitancy. METHODS: A cross-sectional survey was designed using Qualtrics (2020) software and conducted online from April 6th to April 13, 2021. A representative sample of 1201 people living in Great Britain took part, of which 954 (514 female) passed the attention check items. Participants responded to items about their automatic moral intuitions, vaccination behaviours or intentions related to COVID-19 vaccines, and general vaccine hesitancy. Regressions (with and without adjustments for age, gender, and ethnicity) were performed assessing the association between endorsement of each automatic intuition and self-reported uptake of COVID-19 vaccines, and between each automatic intuition and general vaccine hesitancy. RESULTS: People who endorsed the authority foundation and those who more strongly endorsed the liberty foundation tended to be more vaccine hesitant. This pattern generalises across people's self-reported uptake of COVID-19 vaccines and people's hesitancy towards vaccines in general. To a lesser extent people who expressed less need for care and a greater need for sanctity also displayed greater hesitancy towards vaccines in general. The results were consistent across the adjusted and non-adjusted analyses. Age and ethnicity significantly contributed to some models but gender did not. CONCLUSION: Four automatic moral intuitions (authority, liberty, care, and sanctity) were significantly associated with vaccine hesitancy. Foundation-aligned messages could be developed to motivate those people who may otherwise refuse vaccines, e.g., messages that strongly promote liberty or that de-emphasize authority voices. This suggestion moves away from mandates and promotes the inclusion of a more diverse range of voices in pro-vaccination campaigns.

Role of methylated septin 9 as an adjunct diagnostic and prognostic biomarker in hepatocellular carcinoma.

BACKGROUND: Methylated septin 9 (mSEPT9) has a role in hepatocarcinogenesis. We evaluated mSEPT9 performance in patients with hepatocellular carcinoma (HCC) and those at risk of HCC METHODS: Using Epi-proColon® V2.0 assay adapted for 1 mL plasma, we investigated mSEPT9 sensitivity, specificity, associations with influential covariates and relation to death. RESULTS: Of 141 participants included, 136 had liver disease, 38 with HCC (mean-age 71 years) and 103 without HCC (mean-age 56.8 years), with further five without liver disease. 41 patients died (23 HCC) by the end of the study follow-up period. In HCC, mSEPT9 sensitivity and specificity were 89.47% (CI:75.20%-97.06%) and 81.55% (CI:72.70%-88.51%), whilst alpha fetoprotein (AFP) sensitivity and specificity were 50% (CI:33.38%-66.62%) and 97.09% (CI:91.72%-99.40%), respectively. Age-adjusted logistic regression showed mSEPT9 was associated with age, body mass index, HCC, liver cirrhosis, AFP, platelets, neutrophil-to-lymphocyte-ratio, albumin-bilirubin grade and fibrosis-4 index (p < 0.05). Odds for HCC patients to have positive mSEPT9 were 27.4 times more than those without HCC. Time-to-death was associated with mSEPT9 positivity (p < 0.05). Kaplan-Meier curves showed higher HCC survival with mSEPT9 compared to AFP. CONCLUSIONS: The mSEPT9 offers potential diagnostic and prognostic biomarker for HCC. After adjusting for age, mSEPT9 remained associated with liver function, liver fibrosis and inflammatory surrogate markers.

C-reactive protein and albumin association with mortality of hospitalised SARS-CoV-2 patients: A tertiary hospital experience.

OBJECTIVE: The objective was to study hospitalised COVID-19 patients' mortality and intensive care unit (ICU) admission with covariates of interest (age, gender, ethnicity, clinical presentation, comorbidities and admission laboratory findings). METHODS: Logistic regression analyses were performed for patients admitted to University Hospital, University Hospitals Coventry and Warwickshire NHS Trust, between 24 January 2020 - 13 April 2020. RESULTS: There were 321 patients hospitalised. Median age was 73 years and 189 (59%) were male. Ethnicity was divided between Caucasian (77%), and black, Asian, and minority ethnic (BAME) groups (23%). Commonest symptoms were dyspnoea (62.9%), fever (59.1%) and cough (56%). Gastrointestinal symptoms amounted to 11.8%.Forty-four patients (13.7%) received ICU care. ICU male to female ratio was 3:1 (p=0.027; odds ratio (OR) 2.3; 95% confidence interval (CI) 1.1-4.9), BAME (p=0.008; OR 2.5; 95% CI 1.3-4.9), age >65 years (p=0.026; OR 0.28; 95% CI 0.09-0.93), heart disease (p=0.009; OR 0.2; 95% CI 0.1-0.6) and elevated C-reactive protein (CRP; p<0.001; OR 1.004; 95% CI 1.002-1.008) were associated with ICU admission.One-hundred and four patients (32.4%) died. Age >65 years (p=0.011; OR 5; 95% CI 1.6-21.9), neutrophils (p=0.047), neutrophil:lymphocyte ratio (NLR; p=0.028), CRP (p<0.001) and albumin (p=0.002) were associated with mortality. When analysis adjusted for age, CRP (p<0.001; OR 1.006; 95% CI 1.004-1.008) and albumin (p=0.005; OR 0.94; 95% CI 0.90-0.98) remained associated with mortality. CONCLUSIONS: COVID-19 has high mortality. BAME and male patients were associated with ICU admission. High CRP and low albumin (after correcting for age) were associated with mortality.

A bivariate power generalized Weibull distribution: A flexible parametric model for survival analysis.

We are concerned with the flexible parametric analysis of bivariate survival data. Elsewhere, we argued in favour of an adapted form of the 'power generalized Weibull' distribution as an attractive vehicle for univariate parametric survival analysis. Here, we additionally observe a frailty relationship between a power generalized Weibull distribution with one value of the parameter which controls distributional choice within the family and a power generalized Weibull distribution with a smaller value of that parameter. We exploit this relationship to propose a bivariate shared frailty model with power generalized Weibull marginal distributions linked by the BB9 or 'power variance function' copula, then change it to have adapted power generalized Weibull marginals in the obvious way. The particular choice of copula is, therefore, natural in the current context, and the corresponding bivariate adapted power generalized Weibull model a novel combination of pre-existing components. We provide a number of theoretical properties of the models. We also show the potential of the bivariate adapted power generalized Weibull model for practical work via an illustrative example involving a well-known retinopathy dataset, for which the analysis proves to be straightforward to implement and informative in its outcomes.

Exercise or manual physiotherapy compared with a single session of physiotherapy for osteoporotic vertebral fracture: three-arm PROVE RCT.

BACKGROUND: A total of 25,000 people in the UK have osteoporotic vertebral fracture (OVF). Evidence suggests that physiotherapy may have an important treatment role. OBJECTIVE: The objective was to investigate the clinical effectiveness and cost-effectiveness of two different physiotherapy programmes for people with OVF compared with a single physiotherapy session. DESIGN: This was a prospective, adaptive, multicentre, assessor-blinded randomised controlled trial (RCT) with nested qualitative and health economic studies. SETTING: This trial was based in 21 NHS physiotherapy departments. PARTICIPANTS: The participants were people with symptomatic OVF. INTERVENTIONS: Seven sessions of either manual outpatient physiotherapy or exercise outpatient physiotherapy compared with the best practice of a 1-hour single session of physiotherapy (SSPT). MAIN OUTCOME MEASURES: Outcomes were measured at 4 and 12 months. The primary outcomes were quality of life and muscle endurance, which were measured by the disease-specific QUALEFFO-41 (Quality of Life Questionnaire of the European Foundation for Osteoporosis - 41 items) and timed loaded standing (TLS) test, respectively. Secondary outcomes were (1) thoracic kyphosis angle, (2) balance, evaluated via the functional reach test (FRT), and (3) physical function, assessed via the Short Physical Performance Battery (SPPB), 6-minute walk test (6MWT), Physical Activity Scale for the Elderly, a health resource use and falls diary, and the EuroQol-5 Dimensions, five-level version. RESULTS: A total of 615 participants were enrolled, with 216, 203 and 196 randomised by a computer-generated program to exercise therapy, manual therapy and a SSPT, respectively. Baseline data were available for 613 participants, 531 (86.6%) of whom were women; the mean age of these participants was 72.14 years (standard deviation 9.09 years). Primary outcome data were obtained for 69% of participants (429/615) at 12 months: 175 in the exercise therapy arm, 181 in the manual therapy arm and 173 in the SSPT arm. Interim analysis met the criteria for all arms to remain in the study. For the primary outcomes at 12 months, there were no significant benefits over SSPT of exercise [QUALEFFO-41, difference -0.23 points, 95% confidence interval (CI) -3.20 to 1.59 points; p = 1.000; and TLS test, difference 5.77 seconds, 95% CI -4.85 to 20.46 seconds; p = 0.437] or of manual therapy (QUALEFFO-41, difference 1.35 points, 95% CI -1.76 to 2.93 points; p = 0.744; TLS test, difference 9.69 seconds (95% CI 0.09 to 24.86 seconds; p = 0.335). At 4 months, there were significant gains for both manual therapy and exercise therapy over SSPT in the TLS test in participants aged < 70 years. Exercise therapy was superior to a SSPT at 4 months in the SPPB, FRT and 6MWT and manual therapy was superior to a SSPT at 4 months in the TLS test and FRT. Neither manual therapy nor exercise therapy was cost-effective relative to a SSPT using the threshold of £20,000 per quality-adjusted life-year. There were no treatment-related serious adverse events. CONCLUSIONS: This is the largest RCT to date assessing physiotherapy in participants with OVFs. At 1 year, neither treatment intervention conferred more benefit than a single 1-hour physiotherapy advice session. The focus of future work should be on the intensity and duration of interventions to determine if changes to these would demonstrate more sustained effects. TRIAL REGISTRATION: Current Controlled Trials ISRCTN49117867. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 23, No. 44. See the NIHR Journals Library website for further project information.

Osteoporosis is a condition in which bones lose their strength and are more likely to break. It affects around 3 million people in the UK. Fractures of the spine are very common in people with osteoporosis. They can cause a change in body shape, back pain and difficulty with carrying out daily tasks. A treatment that may help people is physiotherapy. There is evidence that several different types of physiotherapy, such as exercise or manual (hands-on) therapy, may help. This was the largest trial of physiotherapy for people with osteoporotic vertebral fracture to date. Seven sessions of physiotherapy treatment based on either exercise or manual therapy were compared with a single 1-hour session of individualised advice from a physiotherapist. The outcome of these treatments was assessed using recognised measures of quality of life, back muscle strength, pain, function and activity at 4 months and 1 year after treatment. How safe the treatments were and whether or not they had any impact on falls or the costs of health and social care were also examined. Interviews were conducted with some of the participants in the trial to seek their opinion about the treatment that they had received. The results show that the participants tolerated all the treatments well, with no significant safety issues, and perceived treatment to be beneficial. The study did not find significant differences between the three treatments in terms of clinical effectiveness or cost-effectiveness at 1 year, although there were benefits in some areas at 4 months.

Comparison of statistical algorithms for daily syndromic surveillance aberration detection.

MOTIVATION: Public health authorities can provide more effective and timely interventions to protect populations during health events if they have effective multi-purpose surveillance systems. These systems rely on aberration detection algorithms to identify potential threats within large datasets. Ensuring the algorithms are sensitive, specific and timely is crucial for protecting public health. Here, we evaluate the performance of three detection algorithms extensively used for syndromic surveillance: the 'rising activity, multilevel mixed effects, indicator emphasis' (RAMMIE) method and the improved quasi-Poisson regression-based method known as 'Farrington Flexible' both currently used at Public Health England, and the 'Early Aberration Reporting System' (EARS) method used at the US Centre for Disease Control and Prevention. We model the wide range of data structures encountered within the daily syndromic surveillance systems used by PHE. We undertake extensive simulations to identify which algorithms work best across different types of syndromes and different outbreak sizes. We evaluate RAMMIE for the first time since its introduction. Performance metrics were computed and compared in the presence of a range of simulated outbreak types that were added to baseline data. RESULTS: We conclude that amongst the algorithm variants that have a high specificity (i.e. >90%), Farrington Flexible has the highest sensitivity and specificity, whereas RAMMIE has the highest probability of outbreak detection and is the most timely, typically detecting outbreaks 2-3 days earlier. AVAILABILITY AND IMPLEMENTATION: R codes developed for this project are available through https://github.com/FelipeJColon/AlgorithmComparison. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comparison of Statistical Algorithms for the Detection of Infectious Disease Outbreaks in Large Multiple Surveillance Systems.

A large-scale multiple surveillance system for infectious disease outbreaks has been in operation in England and Wales since the early 1990s. Changes to the statistical algorithm at the heart of the system were proposed and the purpose of this paper is to compare two new algorithms with the original algorithm. Test data to evaluate performance are created from weekly counts of the number of cases of each of more than 2000 diseases over a twenty-year period. The time series of each disease is separated into one series giving the baseline (background) disease incidence and a second series giving disease outbreaks. One series is shifted forward by twelve months and the two are then recombined, giving a realistic series in which it is known where outbreaks have been added. The metrics used to evaluate performance include a scoring rule that appropriately balances sensitivity against specificity and is sensitive to variation in probabilities near 1. In the context of disease surveillance, a scoring rule can be adapted to reflect the size of outbreaks and this was done. Results indicate that the two new algorithms are comparable to each other and better than the algorithm they were designed to replace.

An improved algorithm for outbreak detection in multiple surveillance systems.

In England and Wales, a large-scale multiple statistical surveillance system for infectious disease outbreaks has been in operation for nearly two decades. This system uses a robust quasi-Poisson regression algorithm to identify abberrances in weekly counts of isolates reported to the Health Protection Agency. In this paper, we review the performance of the system with a view to reducing the number of false reports, while retaining good power to detect genuine outbreaks. We undertook extensive simulations to evaluate the existing system in a range of contrasting scenarios. We suggest several improvements relating to the treatment of trends, seasonality, re-weighting of baselines and error structure. We validate these results by running the existing and proposed new systems in parallel on real data. We find that the new system greatly reduces the number of alarms while maintaining good overall performance and in some instances increasing the sensitivity.

Automated biosurveillance data from England and Wales, 1991-2011.

Outbreak detection systems for use with very large multiple surveillance databases must be suited both to the data available and to the requirements of full automation. To inform the development of more effective outbreak detection algorithms, we analyzed 20 years of data (1991-2011) from a large laboratory surveillance database used for outbreak detection in England and Wales. The data relate to 3,303 distinct types of infectious pathogens, with a frequency range spanning 6 orders of magnitude. Several hundred organism types were reported each week. We describe the diversity of seasonal patterns, trends, artifacts, and extra-Poisson variability to which an effective multiple laboratory-based outbreak detection system must adjust. We provide empirical information to guide the selection of simple statistical models for automated surveillance of multiple organisms, in the light of the key requirements of such outbreak detection systems, namely, robustness, flexibility, and sensitivity.