153 results found
Wang W, Rudd AG, Wang Y, et al., 2022, Correction: Risk prediction of 30-day mortality after stroke using machine learning: a nationwide registry-based cohort study., BMC Neurol, Vol: 22
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 Surveill, Vol: 8
BACKGROUND: Most studies of long COVID (symptoms of COVID-19 infection beyond 4 weeks) have focused on people hospitalized in their initial illness. Long COVID is thought to be underrecorded in UK primary care electronic records. OBJECTIVE: We sought to determine which symptoms people present to primary care after COVID-19 infection and whether presentation differs in people who were not hospitalized, as well as post-long COVID mortality rates. METHODS: We used routine data from the nationally representative primary care sentinel cohort of the Oxford-Royal College of General Practitioners Research and Surveillance Centre (N=7,396,702), applying a predefined long COVID phenotype and grouped by whether the index infection occurred in hospital or in the community. We included COVID-19 infection cases from March 1, 2020, to April 1, 2021. We conducted a before-and-after analysis of long COVID symptoms prespecified by the Office of National Statistics, comparing symptoms presented between 1 and 6 months after the index infection matched with the same months 1 year previously. We conducted logistic regression analysis, quoting odds ratios (ORs) with 95% CIs. RESULTS: In total, 5.63% (416,505/7,396,702) and 1.83% (7623/416,505) of the patients had received a coded diagnosis of COVID-19 infection and diagnosis of, or referral for, long COVID, respectively. People with diagnosis or referral of long COVID had higher odds of presenting the prespecified symptoms after versus before COVID-19 infection (OR 2.66, 95% CI 2.46-2.88, for those with index community infection and OR 2.42, 95% CI 2.03-2.89, for those hospitalized). After an index community infection, patients were more likely to present with nonspecific symptoms (OR 3.44, 95% CI 3.00-3.95; P<.001) compared with after a hospital admission (OR 2.09, 95% CI 1.56-2.80; P<.001). Mental health sequelae were more strongly associated with index hospital infections (OR 2.21, 95% CI 1.64-2.96) than with index community infe
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 Surveill, Vol: 8
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 stati
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
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., Inform Health Soc Care, Vol: 47, Pages: 317-325
The goal of the Foundation Healthcare Group (FHG) Vanguard model was to develop a sustainable local hospital model between two National Health Service (NHS) Trusts (a London Teaching Hospital Trust and a District General Hospital Trust) that makes best use of scarce resources and can be replicated across the NHS, UK. The aim of this study was to evaluate the provision, use, and implementation of the IT infrastructure based on qualitative interviews focused mainly on the perspectives of the IT staff and the clinicians' perspectives. METHODS: In total, 24 interview transcripts, along with 'Acute Care Collaboration' questionnaire responses, were analyzed using a thematic framework for IT infrastructure, sharing themes across the vascular, pediatric, and cardiovascular strands of the FHG programme. RESULTS: Findings indicated that Skype for Business had been an innovative and helpful development widely available to be used between the two Trusts. Clinicians initially reported lack of IT support and infrastructure expected at the outset for a national Vanguard project but later appreciated that remote access to most clinical applications including scans between the two Trusts became operational. The Local Care Record (LCR), an IT project was perceived to have been delivered successfully in South London. Shared technology reduced patient traveling time by providing locally based shared care. CONCLUSION: Lesson learnt is that ensuring patient benefit and priorities is a strong driver to implementation and one needs to identify IT rate-limiting steps at an early stage and on a regular basis and then focus on rapid implementation of solutions. In fact, future work may also assess how the IT infrastructure developed by FHG vanguard project might have helped/boosted the 'digital health' practice during the COVID-19 times. Spreading and scaling-up innovations from the Vanguard sites was the aspiration and challenge for system leaders. After COVID-19, the use of IT is scaled up
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
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 Neurol, Vol: 22
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.
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
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
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
BACKGROUND: The Alpha variant (B.1.1.7 lineage) of SARS-CoV-2 emerged and became the dominant circulating variant in the UK in late 2020. Current literature is unclear on whether the Alpha variant is associated with increased severity. We linked clinical data with viral genome sequence data to compare admitted cases between SARS-CoV-2 waves in London and to investigate the association between the Alpha variant and the severity of disease. METHODS: Clinical, demographic, laboratory and viral sequence data from electronic health record systems were collected for all cases with a positive SARS-CoV-2 RNA test between 13 March 2020 and 17 February 2021 in a multisite London healthcare institution. Multivariate analysis using logistic regression assessed risk factors for severity as defined by hypoxia at admission. RESULTS: There were 5810 SARS-CoV-2 RNA-positive cases of which 2341 were admitted (838 in wave 1 and 1503 in wave 2). Both waves had a temporally aligned rise in nosocomial cases (96 in wave 1 and 137 in wave 2). The Alpha variant was first identified on 15 November 2020 and increased rapidly to comprise 400/472 (85%) of sequenced isolates from admitted cases in wave 2. A multivariate analysis identified risk factors for severity on admission, such as age (OR 1.02, 95% CI 1.01 to 1.03, for every year older; p<0.001), obesity (OR 1.70, 95% CI 1.28 to 2.26; p<0.001) and infection with the Alpha variant (OR 1.68, 95% CI 1.26 to 2.24; p<0.001). CONCLUSIONS: Our analysis is the first in hospitalised cohorts to show increased severity of disease associated with the Alpha variant. The number of nosocomial cases was similar in both waves despite the introduction of many infection control interventions before wave 2.
Carr E, Bendayan R, Bean D, et al., 2021, Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study, BMC Medicine, Vol: 19
Background: The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification. Methods: Training cohorts comprised 1276 patients admitted to King’s College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy’s and St Thomas’ Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals, University Hospitals Birmingham), one hospital in Norway (Oslo University Hospital), and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital). The outcome was severe COVID-19 disease (transfer to intensive care unit (ICU) or death) at 14 days after hospital admission. Age, physiological measures, blood biomarkers, sex, ethnicity, and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) measured at hospital admission were considered in the models. Results: A baseline model of ‘NEWS2 + age’ had poor-to-moderate discrimination for severe COVID-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (CI) 0.680, 0.722; Brier score = 0.192, 95% CI 0.186, 0.197). A supplemented model adding eight routinely collected blood and physiological parameters (supplemental oxygen flow rate, urea, age, oxygen saturation, C-reactive protein, estimated glomerular filtration rate, neutrophil count, neutrophil/lymphocyte ratio) improved discriminat
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 LV, et al., 2021, Desiderata for the development of next-generation electronic health record phenotype libraries., Gigascience, Vol: 10
BACKGROUND: High-quality phenotype definitions are desirable to enable the extraction of patient cohorts from large electronic health record repositories and are characterized by properties such as portability, reproducibility, and validity. Phenotype libraries, where definitions are stored, have the potential to contribute significantly to the quality of the definitions they host. In this work, we present a set of desiderata for the design of a next-generation phenotype library that is able to ensure the quality of hosted definitions by combining the functionality currently offered by disparate tooling. METHODS: A group of researchers examined work to date on phenotype models, implementation, and validation, as well as contemporary phenotype libraries developed as a part of their own phenomics communities. Existing phenotype frameworks were also examined. This work was translated and refined by all the authors into a set of best practices. RESULTS: We present 14 library desiderata that promote high-quality phenotype definitions, in the areas of modelling, logging, validation, and sharing and warehousing. CONCLUSIONS: There are a number of choices to be made when constructing phenotype libraries. Our considerations distil the best practices in the field and include pointers towards their further development to support portable, reproducible, and clinically valid phenotype design. The provision of high-quality phenotype definitions enables electronic health record data to be more effectively used in medical domains.
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
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, Domínguez 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.
Tapuria A, Kordowicz M, Ashworth M, et al., 2021, IT Evaluation of Foundation Healthcare Group Vanguard Project., Stud Health Technol Inform, Vol: 281, Pages: 625-629
The aim of the Foundation Healthcare Group (FHG) Vanguard model was to develop a sustainable local hospital model between two National Health Service (NHS) Trusts (a London Teaching Hospital Trust and a District General Hospital Trust) that makes best use of scarce resources and can be replicated across the NHS, UK. The aim of this study was to evaluate the provision, use and implementation of the IT infrastructure; based on qualitative interviews and focused mainly on the perspectives of the IT staff and the clinicians' perspectives. In total 24 interview transcripts, along with 'Acute Care Collaboration' questionnaire responses, were analysed using a thematic framework for IT infrastructure, sharing themes across the vascular, paediatric and cardiovascular strands of the FHG programme. Findings indicated that Skype for Business had been an innovative and helpful development widely available to be used between the two Trusts. Clinicians initially reported lack of IT support and infrastructure expected at the outset for a national Vanguard project, but later appreciated that remote access to most clinical applications between the two Trusts became operational. The Local Care Record (LCR), an IT project was perceived to have been delivered successfully in South London. Shared technology reduced patient travelling time by providing locally based shared care. Spreading and scaling-up innovations from the Vanguard sites was the aspiration and challenge for system leaders.
Gulliford MC, Charlton J, Boiko O, et al., 2021, Safety of reducing antibiotic prescribing in primary care: a mixed-methods study
<h4>Background</h4>The threat of antimicrobial resistance has led to intensified efforts to reduce antibiotic utilisation, but serious bacterial infections are increasing in frequency.<h4>Objectives</h4>To estimate the risks of serious bacterial infections in association with lower antibiotic prescribing and understand stakeholder views with respect to safe antibiotic reduction.<h4>Design</h4>Mixed-methods research was undertaken, including a qualitative interview study of patient and prescriber views that informed a cohort study and a decision-analytic model, using primary care electronic health records. These three work packages were used to design an application (app) for primary care prescribers.<h4>Data sources</h4>The Clinical Practice Research Datalink.<h4>Setting</h4>This took place in UK general practices.<h4>Participants</h4>A total of 706 general practices with 66.2 million person-years of follow-up from 2002 to 2017 and antibiotic utilisation evaluated for 671,830 registered patients. The qualitative study included 31 patients and 30 health-care professionals from primary care.<h4>Main outcome measures</h4>Sepsis and localised bacterial infections.<h4>Results</h4>Patients were concerned about antimicrobial resistance and the side effects, as well as the benefits, of antibiotic treatment. Prescribers viewed the onset of sepsis as the most concerning potential outcome of reduced antibiotic prescribing. More than 40% of antibiotic prescriptions in primary care had no coded indication recorded across both Vision® and EMIS® practice systems. Antibiotic prescribing rates varied widely between general practices, but there was no evidence that serious bacterial infections were less frequent at higher prescribing practices (adjusted rate ratio for 20% increase in prescribing 1.03, 95% confidence interval 1.00 to 1.06; p = 0.074). The probability o
Tapuria A, Porat T, Kalra D, et al., 2021, Impact of patient access to their electronic health record: systematic review., Informatics for Health and Social Care, Vol: 46, Pages: 194-204, ISSN: 0959-8316
Patient access to their own electronic health records (EHRs) is likely to become an integral part of healthcare systems worldwide. It has the potential to decrease the healthcare provision costs, improve access to healthcare data, self-care, quality of care, and health and patient-centered outcomes. This systematic literature review is aimed at identifying the impact in terms of benefits and issues that have so far been demonstrated by providing patients access to their own EHRs, via providers' secure patient portals from primary healthcare centers and hospitals. Searches were conducted in PubMed, MEDLINE, CINHAL, and Google scholar. Over 2000 papers were screened and were filtered based on duplicates, then by reading the titles and finally based on their abstracts or full text. In total, 74 papers were retained, analyzed, and summarized. Papers were included if providing patient access to their own EHRs was the primary intervention used in the study and its impact or outcome was evaluated. The search technique used to identify relevant literature for this paper involved input from five experts. While findings from 54 of the 74 papers showed positive outcome or benefits of patient access to their EHRs via patient portals, 10 papers have highlighted concerns, 8 papers have highlighted both and 2 have highlighted absence of negative outcomes. The benefits range from re-assurance, reduced anxiety, positive impact on consultations, better doctor-patient relationship, increased awareness and adherence to medication, and improved patient outcomes (e.g., improving blood pressure and glycemic control in a range of study populations). In addition, patient access to their health information was found to improve self-reported levels of engagement or activation related to self-management, enhanced knowledge, and improve recovery scores, and organizational efficiencies in a tertiary level mental health care facility. However, three studies did not find any statistically signific
Fairweather E, Wittner R, Chapman M, et al., 2021, Non-repudiable Provenance for Clinical Decision Support Systems, Pages: 165-182, ISSN: 0302-9743
Provenance templates are now a recognised methodology for the construction of data provenance records. Each template defines the provenance of a domain-specific action in abstract form, which may then be instantiated as required by a single call to the provenance template service. As data reliability and trustworthiness becomes a critical issue in an increasing number of domains, there is a corresponding need to ensure that the provenance of that data is non-repudiable. In this paper we contribute two new, complementary modules to our template model and implementation to produce non-repudiable data provenance. The first, a module that traces the operation of the provenance template service itself, and records a provenance trace of the construction of an object-level document, at the level of individual service calls. The second, a non-repudiation module that generates evidence for the data recorded about each call, annotates the service trace accordingly, and submits a representation of that evidence to a provider-agnostic notary service. We evaluate the applicability of our approach in the context of a clinical decision support system. We first define a policy to ensure the non-repudiation of evidence with respect to a security threat analysis in order to demonstrate the suitability of our solution. We then select three use cases from within a particular system, Consult, with contrasting data provenance recording requirements and analyse the subsequent performance of our prototype implementation against three different notary providers.
Fairweather E, Chapman M, Curcin V, 2021, A Delayed Instantiation Approach to Template-Driven Provenance for Electronic Health Record Phenotyping, Pages: 3-19, ISSN: 0302-9743
Provenance templates are an established methodology for the capture of provenance data. Each template defines the provenance of a domain-specific action in abstract form, which may then be instantiated as required by a single call to a given service interface. This approach, whilst simplifying the process of recording provenance for the user, introduces computational and storage demands on the capture process, particularly when used by clients with write-intensive provenance requirements such as other service-based software. To address these issues, we adopt a new approach based upon delayed instantiation and present a revised, two-part paradigm for template-driven provenance, in which we separate capture and query functionality to improve the overall efficiency of the model. A dedicated capture service is first employed to record template service requests in a relational database in the form of a meta-level description detailing the construction of each document. These low-overhead records are then accessed by an independent query service to construct views of concrete provenance documents for specific time frames as and when required by the user. These views may subsequently be analysed using query templates, a new technique defined here whereby templates can also be used to search for any matching subgraphs within a document and return the respective instantiating substitutions. We evaluate the performance gains of our new system in the context of Phenoflow, an electronic health record (EHR) phenotyping platform.
Chapman M, Fairweather E, Khan A, et al., 2021, COVID-19 Analytics in Jupyter: Intuitive Provenance Integration Using ProvIt, Pages: 256-262, ISSN: 0302-9743
Whilst the need to record and understand the evolution of data, together with the processes and users associated with those changes, is now widely appreciated, the uptake of solutions to these issues remains slow. Data provenance techniques have the potential to provide such an understanding, but their use is often considered a specialist activity, requiring detailed knowledge of standards such as W3C PROV. In this work, we introduce ProvIt, a suite of tools designed to lower the barriers to entry for the use of provenance technology. We demonstrate the utility of ProvIt by using it to add provenance capabilities to the Jupyter IDE, in order to provide insight into the tools used by a group of researchers analysing a COVID-19 dataset.
Chapman M, Rasmussen LV, Pacheco JA, et al., 2021, Phenoflow: A Microservice Architecture for Portable Workflow-based Phenotype Definitions., AMIA Jt Summits Transl Sci Proc, Vol: 2021, Pages: 142-151
Phenotyping is an effective way to identify cohorts of patients with particular characteristics within a population. In order to enhance the portability of a phenotype definition across institutions, it is often defined abstractly, with implementers expected to realise the phenotype computationally before executing it against a dataset. However, un-clear definitions, with little information about how best to implement the definition in practice, hinder this process. To address this issue, we propose a new multi-layer, workflow-based model for defining phenotypes, and a novel authoring architecture, Phenoflow, that supports the development of these structured definitions and their realisation as computable phenotypes. To evaluate our model, we determine its impact on the portability of both code-based (COVID-19) and logic-based (diabetes) definitions, in the context of key datasets, including 26,406 patients at North-western University. Our approach is shown to ensure the portability of phenotype definitions and thus contributes to the transparency of resulting studies.
Zakeri R, Bendayan R, Ashworth M, et al., 2020, A case-control and cohort study to determine the relationship between ethnic background and severe COVID-19, EClinicalMedicine, Vol: 28
Background: People of minority ethnic backgrounds may be disproportionately affected by severe COVID-19. Whether this relates to increased infection risk, more severe disease progression, or worse in-hospital survival is unknown. The contribution of comorbidities or socioeconomic deprivation to ethnic patterning of outcomes is also unclear. Methods: We conducted a case-control and a cohort study in an inner city primary and secondary care setting to examine whether ethnic background affects the risk of hospital admission with severe COVID-19 and/or in-hospital mortality. Inner city adult residents admitted to hospital with confirmed COVID-19 (n = 872 cases) were compared with 3,488 matched controls randomly sampled from a primary healthcare database comprising 344,083 people residing in the same region. For the cohort study, we studied 1827 adults consecutively admitted with COVID-19. The primary exposure variable was self-defined ethnicity. Analyses were adjusted for socio-demographic and clinical variables. Findings: The 872 cases comprised 48.1% Black, 33.7% White, 12.6% Mixed/Other and 5.6% Asian patients. In conditional logistic regression analyses, Black and Mixed/Other ethnicity were associated with higher admission risk than white (OR 3.12 [95% CI 2.63–3.71] and 2.97 [2.30–3.85] respectively). Adjustment for comorbidities and deprivation modestly attenuated the association (OR 2.24 [1.83–2.74] for Black, 2.70 [2.03–3.59] for Mixed/Other). Asian ethnicity was not associated with higher admission risk (adjusted OR 1.01 [0.70–1.46]). In the cohort study of 1827 patients, 455 (28.9%) died over a median (IQR) of 8 (4–16) days. Age and male sex, but not Black (adjusted HR 1.06 [0.82–1.37]) or Mixed/Other ethnicity (adjusted HR 0.72 [0.47–1.10]), were associated with in-hospital mortality. Asian ethnicity was associated with higher in-hospital mortality but with a large confidence interval (adjusted HR 1.71 [1.15&ndas
Balatsoukas P, Sassoon I, Chapman M, et al., 2020, In the wild pilot usability assessment of a connected health system for stroke self management
This paper reports on the findings of a pilot study for the formative "in the wild"assessment of the usability of CONSULT, a research-led connected health system for stroke self-management and prevention. CONSULT integrates data from commercial wellness sensors, electronic health records and clinical guidelines and enables users to monitor their vital signs to support self-monitoring and provision of tailored advice. The CONSULT system includes a dashboard and a chatbot. To assess the usability of our system, six volunteers were recruited to interact with CONSULT over a period of seven days. System logs confirmed that participants interacted with the CONSULT system throughout. CONSULT's ability to integrate data from different sensors was an aspect of this systems that all participants liked and kept them motivated to track their vital signs. The study also revealed several usability issues that designers of this type of systems should consider. Some of the most prevalent issues were: information overload, data misinterpretation, need for more anthropomorphic conversational capabilities for the chatbot; lack of visibility of the data transmission status. This paper concludes with reflections on the importance of these findings when assessing the usability of connected health systems, like CONSULT.
Missier P, Bryans J, Gamble C, et al., 2020, Abstracting PROV provenance graphs: A validity-preserving approach, Future Generation Computer Systems, Vol: 111, Pages: 352-367, ISSN: 0167-739X
Data provenance is a structured form of metadata designed to record the activities and datasets involved in data production, as well as their dependency relationships. The PROV data model, released by the W3C in 2013, defines a schema and constraints that together provide a structural and semantic foundation for provenance. This enables the interoperable exchange of provenance between data producers and consumers. When the provenance content is sensitive and subject to disclosure restrictions, however, a way of hiding parts of the provenance in a principled way before communicating it to certain parties is required. In this paper we present a provenance abstraction operator that achieves this goal. It maps a graphical representation of a PROV document PG1 to a new abstract version PG2, ensuring that (i) PG2 is a valid PROV graph, and (ii) the dependencies that appear in PG2 are justified by those that appear in PG1. These two properties ensure that further abstraction of abstract PROV graphs is possible. A guiding principle of the work is that of minimum damage: the resultant graph is altered as little as possible, while ensuring that the two properties are maintained. The operator developed is implemented as part of a user tool, described in a separate paper, that lets owners of sensitive provenance information control the abstraction by specifying an abstraction policy.
Curcin V, 2020, Why does human phenomics matter today?, LEARNING HEALTH SYSTEMS, Vol: 4, ISSN: 2379-6146
Sassoon I, Kokciyan N, Chapman M, et al., 2020, Implementing argument and explanation schemes in dialogue, Pages: 471-472, ISSN: 0922-6389
Gulliford MC, Charlton J, Winter JR, et al., 2020, Probability of sepsis after infection consultations in primary care in the United Kingdom in 2002-2017: Population-based cohort study and decision analytic model., PLoS Med, Vol: 17
BACKGROUND: Efforts to reduce unnecessary antibiotic prescribing have coincided with increasing awareness of sepsis. We aimed to estimate the probability of sepsis following infection consultations in primary care when antibiotics were or were not prescribed. METHODS AND FINDINGS: We conducted a cohort study including all registered patients at 706 general practices in the United Kingdom Clinical Practice Research Datalink, with 66.2 million person-years of follow-up from 2002 to 2017. There were 35,244 first episodes of sepsis (17,886, 51%, female; median age 71 years, interquartile range 57-82 years). Consultations for respiratory tract infection (RTI), skin or urinary tract infection (UTI), and antibiotic prescriptions were exposures. A Bayesian decision tree was used to estimate the probability (95% uncertainty intervals [UIs]) of sepsis following an infection consultation. Age, gender, and frailty were evaluated as association modifiers. The probability of sepsis was lower if an antibiotic was prescribed, but the number of antibiotic prescriptions required to prevent one episode of sepsis (number needed to treat [NNT]) decreased with age. At 0-4 years old, the NNT was 29,773 (95% UI 18,458-71,091) in boys and 27,014 (16,739-65,709) in girls; over 85 years old, NNT was 262 (236-293) in men and 385 (352-421) in women. Frailty was associated with greater risk of sepsis and lower NNT. For severely frail patients aged 55-64 years, the NNT was 247 (156-459) in men and 343 (234-556) in women. At all ages, the probability of sepsis was greatest for UTI, followed by skin infection, followed by RTI. At 65-74 years, the NNT following RTI was 1,257 (1,112-1,434) in men and 2,278 (1,966-2,686) in women; the NNT following skin infection was 503 (398-646) in men and 784 (602-1,051) in women; following UTI, the NNT was 121 (102-145) in men and 284 (241-342) in women. NNT values were generally smaller for the period from 2014 to 2017, when sepsis was diagnosed more frequently.
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