UK Research Information Shared Service (UKRISS) Final Report, July 2014

The reporting of research information is a complex and expensive activity for research organisations (ROs). There is little alignment between funders of the reporting requests made to institutions and requests made to individual researchers about their research outputs and outcomes. This inevitably results in duplication and increased costs across the sector, whilst limiting the potential sharing and reuse of the information. The UK Research Information Shared Service (UKRISS) project conducted a feasibility and scoping study for the reporting of research information at a national level based on CERIF (Common European Research Information Format), with the objective of increasing efficiency, productivity and quality across the sector. The aim was to define and prototype solutions which are compelling, easy to use, have a low entry barrier, and support innovative information sharing and benchmarking. CERIF has emerged as the preferred format for expressing research information across Europe. To date, CERIF has been piloted for specific applications, but not as a format for reporting requirements across all UK ROs. The final report presents the work carried out by the UKRISS project, including requirements gathering, modelling and prototyping, as well as recommendation for sustainability. UKRISS was divided into two phases. Phase 1, mapping the reporting landscape, ran from March 2012 to December 2012. Phase 2, exploring delivery of potential solutions, began in February 2013 and ended in December 2013

Analyzing the heterogeneity of rule-based EHR phenotyping algorithms in CALIBER and the UK Biobank

Electronic Health Records (EHR) are data generated during routine interactions across healthcare settings and contain rich, longitudinal information on diagnoses, symptoms, medications, investigations and tests. A primary use-case for EHR is the creation of phenotyping algorithms used to identify disease status, onset and progression or extraction of information on risk factors or biomarkers. Phenotyping however is challenging since EHR are collected for different purposes, have variable data quality and often require significant harmonization. While considerable effort goes into the phenotyping process, no consistent methodology for representing algorithms exists in the UK. Creating a national repository of curated algorithms can potentially enable algorithm dissemination and reuse by the wider community. A critical first step is the creation of a robust minimum information standard for phenotyping algorithm components (metadata, implementation logic, validation evidence) which involves identifying and reviewing the complexity and heterogeneity of current UK EHR algorithms. In this study, we analyzed all available EHR phenotyping algorithms (n=70) from two large-scale contemporary EHR resources in the UK (CALIBER and UK Biobank). We documented EHR sources, controlled clinical terminologies, evidence of algorithm validation, representation and implementation logic patterns. Understanding the heterogeneity of UK EHR algorithms and identifying common implementation patterns will facilitate the design of a minimum information standard for representing and curating algorithms nationally and internationally

Efficiently Reusing Natural Language Processing Models for Phenotype Identification in Free-text Electronic Medical Records: Methodological Study

Background: Many efforts have been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records to construct comprehensive patient profiles for delivering better health-care. Reusing NLP models in new settings, however, remains cumbersome - requiring validation and/or retraining on new data iteratively to achieve convergent results. Objective: The aim of this work is to minimise the effort involved in reusing NLP models on free-text medical records. Methods: We formally define and analyse the model adaptation problem in phenotype identification tasks. We identify “duplicate waste” and “imbalance waste”, which collectively impede efficient model reuse. We propose a concept embedding based approach to minimise these sources of waste without the need for labelled data from new settings. Results: We conduct experiments on data from a large mental health registry to reuse NLP models in four phenotype identification tasks. The proposed approach can choose the best model for a new task, identifying up to 76% of phenotype mentions without the need for validation and model retraining, and with very good performance (93-97% accuracy). It can also provide guidance for validating and retraining the selected model for novel language patterns in new tasks, saving around 80% of the effort required in “blind” model-adaptation approaches. Conclusions: Adapting pre-trained NLP models for new tasks can be more efficient and effective if the language pattern landscapes of old settings and new settings can be made explicit and comparable. Our experiments show that the phenotype embedding approach is an effective way to model language patterns for phenotype identification tasks and that its use can guide efficient NLP model reuse

Mental health in UK Biobank - development, implementation and results from an online questionnaire completed by 157 366 participants: a reanalysis

Background UK Biobank is a well-characterised cohort of over 500 000 participants including genetics, environmental data and imaging. An online mental health questionnaire was designed for UK Biobank participants to expand its potential. Aims Describe the development, implementation and results of this questionnaire. Method An expert working group designed the questionnaire, using established measures where possible, and consulting a patient group. Operational criteria were agreed for defining likely disorder and risk states, including lifetime depression, mania/hypomania, generalised anxiety disorder, unusual experiences and self-harm, and current post-traumatic stress and hazardous/harmful alcohol use. Results A total of 157 366 completed online questionnaires were available by August 2017. Participants were aged 45–82 (53% were ≥65 years) and 57% women. Comparison of self-reported diagnosed mental disorder with a contemporary study shows a similar prevalence, despite respondents being of higher average socioeconomic status. Lifetime depression was a common finding, with 24% (37 434) of participants meeting criteria and current hazardous/harmful alcohol use criteria were met by 21% (32 602), whereas other criteria were met by less than 8% of the participants. There was extensive comorbidity among the syndromes. Mental disorders were associated with a high neuroticism score, adverse life events and long-term illness; addiction and bipolar affective disorder in particular were associated with measures of deprivation. Conclusions The UK Biobank questionnaire represents a very large mental health survey in itself, and the results presented here show high face validity, although caution is needed because of selection bias. Built into UK Biobank, these data intersect with other health data to offer unparalleled potential for crosscutting biomedical research involving mental health

Effects of antiplatelet therapy after stroke due to intracerebral haemorrhage (RESTART): a randomised, open-label trial

Antiplatelet therapy reduces the risk of major vascular events for people with occlusive vascular disease, although it might increase the risk of intracranial haemorrhage. Patients surviving the commonest subtype of intracranial haemorrhage, intracerebral haemorrhage, are at risk of both haemorrhagic and occlusive vascular events, but whether antiplatelet therapy can be used safely is unclear. We aimed to estimate the relative and absolute effects of antiplatelet therapy on recurrent intracerebral haemorrhage and whether this risk might exceed any reduction of occlusive vascular events

Effects of Antiplatelet Therapy After Stroke Caused by Intracerebral Hemorrhage Extended Follow-up of the RESTART Randomized Clinical Trial

Importance: The Restart or Stop Antithrombotics Randomized Trial (RESTART) found that antiplatelet therapy appeared to be safe up to 5 years after intracerebral hemorrhage (ICH) that had occurred during antithrombotic (antiplatelet or anticoagulant) therapy. Objectives: To monitor adherence, increase duration of follow-up, and improve precision of estimates of the effects of antiplatelet therapy on recurrent ICH and major vascular events. Design, Setting and Participants: From May 22, 2013, through May 31, 2018, this prospective, open, blinded end point, parallel-group randomized clinical trial studied 537 participants at 122 hospitals in the UK. Participants were individuals 18 years or older who had taken antithrombotic therapy for the prevention of occlusive vascular disease when they developed ICH, discontinued antithrombotic therapy, and survived for 24 hours. After initial follow-up ended on November 30, 2018, annual follow-up was extended until November 30, 2020, for a median of 3.0 years (interquartile range [IQR], 2.0-5.0 years) for the trial cohort. Interventions: Computerized randomization that incorporated minimization allocated participants (1:1) to start or avoid antiplatelet therapy. Main Outcomes and Measures: Participants were followed up for the primary outcome (recurrent symptomatic ICH) and secondary outcomes (all major vascular events) for up to 7 years. Data from all randomized participants were analyzed using Cox proportional hazards regression, adjusted for minimization covariates. Results: A total of 537 patients (median age, 76.0 years; IQR, 69.0-82.0 years; 360 [67.0%] male; median time after ICH onset, 76.0 days; IQR, 29.0-146.0 days) were randomly allocated to start (n = 268) or avoid (n = 269 [1 withdrew]) antiplatelet therapy. The primary outcome of recurrent ICH affected 22 of 268 participants (8.2%) allocated to antiplatelet therapy compared with 25 of 268 participants (9.3%) allocated to avoid antiplatelet therapy (adjusted hazard ratio, 0.87; 95% CI, 0.49-1.55; P = .64). A major vascular event affected 72 participants (26.8%) allocated to antiplatelet therapy compared with 87 participants (32.5%) allocated to avoid antiplatelet therapy (hazard ratio, 0.79; 95% CI, 0.58-1.08; P = .14). Conclusions and Relevance: Among patients with ICH who had previously taken antithrombotic therapy, this study found no statistically significant effect of antiplatelet therapy on recurrent ICH or all major vascular events. These findings provide physicians with some reassurance about the use of antiplatelet therapy after ICH if indicated for secondary prevention of major vascular events