Publications
171 results found
Shi M, Liu L, Wafa H, et al., 2024, 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., Neuroepidemiology, Vol: 58, Pages: 1-14
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 ICr
Wang W, Otieno JA, Eriksson M, et al., 2023, 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., BMJ Open, Vol: 13
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
Delaney B, Dominguez J, Prociuk D, et al., 2023, ROAD2H: development and evaluation of an open-sourceexplainable artificial intelligence approach for managingco-morbidity and clinical guidelines, Learning Health Systems, ISSN: 2379-6146
IntroductionClinical 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.MethodsWe 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.ResultsPulmonologists 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.ConclusionAn ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long-term condition. Exte
Zaman M, Goff L, L'Esperance V, et al., 2023, A concept mapping approach to assess factors influencing the delivery of community-based salon interventions to prevent cardiovascular disease and breast cancer among ethnically diverse women in south London, The British journal of general practice : the journal of the Royal College of General Practitioners, Vol: 73
BACKGROUND: In the UK, women from ethnically diverse and socioeconomically deprived groups are at increased risk of underdiagnosis of cardiovascular disease (CVD) and low uptake for breast cancer screening. Raising awareness for CVD and breast cancer screening in partnership with salons can improve early detection, management and uptake of screening facilitating women and the NHS. AIM: To explore the perceptions of hair and beauty professionals in the UK on factors that could influence the ability of salons to promote a culturally adapted educational intervention to improve CVD and breast cancer awareness and screening. METHOD: Concept mapping is a multi-stage mixed methods participatory approach. Snowball sampling and dissemination of study information (online and face-to-face) among salon staff nationally 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 in response. Statements will be sorted into categories based on similarity and rated for importance and feasibility. Concept maps using multidimensional scaling and hierarchical cluster analyses will be produced. RESULTS: A total of 19 participants participated in the first stage. We will report on statements generated by participants, statement clusters and ratings for importance and feasibility. This will be depicted in a Go-Zone map that will show statements simultaneously rated in both importance and feasibility. CONCLUSION: Participatory approaches can support the development of educational community-based interventions aiming to establish partnerships between community assets and health systems for CVD and breast cancer awareness and prevention.
Tapuria A, Kalra D, Curcin V, 2023, Digital Analysis of Clinical Screening Criteria for a Rare Disease - Behcet's Disease., Stud Health Technol Inform, Vol: 305, Pages: 444-447
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.
Knoche H, Abdul-Rahman A, Clark L, et al., 2023, Identifying Challenges and Opportunities for Intelligent Data-Driven Health Interfaces to Support Ongoing Care
This workshop will explore future work in the area of intelligent, conversational, data-driven health interfaces both from patients' and health care professionals' perspectives. We aim to bring together a diverse set of experts and stakeholders to jointly discuss the opportunities and challenges at the intersection of public health care provisioning, patient and caretaker empowerment, monitoring provisioning of health care and its quality. This will require AI-supported, conversational decision-making interfaces that adhere to ethical and privacy standards and address issues around agency, control, engagement, motivation, and accessibility. The goal of the workshop is to create a community around intelligent data-driven interfaces and create a road map for their future research.
Wittner R, Holub P, Mascia C, et al., 2023, Toward a common standard for data and specimen provenance in life sciences, LEARNING HEALTH SYSTEMS, ISSN: 2379-6146
Wang W, Ferrari D, Haddon-Hill G, et al., 2023, Electronic Health Records as Source of Research Data, Neuromethods, Pages: 331-354
Electronic health records (EHRs) are the collection of all digitalized information regarding individual’s health. EHRs are not only the base for storing clinical information for archival purposes, but they are also the bedrock on which clinical research and data science thrive. In this chapter, we describe the main aspects of good quality EHR systems, and some of the standard practices in their implementation, to then conclude with details and reflections on their governance and private management.
Wang Y, Chukwusa E, Koffman J, et al., 2023, Public Opinions About Palliative and End-of-Life Care During the COVID-19 Pandemic: Twitter-Based Content Analysis, JMIR FORMATIVE RESEARCH, Vol: 7
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Wang W, Snell LB, Ferrari D, et al., 2022, Real-world effectiveness of steroids in severe COVID-19: a retrospective cohort study, BMC INFECTIOUS DISEASES, Vol: 22
Wang W, Rudd AG, Wang Y, et al., 2022, Risk prediction of 30-day mortality after stroke using machine learning: a nationwide registry-based cohort study (vol 22, 195, 2022), BMC NEUROLOGY, Vol: 22
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- Citations: 1
Meza-Torres B, Delanerolle G, Okusi C, et al., 2022, Differences in Clinical Presentation With Long COVID After Community and Hospital Infection and Associations With All-Cause Mortality: English Sentinel Network Database Study, JMIR PUBLIC HEALTH AND SURVEILLANCE, Vol: 8, ISSN: 2369-2960
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- Citations: 8
Mayor N, Meza-Torres B, Okusi C, et al., 2022, Developing a Long COVID Phenotype for Postacute COVID-19 in a National Primary Care Sentinel Cohort: Observational Retrospective Database Analysis, JMIR PUBLIC HEALTH AND SURVEILLANCE, Vol: 8, ISSN: 2369-2960
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- Citations: 2
de Jong VMT, Rousset RZ, Antonio-Villa NE, et al., 2022, Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis, BMJ-BRITISH MEDICAL JOURNAL, Vol: 378, ISSN: 0959-535X
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- Citations: 18
Tapuria A, Kordowicz M, Ashworth M, et al., 2022, IT Evaluation of Foundation Healthcare Group NHS Vanguard programme: IT simultaneously an enabler and a rate limiting factor, INFORMATICS FOR HEALTH & SOCIAL CARE, Vol: 47, Pages: 317-325, ISSN: 1753-8157
Thygesen JH, Tomlinson C, Hollings S, et al., 2022, COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records, LANCET DIGITAL HEALTH, Vol: 4, Pages: E542-E557
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- Citations: 17
Molokhia M, Ayis S, Karamanos A, et al., 2022, 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, ECLINICALMEDICINE, Vol: 49
Wang W, Rudd AG, Wang Y, et al., 2022, Risk prediction of 30-day mortality after stroke using machine learning: a nationwide registry-based cohort study, BMC NEUROLOGY, Vol: 22
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- Citations: 3
Sivan M, Greenhalgh T, Darbyshire JL, et al., 2022, LOng COvid Multidisciplinary consortium Optimising Treatments and services acrOss the NHS (LOCOMOTION): protocol for a mixed-methods study in the UK, BMJ OPEN, Vol: 12, ISSN: 2044-6055
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- Citations: 14
Drake A, Sassoon I, Balatsoukas P, et al., 2022, The relationship of socio-demographic factors and patient attitudes to connected health technologies: A survey of stroke survivors, HEALTH INFORMATICS JOURNAL, Vol: 28, ISSN: 1460-4582
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- Citations: 2
Wang W, Snell LB, Ferrari D, et al., 2022, Real-world effectiveness of steroids in severe COVID-19: longer courses associated with lower risk of death or ICU admission
<jats:title>Abstract</jats:title> <jats:p>Purpose We aim to investigate the associations of steroid and length of steroid use with outcomes in severe COVID-19.Methods Severe cases of COVID-19, defined by hypoxia at presentation, and admitted to a multi-site healthcare institution in London were analysed between 02-Sep-2020 and 27-May-2021. The associations between duration of steroid treatment (prescription-days) and outcomes were explored using Cox proportional-hazards models adjusting for confounders. Length of steroid treatment was analysed as both a continuous variable and categorised into < 3, 3–10, and > 10 days. The primary outcome was in-hospital mortality and secondary outcome was in-hospital mortality or intensive care unit (ICU) level-3 admission.Results 734 severe COVID-19 cases were included, with 137/734 (18.7%) treated with steroids for < 3 days, 497/734 (67.7%) for 3–10 days, and 100/734 (13.6%) for > 10 days. Cox modelling with continuous days showed increasing length of steroids decreased the hazard of in-hospital mortality by a factor of 0.98 [95% CI: 0.96-1.0] per additional day and in-hospital mortality or ICU admission by a factor of 0.91 [95% CI: 0.87–0.95] per additional day. Further, when taking 3–10 days steroid treatment group as the reference group, > 10 days steroid showed trends towards decreased hazards for death (HR 0.59 [95%CI: 0.30–1.14]) and was significantly protective for death/ICU outcome (HR 0.28 [95%CI: 0.11–0.68]).Conclusion The protective effect of steroid for severe COVID-19 reported in randomised clinical trials was replicated in this large real-world cohort. We found an association between longer steroid courses and lower risk of death or ICU admission that warrants further investigation.</jats:p>
Snell LB, Wang W, Alcolea-Medina A, et al., 2022, Descriptive comparison of admission characteristics between pandemic waves and multivariable analysis of the association of the Alpha variant (B.1.1.7 lineage) of SARS-CoV-2 with disease severity in inner London, BMJ OPEN, Vol: 12, ISSN: 2044-6055
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- Citations: 8
Chapman M, G-Medhin A, Sassoon I, et al., 2022, Using Microservices to Design Patient-facing Research Software, IEEE 18th International Conference on E-Science (E-Science), Publisher: IEEE, Pages: 44-54, ISSN: 2325-372X
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- Citations: 1
Ferrari D, Guidetti V, Wang Y, et al., 2022, Multi-objective Symbolic Regression to Generate Data-driven, Non-fixed Structure and Intelligible Mortality Predictors using EHR: Binary Classification Methodology and Comparison with State-of-the-art., AMIA Annu Symp Proc, Vol: 2022, Pages: 442-451
Symbolic Regression (SR) is a data-driven methodology based on Genetic Programming, and it is widely used to produce arithmetic expressions for modelling learning tasks. Compared to other popular statistical techniques, SR outcomes are given by an arbitrary set of mathematical operations, representing arbitrarily complex linear and non-linear functions without a predefined fixed structure. Another advantage is that, unlike other machine learning algorithms, SR produces interpretable results. In this paper, we explore the qualities and limitations of this technique in a novel implementation as a binary classifier for in-hospital or short-term mortality prediction in patients with Covid-19. Our results highlight that SR provides a competitive alternative to popular statistical and machine learning methodologies to model relevant clinical phenomena thanks to good classification performance, stability in unbalanced dataset management, and intrinsic interpretability.
Hay AD, Moore M, Taylor J, et al., 2021, Immediate oral versus immediate topical versus delayed oral antibiotics for children with acute otitis media with discharge: the REST three-arm non-inferiority electronic platform-supported RCT Introduction, HEALTH TECHNOLOGY ASSESSMENT, Vol: 25, Pages: 1-+, ISSN: 1366-5278
Chapman M, Mumtaz S, Rasmussen L, et al., 2021, Desiderata for the development of next-generation electronic health record phenotype libraries, GIGASCIENCE, Vol: 10, ISSN: 2047-217X
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- Citations: 8
Wongkoblap A, Vadillo MA, Curcin V, 2021, Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study, JMIR MENTAL HEALTH, Vol: 8, ISSN: 2368-7959
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- Citations: 3
Cabral C, Curtis K, Curcin V, et al., 2021, Challenges to implementing electronic trial data collection in primary care: a qualitative study, BMC Family Practice, Vol: 22, ISSN: 1471-2296
BackgroundWithin-consultation recruitment to primary care trials is challenging. Ensuring procedures are efficient and self-explanatory is the key to optimising recruitment. Trial recruitment software that integrates with the electronic health record to support and partially automate procedures is becoming more common. If it works well, such software can support greater participation and more efficient trial designs. An innovative electronic trial recruitment and outcomes software was designed to support recruitment to the Runny Ear randomised controlled trial, comparing topical, oral and delayed antibiotic treatment for acute otitis media with discharge in children. A qualitative evaluation investigated the views and experiences of primary care staff using this trial software.MethodsStaff were purposively sampled in relation to site, role and whether the practice successfully recruited patients. In-depth interviews were conducted using a flexible topic guide, audio recorded and transcribed. Data were analysed thematically.ResultsSixteen staff were interviewed, including GPs, practice managers, information technology (IT) leads and research staff. GPs wanted trial software that automatically captures patient data. However, the experience of getting the software to work within the limited and complex IT infrastructure of primary care was frustrating and time consuming. Installation was reliant on practice level IT expertise, which varied between practices. Although most had external IT support, this rarely included supported for research IT. Arrangements for approving new software varied across practices and often, but not always, required authorisation from Clinical Commissioning Groups.ConclusionsPrimary care IT systems are not solely under the control of individual practices or CCGs or the National Health Service. Rather they are part of a complex system that spans all three and is influenced by semi-autonomous stakeholders operating at different levels. This led
Ford E, Edelman N, Somers L, et al., 2021, Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners, BMC Medical Informatics and Decision Making, Vol: 21, ISSN: 1472-6947
BACKGROUND: Well-established electronic data capture in UK general practice means that algorithms, developed on patient data, can be used for automated clinical decision support systems (CDSSs). These can predict patient risk, help with prescribing safety, improve diagnosis and prompt clinicians to record extra data. However, there is persistent evidence of low uptake of CDSSs in the clinic. We interviewed UK General Practitioners (GPs) to understand what features of CDSSs, and the contexts of their use, facilitate or present barriers to their use. METHODS: We interviewed 11 practicing GPs in London and South England using a semi-structured interview schedule and discussed a hypothetical CDSS that could detect early signs of dementia. We applied thematic analysis to the anonymised interview transcripts. RESULTS: We identified three overarching themes: trust in individual CDSSs; usability of individual CDSSs; and usability of CDSSs in the broader practice context, to which nine subthemes contributed. Trust was affected by CDSS provenance, perceived threat to autonomy and clear management guidance. Usability was influenced by sensitivity to the patient context, CDSS flexibility, ease of control, and non-intrusiveness. CDSSs were more likely to be used by GPs if they did not contribute to alert proliferation and subsequent fatigue, or if GPs were provided with training in their use. CONCLUSIONS: Building on these findings we make a number of recommendations for CDSS developers to consider when bringing a new CDSS into GP patient records systems. These include co-producing CDSS with GPs to improve fit within clinic workflow and wider practice systems, ensuring a high level of accuracy and a clear clinical pathway, and providing CDSS training for practice staff. These recommendations may reduce the proliferation of unhelpful alerts that can result in important decision-support being ignored.
Chapman M, DomÃnguez J, Fairweather E, et al., 2021, Using Computable Phenotypes in Point-of-Care Clinical Trial Recruitment., Stud Health Technol Inform, Vol: 281, Pages: 560-564
A key challenge in point-of-care clinical trial recruitment is to autonomously identify eligible patients on presentation. Similarly, the aim of computable phenotyping is to identify those individuals within a population that exhibit a certain condition. This synergy creates an opportunity to leverage phenotypes in identifying eligible patients for clinical trials. To investigate the feasibility of this approach, we use the Transform clinical trial platform and replace its archetype-based eligibility criteria mechanism with a computable phenotype execution microservice. Utilising a phenotype for acute otitis media with discharge (AOMd) created with the Phenoflow platform, we compare the performance of Transform with and without the use of phenotype-based eligibility criteria when recruiting AOMd patients. The parameters of the trial simulated are based on those of the REST clinical trial, conducted in UK primary care.
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