Tonsillectomy among children with low baseline acute throat infection consultation rates in UK general practices: a cohort study.

OBJECTIVE: To investigate the effectiveness of tonsillectomy in reducing acute throat infection (ATI) consultation rates over 6 years' follow-up among children with low baseline ATI consultation rates. DESIGN: Retrospective cohort study. SETTING: UK general practices from the Clinical Practice Research Datalink. PARTICIPANTS: Children aged 4-15 years with ≤3 ATI consultations during the 3 years prior to 2001 (baseline). 450 children who underwent tonsillectomy (tonsillectomy group) and 13 442 other children with an ATI consultation (comparison group) in 2001. MAIN OUTCOME MEASURES: Mean differences in ATI consultation rates over the first 3 years' and subsequent 3 years' follow-up compared with 3 years prior to 2001 (baseline); odds of ≥3 ATI consultations at the same time points. RESULTS: Among children in the tonsillectomy group, the 3-year mean ATI consultation rate decreased from 1.31 to 0.66 over the first 3 years' follow-up and further declined to 0.60 over the subsequent 3 years' follow-up period. Compared with children who had no operation, those who underwent tonsillectomy experienced a reduction in 3-year mean ATI consultations per child of 2.5 (95% CI 2.3 to 2.6, p<0.001) over the first 3 years' follow-up, but only 1.2 (95% CI 1.0 to 1.4, p<0.001) over the subsequent 3 years' follow-up compared with baseline, respectively. This equates to a mean reduction of 3.7 ATI consultations over a 6-year period and approximates to a mean annual reduction of 0.6 ATI consultations per child, per year, over 6 years' follow-up. Children who underwent tonsillectomy were also much less likely to experience ≥3 ATI consultations during the first 3 years' follow-up (adjusted OR=0.12, 95% CI 0.08 to 0.17) and the subsequent 3 years' follow-up (adjusted OR=0.24, 95% CI 0.14 to 0.41). CONCLUSIONS: Among children with low baseline ATI rates, there was a statistically significant reduction in ATI consultation rates over 6 years' follow-up. However, the relatively modest clinical benefit needs to be weighed against the potential risks and complications associated with surgery

Abstracting PROV provenance graphs:A validity-preserving approach

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.</p

Establishment of Requirements and Methodology for the Development and Implementation of GreyMatters, a Memory Clinic Information System

INTRODUCTION: The aim of the paper is to establish the requirements and methodology for the development process of GreyMatters, a memory clinic system, outlining the conceptual, practical, technical and ethical challenges, and the experiences of capturing clinical and research oriented data along with the implementation of the system. METHODS: The methodology for development of the information system involved phases of requirements gathering, modeling and prototype creation, and 'bench testing' the prototype with experts. The standard Institute of Electrical and Electronics Engineers (IEEE) recommended approach for the specifications of software requirements was adopted. An electronic health record (EHR) standard, EN13606 was used, and clinical modelling was done through archetypes and the project complied with data protection and privacy legislation. RESULTS: The requirements for GreyMatters were established. Though the initial development was complex, the requirements, methodology and standards adopted made the construction, deployment, adoption and population of a memory clinic and research database feasible. The electronic patient data including the assessment scales provides a rich source of objective data for audits and research and to establish study feasibility and identify potential participants for the clinical trials. CONCLUSION: The establishment of requirements and methodology, addressing issues of data security and confidentiality, future data compatibility and interoperability and medico-legal aspects such as access controls and audit trails, led to a robust and useful system. The evaluation supports that the system is an acceptable tool for clinical, administrative, and research use and forms a useful part of the wider information architecture

TRANSFoRm eHealth solution for quality of life monitoring.

Patient Recorded Outcome Measures (PROMs) are an essential part of quality of life monitoring, clinical trials, improvement studies and other medical tasks. Recently, web and mobile technologies have been explored as means of improving the response rates and quality of data collected. Despite the potential benefit of this approach, there are currently no widely accepted standards for developing or implementing PROMs in CER (Comparative Effectiveness Research). Within the European Union project Transform (Translational Research and Patient Safety in Europe) an eHealth solution for quality of life monitoring has been developed and validated. This paper presents the overall architecture of the system as well as a detailed description of the mobile and web applications

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

Requirements and validation of a prototype learning health system for clinical diagnosis

Introduction Diagnostic error is a major threat to patient safety in the context of family practice. The patient safety implications are severe for both patient and clinician. Traditional approaches to diagnostic decision support have lacked broad acceptance for a number of well-documented reasons: poor integration with electronic health records and clinician workflow, static evidence that lacks transparency and trust, and use of proprietary technical standards hindering wider interoperability. The learning health system (LHS) provides a suitable infrastructure for development of a new breed of learning decision support tools. These tools exploit the potential for appropriate use of the growing volumes of aggregated sources of electronic health records. Methods We describe the experiences of the TRANSFoRm project developing a diagnostic decision support infrastructure consistent with the wider goals of the LHS. We describe an architecture that is model driven, service oriented, constructed using open standards, and supports evidence derived from electronic sources of patient data. We describe the architecture and implementation of 2 critical aspects for a successful LHS: the model representation and translation of clinical evidence into effective practice and the generation of curated clinical evidence that can be used to populate those models, thus closing the LHS loop. Results/Conclusions Six core design requirements for implementing a diagnostic LHS are identified and successfully implemented as part of this research work. A number of significant technical and policy challenges are identified for the LHS community to consider, and these are discussed in the context of evaluating this work: medico-legal responsibility for generated diagnostic evidence, developing trust in the LHS (particularly important from the perspective of decision support), and constraints imposed by clinical terminologies on evidence generation

Desiderata for the development of next-generation electronic health record phenotype libraries

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

Desiderata for the development of next-generation electronic health record phenotype libraries

BackgroundHigh-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.MethodsA 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.ResultsWe present 14 library desiderata that promote high-quality phenotype definitions, in the areas of modelling, logging, validation, and sharing and warehousing.ConclusionsThere 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

Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond

The last 6 years have seen sustained investment in health data science in the United Kingdom and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and well‐being. However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper, we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency. We believe a step change can be achieved through meaningful stakeholder involvement at every stage of research planning, design, and execution and team‐based data science, as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social licence for health data research and ensure transparent and secure data usage for public benefit

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

BACKGROUND: Acute otitis media is a painful infection of the middle ear that is commonly seen in children. In some children, the eardrum spontaneously bursts, discharging visible pus (otorrhoea) into the outer ear. OBJECTIVE: To compare the clinical effectiveness of immediate topical antibiotics or delayed oral antibiotics with the clinical effectiveness of immediate oral antibiotics in reducing symptom duration in children presenting to primary care with acute otitis media with discharge and the economic impact of the alternative strategies. DESIGN: This was a pragmatic, three-arm, individually randomised (stratified by age < 2 vs. ≥ 2 years), non-inferiority, open-label trial, with economic and qualitative evaluations, supported by a health-record-integrated electronic trial platform [TRANSFoRm (Translational Research and Patient Safety in Europe)] with an internal pilot. SETTING: A total of 44 English general practices. PARTICIPANTS: Children aged ≥ 12 months and < 16 years whose parents (or carers) were seeking medical care for unilateral otorrhoea (ear discharge) following recent-onset (≤ 7 days) acute otitis media. INTERVENTIONS: (1) Immediate ciprofloxacin (0.3%) solution, four drops given three times daily for 7 days, or (2) delayed 'dose-by-age' amoxicillin suspension given three times daily (clarithromycin twice daily if the child was penicillin allergic) for 7 days, with structured delaying advice. All parents were given standardised information regarding symptom management (paracetamol/ibuprofen/fluids) and advice to complete the course. COMPARATOR: Immediate 'dose-by-age' oral amoxicillin given three times daily (or clarithromycin given twice daily) for 7 days. Parents received standardised symptom management advice along with advice to complete the course. MAIN OUTCOME MEASURE: Time from randomisation to the first day on which all symptoms (pain, fever, being unwell, sleep disturbance, otorrhoea and episodes of distress/crying) were rated 'no' or 'very slight' problem (without need for analgesia). METHODS: Participants were recruited from routine primary care appointments. The planned sample size was 399 children. Follow-up used parent-completed validated symptom diaries. RESULTS: Delays in software deployment and configuration led to small recruitment numbers and trial closure at the end of the internal pilot. Twenty-two children (median age 5 years; 62% boys) were randomised: five, seven and 10 to immediate oral, delayed oral and immediate topical antibiotics, respectively. All children received prescriptions as randomised. Seven (32%) children fully adhered to the treatment as allocated. Symptom duration data were available for 17 (77%) children. The median (interquartile range) number of days until symptom resolution in the immediate oral, delayed oral and immediate topical antibiotic arms was 6 (4-9), 4 (3-7) and 4 (3-6), respectively. Comparative analyses were not conducted because of small numbers. There were no serious adverse events and six reports of new or worsening symptoms. Qualitative clinician interviews showed that the trial question was important. When the platform functioned as intended, it was liked. However, staff reported malfunctioning software for long periods, resulting in missed recruitment opportunities. Troubleshooting the software placed significant burdens on staff. LIMITATIONS: The over-riding weakness was the failure to recruit enough children. CONCLUSIONS: We were unable to answer the main research question because of a failure to reach the required sample size. Our experience of running an electronic platform-supported trial in primary care has highlighted challenges from which we have drawn recommendations for the National Institute for Health Research (NIHR) and the research community. These should be considered before such a platform is used again. TRIAL REGISTRATION: Current Controlled Trials ISRCTN12873692 and EudraCT 2017-003635-10. FUNDING: This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 67. See the NIHR Journals Library website for further project information