Co-development of breast cancer health promotion educational materials for ethnically diverse women working with hairdressing and beauty salons: BELONG study.

BACKGROUND: Improved screening uptake is essential for early breast cancer detection, women's health and reducing health disparities. However, minority ethnic and deprived communities often face lower breast cancer screening rates and limited access to culturally tailored educational materials. A recent review found limited culturally tailored materials for breast cancer education. AIM: To investigate the culturally appropriate interfaces and preferences of salon staff in educating their clients about breast cancer METHOD: We used a two-stage approach, following the Double Diamond framework; discover and define phases. Relevant breast cancer materials (i.e., based on cultural appropriateness, English language presentation, and alignment with the UK context) were assessed using the Suitability Assessment of Materials (SAM) toolkit. Interviews with ethnically diverse salon staff provided insights into their needs and preferences for client education materials. Thematic analysis was applied to interview transcripts. RESULTS: Cultural appropriateness was evident in 9/14 (64%) of the materials identified (e.g., targeting black ethnicities with positive representations). Of those, six of them demonstrated an overall SAM rating of 76% ("Superior"). Thematic analysis of interviews identified seven key themes, including the importance of engagement strategies, education and awareness for health promotion, salon staff's role, preferred training methods, supportive materials, inclusivity, representation, and participant satisfaction. CONCLUSION: This study highlights the SAM toolkit's role in selecting suitable educational materials for breast cancer prevention. The research offers prospects for improving breast cancer awareness in ethnically diverse communities and addressing healthcare access disparities, with salon hairdressers emerging as crucial advocates for health promotion.

Mechanisms for Integrating Real Data into Search Game Simulations: An Application to Winter Health Service Pressures and Preventative Policies.

While modelling and simulation are powerful techniques for exploring complex phenomena, if they are not coupled with suitable real-world data any results obtained are likely to require extensive validation. We consider this problem in the context of search game modelling, and suggest that both demographic and behaviour data are used to configure certain model parameters. We show this integration in practice by using a combined dataset of over 150,000 individuals to configure a specific search game model that captures the environment, population, interventions and individual behaviours relating to winter health service pressures. The presence of this data enables us to more accurately explore the potential impact of service pressure interventions, which we do across 33,000 simulations using a computational version of the model. We find government advice to be the best-performing intervention in simulation, in respect of improved health, reduced health inequalities, and thus reduced pressure on health service utilisation.

Creating a next-generation phenotype library: the health data research UK Phenotype Library.

Objective: To enable reproducible research at scale by creating a platform that enables health data users to find, access, curate, and re-use electronic health record phenotyping algorithms. Materials and Methods: We undertook a structured approach to identifying requirements for a phenotype algorithm platform by engaging with key stakeholders. User experience analysis was used to inform the design, which we implemented as a web application featuring a novel metadata standard for defining phenotyping algorithms, access via Application Programming Interface (API), support for computable data flows, and version control. The application has creation and editing functionality, enabling researchers to submit phenotypes directly. Results: We created and launched the Phenotype Library in October 2021. The platform currently hosts 1049 phenotype definitions defined against 40 health data sources and >200K terms across 16 medical ontologies. We present several case studies demonstrating its utility for supporting and enabling research: the library hosts curated phenotype collections for the BREATHE respiratory health research hub and the Adolescent Mental Health Data Platform, and it is supporting the development of an informatics tool to generate clinical evidence for clinical guideline development groups. Discussion: This platform makes an impact by being open to all health data users and accepting all appropriate content, as well as implementing key features that have not been widely available, including managing structured metadata, access via an API, and support for computable phenotypes. Conclusions: We have created the first openly available, programmatically accessible resource enabling the global health research community to store and manage phenotyping algorithms. Removing barriers to describing, sharing, and computing phenotypes will help unleash the potential benefit of health data for patients and the public.

Effect of on-demand vs continuous prescription of proton pump inhibitors on symptom burden and quality of life: results of a real-world randomized controlled trial in primary care patients with gastroesophageal reflux disease.

OBJECTIVES: This study aimed to assess the impact of on-demand versus continuous prescribing of proton pump inhibitors (PPIs) on symptom burden and health-related quality of life in patients with gastroesophageal reflux disease (GERD) presenting to primary care. METHODS: Thirty-six primary care centres across Europe enrolled adult GERD patients from electronic health records. Participants were randomised to on-demand or continuous PPI prescriptions and were followed for 8 weeks. PPI intake, symptom burden, and quality of life were compared between the two groups using mixed-effect regression analyses. Spearman's correlation was used to assess the association between changes in PPI dose and patient-reported outcomes. RESULTS: A total of 488 patients (median age 51 years, 58% women) completed the initial visit, with 360 attending the follow-up visit. There was no significant difference in PPI use between the continuous and on-demand prescription groups (b=.57, 95%CI:0.40-1.53), although PPI use increased in both groups (b = 1.33, 95%CI:0.65 - 2.01). Advice on prescribing strategy did not significantly affect patient-reported outcomes. Both symptom burden (Reflux Disease Questionnaire, b=-0.61, 95%CI:-0.73 - -0.49) and quality of life (12-item Short Form Survey physical score b = 3.31, 95%CI:2.17 - 4.45) improved from baseline to follow-up in both groups. Increased PPI intake correlated with reduced reflux symptoms (n = 347, ρ=-0.12, p = 0.02) and improved quality of life (n = 217, ρ = 0.16, p = 0.02). CONCLUSION: In real-world settings, both continuous and on-demand PPI prescriptions resulted in similar increases in PPI consumption with no difference in treatment effects. Achieving an adequate PPI dose to alleviate reflux symptom burden improves quality of life in GERD patients. EudraCT number 2014-001314-25.

Continuous and on-demand prescription increase in proton pump inhibitor consumption equally in real-world settings and did not result in different outcomes.Reaching a sufficient dose of proton pump inhibitor to reduce reflux symptom burden improves quality of life in patients with gastroesophageal reflux disease.

ROAD2H: Development and evaluation of an open-source explainable artificial intelligence approach for managing co-morbidity and clinical guidelines.

Introduction: Clinical decision support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real-world co-morbidity. In patient-specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co-morbidity are a requirement. In this work, we develop and evaluate a non-proprietary, standards-based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial electronic health record (EHR) system in Serbia, a middle-income country in West Balkans. Methods: We used an ontological framework, the Transition-based Medical Recommendation (TMR) model, to represent, and reason about, guideline concepts, and chose the 2017 International global initiative for chronic obstructive lung disease (GOLD) guideline and a Serbian hospital as the deployment and evaluation site, respectively. To mitigate potential guideline conflicts, we used a TMR-based implementation of the Assumptions-Based Argumentation framework extended with preferences and Goals (ABA+G). Remote EHR integration of computable guidelines was via a microservice architecture based on HL7 FHIR and CDS Hooks. A prototype integration was developed to manage chronic obstructive pulmonary disease (COPD) with comorbid cardiovascular or chronic kidney diseases, and a mixed-methods evaluation was conducted with 20 simulated cases and five pulmonologists. Results: Pulmonologists agreed 97% of the time with the GOLD-based COPD symptom severity assessment assigned to each patient by the CDSS, and 98% of the time with one of the proposed COPD care plans. Comments were favourable on the principles of explainable argumentation; inclusion of additional co-morbidities was suggested in the future along with customisation of the level of explanation with expertise. Conclusion: An ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long-term condition. Extension to other guidelines and multiple co-morbidities is needed to test the approach further.

Social media for palliative and end-of-life care research: a systematic review.

BACKGROUND: Social media with real-time content and a wide-reaching user network opens up more possibilities for palliative and end-of-life care (PEoLC) researchers who have begun to embrace it as a complementary research tool. This review aims to identify the uses of social media in PEoLC studies and to examine the ethical considerations and data collection approaches raised by this research approach. METHODS: Nine online databases were searched for PEoLC research using social media published before December 2022. Thematic analysis and narrative synthesis approach were used to categorise social media applications. RESULTS: 21 studies were included. 16 studies used social media to conduct secondary analysis and five studies used social media as a platform for information sharing. Ethical considerations relevant to social media studies varied while 15 studies discussed ethical considerations, only 6 studies obtained ethical approval and 5 studies confirmed participant consent. Among studies that used social media data, most of them manually collected social media data, and other studies relied on Twitter application programming interface or third-party analytical tools. A total of 1 520 329 posts, 325 videos and 33 articles related to PEoLC from 2008 to 2022 were collected and analysed. CONCLUSIONS: Social media has emerged as a promising complementary research tool with demonstrated feasibility in various applications. However, we identified the absence of standardised ethical handling and data collection approaches which pose an ongoing challenge. We provided practical recommendations to bridge these pressing gaps for researchers wishing to use social media in future PEoLC-related studies.

Toward a common standard for data and specimen provenance in life sciences.

Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre-analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross-organizational documentation, traceability, and non-repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine-actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.

Effectiveness and Safety of Non-Vitamin K Oral Anticoagulants versus Warfarin in Patients with Atrial Fibrillation and Previous Stroke: A Systematic Review and Meta-Analysis.

INTRODUCTION: Current evidence regarding the clinical outcomes of non-vitamin K oral anticoagulants (NOACs) versus warfarin in patients with atrial fibrillation (AF) and previous stroke is inconclusive, especially in patients with previous intracranial haemorrhage (ICrH). We aim to undertake a systematic review and meta-analysis assessing the effectiveness and safety of NOACs versus warfarin in AF patients with a history of stroke. METHODS: We searched studies published up to December 10, 2022, on PubMed, Medline, Embase, and Cochrane Central Register of Controlled Trials. Studies on adults with AF and previous ischaemic stroke (IS) or IrCH receiving either NOACs or warfarin and capturing outcome events (thromboembolic events, ICrH, and all-cause mortality) were eligible for inclusion. RESULTS: Six randomized controlled trials (RCTs) (including 19,489 patients with previous IS) and fifteen observational studies (including 132,575 patients with previous IS and 13,068 patients with previous ICrH) were included. RCT data showed that compared with warfarin, NOACs were associated with a significant reduction in thromboembolic events (odds ratio [OR]: 0.85, 95% confidence interval [CI]: 0.75-0.96), ICrH (OR: 0.57, 95% CI: 0.36-0.90), and all-cause mortality (OR: 0.88, 95% CI: 0.80-0.98). In analysing observational studies, similar results were retrieved. Moreover, patients with previous ICrH had a lower OR on thromboembolic events than those with IS (OR: 0.66, 95% CI: 0.46-0.95 vs. OR: 0.80, 95% CI: 0.70-0.93) in the comparison between NOACs and warfarin. CONCLUSIONS: Observational data showed that in AF patients with previous stroke, NOACs showed better clinical performance compared to warfarin and the benefits of NOACs were more pronounced in patients with previous IrCH versus those with IS. RCT data also showed NOACs are superior to warfarin. However, current RCTs only included AF patients who survived an IS, and further large RCTs focused on patients with previous ICrH are warranted.

Factors affecting the delivery of community-based salon interventions to prevent cardiovascular disease and breast cancer among ethnically diverse women in South London: a concept-mapping approach.

BACKGROUND: In the UK, women from ethnically diverse and socioeconomically deprived communities are at increased risk of underdiagnosis of cardiovascular disease (CVD) and breast cancer. Promoting CVD prevention and awareness of breast cancer screening via community salons and primary health care partnerships can improve uptake of screening services and early detection. METHODS: Concept mapping is a multistage mixed methods participatory approach comprised of six stages: preparation, brainstorming, structuring of statements, representing statements, interpretation and utilisation of maps using Group wisdom software. A target of 20 salons, excluding male-only salons were approached. Salons included Salons included hairdressing or hairdressing and beauty salons. Purposeful and convenience sampling (online and face to face) among UK salons (hair and beauty) was conducted. Participants were given a focus prompt "What would be some factors that can influence the ability of salons to deliver this service?" and required to generate statements, which were sorted into categories based on similarity and rated for importance and feasibility. Concept maps using multidimensional scaling and hierarchical cluster analyses were produced. FINDINGS: Of 35 participants invited, 25 (71%) consented and agreed to take part in concept mapping. Reported ages were 26-35 years (n=5, 20%), 36-45 years (n=12, 48%), 46-55 years (n=3, 12%), 56-65 years (n=5, 20%), and no age reported (n=10, 40%). Around 36% (n=9) of participants were from non-White ethnic groups, with 12% (n=3) being male and 88% (n=22) female. Seven clusters emerged. Salon staff capabilities and capacities and engaging in health conversations in community salons scored average bridging values of 0·09 and 0·2 respectively, indicating good cluster homogeneity (similar meaning statements were closely sorted). Facilitating health-care access with GP practices was rated highly important to effectively promote the intervention. Engaging in health conversations in community salons and salon incentives for participation were examples of factors that were highly feasible to address. The r correlation coefficient was 0·68 between importance and feasibility to address factors affecting community health interventions. INTERPRETATION: Salons are well positioned to support health promotion interventions. Actionable priorities were identified for a salon-GP surgery partnership to promote CVD prevention through lifestyle changes and health check uptake, raising breast cancer screening awareness and address issue of equity. FUNDING: National Institute of Health and Care Research (NIHR), Research for Patient Benefit (RfPB) Programme NIHR202769.

Developing and externally validating a machine learning risk prediction model for 30-day mortality after stroke using national stroke registers in the UK and Sweden.

OBJECTIVES: We aimed to develop and externally validate a generalisable risk prediction model for 30-day stroke mortality suitable for supporting quality improvement analytics in stroke care using large nationwide stroke registers in the UK and Sweden. DESIGN: Registry-based cohort study. SETTING: Stroke registries including the Sentinel Stroke National Audit Programme (SSNAP) in England, Wales and Northern Ireland (2013-2019) and the national Swedish stroke register (Riksstroke 2015-2020). PARTICIPANTS AND METHODS: Data from SSNAP were used for developing and temporally validating the model, and data from Riksstroke were used for external validation. Models were developed with the variables available in both registries using logistic regression (LR), LR with elastic net and interaction terms and eXtreme Gradient Boosting (XGBoost). Performances were evaluated with discrimination, calibration and decision curves. OUTCOME MEASURES: The primary outcome was all-cause 30-day in-hospital mortality after stroke. RESULTS: In total, 488 497 patients who had a stroke with 12.4% 30-day in-hospital mortality were used for developing and temporally validating the model in the UK. A total of 128 360 patients who had a stroke with 10.8% 30-day in-hospital mortality and 13.1% all mortality were used for external validation in Sweden. In the SSNAP temporal validation set, the final XGBoost model achieved the highest area under the receiver operating characteristic curve (AUC) (0.852 (95% CI 0.848 to 0.855)) and was well calibrated. The performances on the external validation in Riksstroke were as good and achieved AUC at 0.861 (95% CI 0.858 to 0.865) for in-hospital mortality. For Riksstroke, the models slightly overestimated the risk for in-hospital mortality, while they were better calibrated at the risk for all mortality. CONCLUSION: The risk prediction model was accurate and externally validated using high quality registry data. This is potentially suitable to be deployed as part of quality improvement analytics in stroke care to enable the fair comparison of stroke mortality outcomes across hospitals and health systems across countries.

Digital Analysis of Clinical Screening Criteria for a Rare Disease - Behcet's Disease.

The objective is to identify clinical screening criteria for a rare disease,- Behcet's disease and to analyse the digitally structured and unstructured components of the Identified Clinical criteria, build a clinical archetype using OpenEHR editor to be used by learning health support systems for clinical screening of the disease. Methods/Search Strategy: Literature search was conducted, 230 papers were screened, and finally 5 papers were retained, analysed and summarised. Digital Analysis of the clinical criteria was done and a sandardised clinical knowledge model of the same was built using OpenEHR editor, underpinned by OpenEHR international standards. Results The structured and unstructured components of the criteria analysed to be able to incorporate them in a learning health system to screen patients for Behcet's disease. SNOMED CT and Read codes were assigned to the structured componenets. Possible misdiagnosis were identified, along with their corresponding clinical terminology codes that can be incorporated in the Electronic Health Record systems. Conclusion: The identified clinical screening was digitally analysed which can be embedded into a clinical decision support system that can be plugged onto the primary care systems to give an alert to the clinicians if a patient needs to be screened for a rare disease, for e.g., Behcet's.

Public Opinions About Palliative and End-of-Life Care During the COVID-19 Pandemic: Twitter-Based Content Analysis.

BACKGROUND: Palliative and end-of-life care (PEoLC) played a critical role in relieving distress and providing grief support in response to the heavy toll caused by the COVID-19 pandemic. However, little is known about public opinions concerning PEoLC during the pandemic. Given that social media have the potential to collect real-time public opinions, an analysis of this evidence is vital to guide future policy-making. OBJECTIVE: This study aimed to use social media data to investigate real-time public opinions regarding PEoLC during the COVID-19 crisis and explore the impact of vaccination programs on public opinions about PEoLC. METHODS: This Twitter-based study explored tweets across 3 English-speaking countries: the United States, the United Kingdom, and Canada. From October 2020 to March 2021, a total of 7951 PEoLC-related tweets with geographic tags were retrieved and identified from a large-scale COVID-19 Twitter data set through the Twitter application programming interface. Topic modeling realized through a pointwise mutual information-based co-occurrence network and Louvain modularity was used to examine latent topics across the 3 countries and across 2 time periods (pre- and postvaccination program periods). RESULTS: Commonalities and regional differences among PEoLC topics in the United States, the United Kingdom, and Canada were identified specifically: cancer care and care facilities were of common interest to the public across the 3 countries during the pandemic; the public expressed positive attitudes toward the COVID-19 vaccine and highlighted the protection it affords to PEoLC professionals; and although Twitter users shared their personal experiences about PEoLC in the web-based community during the pandemic, this was more prominent in the United States and Canada. The implementation of the vaccination programs raised the profile of the vaccine discussion; however, this did not influence public opinions about PEoLC. CONCLUSIONS: Public opinions on Twitter reflected a need for enhanced PEoLC services during the COVID-19 pandemic. The insignificant impact of the vaccination program on public discussion on social media indicated that public concerns regarding PEoLC continued to persist even after the vaccination efforts. Insights gleaned from public opinions regarding PEoLC could provide some clues for policy makers on how to ensure high-quality PEoLC during public health emergencies. In this post-COVID-19 era, PEoLC professionals may wish to continue to examine social media and learn from web-based public discussion how to ease the long-lasting trauma caused by this crisis and prepare for public health emergencies in the future. Besides, our results showed social media's potential in acting as an effective tool to reflect public opinions in the context of PEoLC.

Real-world effectiveness of steroids in severe COVID-19: a retrospective cohort study.

INTRODUCTION: Randomised controlled trials have shown that steroids reduce the risk of dying in patients with severe Coronavirus disease 2019 (COVID-19), whilst many real-world studies have failed to replicate this result. We aim to investigate real-world effectiveness of steroids in severe COVID-19. METHODS: Clinical, demographic, and viral genome data extracted from electronic patient record (EPR) was analysed from all SARS-CoV-2 RNA positive patients admitted with severe COVID-19, defined by hypoxia at presentation, between March 13th 2020 and May 27th 2021. Steroid treatment was measured by the number of prescription-days with dexamethasone, hydrocortisone, prednisolone or methylprednisolone. The association between steroid > 3 days treatment and disease outcome was explored using multivariable cox proportional hazards models with adjustment for confounders (including age, gender, ethnicity, co-morbidities and SARS-CoV-2 variant). The outcome was in-hospital mortality. RESULTS: 1100 severe COVID-19 cases were identified having crude hospital mortality of 15.3%. 793/1100 (72.1%) individuals were treated with steroids and 513/1100 (46.6%) received steroid ≤ 3 days. From the multivariate model, steroid > 3 days was associated with decreased hazard of in-hospital mortality (HR: 0.47 (95% CI: 0.31-0.72)). CONCLUSION: The protective effect of steroid treatment for severe COVID-19 reported in randomised clinical trials was replicated in this retrospective study of a large real-world cohort.

Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis.

OBJECTIVE: To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19. DESIGN: Two stage individual participant data meta-analysis. SETTING: Secondary and tertiary care. PARTICIPANTS: 46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021. DATA SOURCES: Multiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in The BMJ, and through PROSPERO, reference checking, and expert knowledge. MODEL SELECTION AND ELIGIBILITY CRITERIA: Prognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor. METHODS: Eight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters. MAIN OUTCOME MEASURES: 30 day mortality or in-hospital mortality. RESULTS: Datasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al's model (0.96, 0.59 to 1.55, 0.21 to 4.28). CONCLUSION: The prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care.

Developing a Long COVID Phenotype for Postacute COVID-19 in a National Primary Care Sentinel Cohort: Observational Retrospective Database Analysis.

BACKGROUND: Following COVID-19, up to 40% of people have ongoing health problems, referred to as postacute COVID-19 or long COVID (LC). LC varies from a single persisting symptom to a complex multisystem disease. Research has flagged that this condition is underrecorded in primary care records, and seeks to better define its clinical characteristics and management. Phenotypes provide a standard method for case definition and identification from routine data and are usually machine-processable. An LC phenotype can underpin research into this condition. OBJECTIVE: This study aims to develop a phenotype for LC to inform the epidemiology and future research into this condition. We compared clinical symptoms in people with LC before and after their index infection, recorded from March 1, 2020, to April 1, 2021. We also compared people recorded as having acute infection with those with LC who were hospitalized and those who were not. METHODS: We used data from the Primary Care Sentinel Cohort (PCSC) of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) database. This network was recruited to be nationally representative of the English population. We developed an LC phenotype using our established 3-step ontological method: (1) ontological step (defining the reasoning process underpinning the phenotype, (2) coding step (exploring what clinical terms are available, and (3) logical extract model (testing performance). We created a version of this phenotype using Protégé in the ontology web language for BioPortal and using PhenoFlow. Next, we used the phenotype to compare people with LC (1) with regard to their symptoms in the year prior to acquiring COVID-19 and (2) with people with acute COVID-19. We also compared hospitalized people with LC with those not hospitalized. We compared sociodemographic details, comorbidities, and Office of National Statistics-defined LC symptoms between groups. We used descriptive statistics and logistic regression. RESULTS: The long-COVID phenotype differentiated people hospitalized with LC from people who were not and where no index infection was identified. The PCSC (N=7.4 million) includes 428,479 patients with acute COVID-19 diagnosis confirmed by a laboratory test and 10,772 patients with clinically diagnosed COVID-19. A total of 7471 (1.74%, 95% CI 1.70-1.78) people were coded as having LC, 1009 (13.5%, 95% CI 12.7-14.3) had a hospital admission related to acute COVID-19, and 6462 (86.5%, 95% CI 85.7-87.3) were not hospitalized, of whom 2728 (42.2%) had no COVID-19 index date recorded. In addition, 1009 (13.5%, 95% CI 12.73-14.28) people with LC were hospitalized compared to 17,993 (4.5%, 95% CI 4.48-4.61; P<.001) with uncomplicated COVID-19. CONCLUSIONS: Our LC phenotype enables the identification of individuals with the condition in routine data sets, facilitating their comparison with unaffected people through retrospective research. This phenotype and study protocol to explore its face validity contributes to a better understanding of LC.

What factors influence differential uptake of NHS Health Checks, diabetes and hypertension reviews among women in ethnically diverse South London? Cross-sectional analysis of 63,000 primary care records.

Background: Uptake of health checks among women has not been examined in relation to patient and General Practitioner (GP) practice level factors. We investigated patient and practice level factors associated with differential uptake of health checks. Methods: Primary care records from 44 practices in Lambeth for women aged 40-74 years old (N = 62,967) from 2000-2018 were analysed using multi-level logistic regression models. An odds ratio (OR) >1 indicates increased occurrence of no health check. Findings: The mean age (IQR) of the included female sample (aged 40-74 years) was 52.9 years (45.0-59.0). Adjusted for patient-level factors (age, ethnicity, English as first language, overweight/obesity, smoking, attendance to GP practices, and co-morbidity), the odds of non-uptake of health checks were higher for Other White (OR 1.24, 95% confidence interval 1.17-1.33), and Other ethnicity (1.20, 1.07-1.35) vs. White British. It was also higher for 50-69 year olds (1.55, 1.47-1.62), 70-74 year olds (1.60, 1.49-1.72) vs. 40-49 year olds. These ORs did not change on adjustments for practice level factors (proportion of patients living in deprived areas, proportion of patients with ≥1 chronic condition, ≥3 emergency diabetes admissions annually, GP density/1000 patients, quality outcome framework score of ≥ 95%, and patient satisfaction scores of ≥80%). Non-uptake was lower for Black Caribbeans, Bangladeshis, overweight/obese patients, frequent practice attenders and comorbid patients. Interpretation: Differential uptake in health checks remained after adjustment for patient and practice level factors. Better measures of social determinants of health and of practice context are needed. Funding: NIHR Research for Patient Benefit Programme (NIHR202769).

The relationship of socio-demographic factors and patient attitudes to connected health technologies: A survey of stroke survivors.

More evidence is needed on technology implementation for remote monitoring and self-management across the various settings relevant to chronic conditions. This paper describes the findings of a survey designed to explore the relevance of socio-demographic factors to attitudes towards connected health technologies in a community of patients. Stroke survivors living in the UK were invited to answer questions about themselves and about their attitudes to a prototype remote monitoring and self-management app developed around their preferences. Eighty (80) responses were received and analysed, with limitations and results presented in full. Socio-demographic factors were not found to be associated with variations in participants' willingness to use the system and attitudes to data sharing. Individuals' levels of interest in relevant technology was suggested as a more important determinant of attitudes. These observations run against the grain of most relevant literature to date, and tend to underline the importance of prioritising patient-centred participatory research in efforts to advance connected health technologies.

LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS (LOCOMOTION): protocol for a mixed-methods study in the UK.

INTRODUCTION: Long COVID, a new condition whose origins and natural history are not yet fully established, currently affects 1.5 million people in the UK. Most do not have access to specialist long COVID services. We seek to optimise long COVID care both within and outside specialist clinics, including improving access, reducing inequalities, helping self-management and providing guidance and decision support for primary care. We aim to establish a 'gold standard' of care by systematically analysing current practices, iteratively improving pathways and systems of care. METHODS AND ANALYSIS: This mixed-methods, multisite study is informed by the principles of applied health services research, quality improvement, co-design, outcome measurement and learning health systems. It was developed in close partnership with patients (whose stated priorities are prompt clinical assessment; evidence-based advice and treatment and help with returning to work and other roles) and with front-line clinicians. Workstreams and tasks to optimise assessment, treatment and monitoring are based in three contrasting settings: workstream 1 (qualitative research, up to 100 participants), specialist management in 10 long COVID clinics across the UK, via a quality improvement collaborative, experience-based co-design and targeted efforts to reduce inequalities of access, return to work and peer support; workstream 2 (quantitative research, up to 5000 participants), patient self-management at home, technology-supported monitoring and validation of condition-specific outcome measures and workstream 3 (quantitative research, up to 5000 participants), generalist management in primary care, harnessing electronic record data to study population phenotypes and develop evidence-based decision support, referral pathways and analysis of costs. Study governance includes an active patient advisory group. ETHICS AND DISSEMINATION: LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS study is sponsored by the University of Leeds and approved by Yorkshire & The Humber-Bradford Leeds Research Ethics Committee (ref: 21/YH/0276). Participants will provide informed consent. Dissemination plans include academic and lay publications, and partnerships with national and regional policymakers. TRIAL REGISTRATION NUMBER: NCT05057260, ISRCTN15022307.

Risk prediction of 30-day mortality after stroke using machine learning: a nationwide registry-based cohort study.

BACKGROUNDS: We aimed to develop and validate machine learning (ML) models for 30-day stroke mortality for mortality risk stratification and as benchmarking models for quality improvement in stroke care. METHODS: Data from the UK Sentinel Stroke National Audit Program between 2013 to 2019 were used. Models were developed using XGBoost, Logistic Regression (LR), LR with elastic net with/without interaction terms using 80% randomly selected admissions from 2013 to 2018, validated on the 20% remaining admissions, and temporally validated on 2019 admissions. The models were developed with 30 variables. A reference model was developed using LR and 4 variables. Performances of all models was evaluated in terms of discrimination, calibration, reclassification, Brier scores and Decision-curves. RESULTS: In total, 488,497 stroke patients with a 12.3% 30-day mortality rate were included in the analysis. In 2019 temporal validation set, XGBoost model obtained the lowest Brier score (0.069 (95% CI: 0.068-0.071)) and the highest area under the ROC curve (AUC) (0.895 (95% CI: 0.891-0.900)) which outperformed LR reference model by 0.04 AUC (p < 0.001) and LR with elastic net and interaction term model by 0.003 AUC (p < 0.001). All models were perfectly calibrated for low (< 5%) and moderate risk groups (5-15%) and ≈1% underestimation for high-risk groups (> 15%). The XGBoost model reclassified 1648 (8.1%) low-risk cases by the LR reference model as being moderate or high-risk and gained the most net benefit in decision curve analysis. CONCLUSIONS: All models with 30 variables are potentially useful as benchmarking models in stroke-care quality improvement with ML slightly outperforming others.