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Enhancing biological resilience may offer a novel way to prevent and ameliorate disease in older patients. We investigated whether changes in C-reactive protein (CRP), as a dynamic marker of the acute inflammatory response to diverse stressors, may provide a way to operationalize the concept of resilience in older adults. We tested this hypothesis by examining whether such changes could predict prognosis by identifying which individuals are at greater risk of 6-month mortality.Analysis of prospective, routinely collected datasets containing data on hospitalization, clinical chemistry and rehabilitation outcomes for rehabilitation inpatients between 1999 and 2011. Maximum CRP response during acute illness and CRP recovery indices (time and slope of CRP decay to half maximum, and to

For over a decade, Scotland has implemented and operationalized a system of Safe Havens, which provides secure analytics platforms for researchers to access linked, deidentified electronic health records (EHRs) while managing the risk of unauthorized reidentification. In this paper, a perspective is provided on the state-of-the-art Scottish Safe Haven network, including its evolution, to define the key activities required to scale the Scottish Safe Haven network's capability to facilitate research and health care improvement initiatives. A set of processes related to EHR data and their delivery in Scotland have been discussed. An interview with each Safe Haven was conducted to understand their services in detail, as well as their commonalities. The results show how Safe Havens in Scotland have protected privacy while facilitating the reuse of the EHR data. This study provides a common definition of a Safe Haven and promotes a consistent understanding among the Scottish Safe Haven network and the clinical and academic research community. We conclude by identifying areas where efficiencies across the network can be made to meet the needs of population-level studies at scale., (©Chuang Gao, Mark McGilchrist, Shahzad Mumtaz, Christopher Hall, Lesley Ann Anderson, John Zurowski, Sharon Gordon, Joanne Lumsden, Vicky Munro, Artur Wozniak, Michael Sibley, Christopher Banks, Chris Duncan, Pamela Linksted, Alastair Hume, Catherine L Stables, Charlie Mayor, Jacqueline Caldwell, Katie Wilde, Christian Cole, Emily Jefferson. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 09.03.2022.)

Introduction: The current model of medical knowledge production, transfer, and application suffers from serious shortcomings. Learning health systems (LHS) have recently emerged as a potential solution-systems in which health information generated from patients is continuously analyzed to improve knowledge that will be transferred to patient care., Method: Various approaches of data integration already exist and could be considered for the implementation of a LHS. We discuss what are the possible informatics approaches to address the functional requirements of LHS, in the specific context of primary care, and present the experience and lessons learned from the TRANSFoRm project., Result: Implemented in 4 countries around 5 systems, TRANSFoRm is based on a local-as-view data mediation approach integrating the structural and terminological models in the same framework. It clearly demonstrated that it has the potential to address the requirements for a LHS in primary care, by dealing with data fragmented across multiple points of service. Also, it has the potential to support the generation of hypotheses from the context of clinical care, retrospective and prospective research, and decision support systems that improve the relevance of medical decisions., Conclusion: The LHS approach embodies a shift from an institution-centered to a patient-centered perspective in knowledge production and transfer and can address important challenges in the primary care setting., Competing Interests: No conflict of interest to be declared by any co‐author.

Background: Bisphosphonate therapy may have actions beyond bone, including effects on cardiovascular, immune and muscle function. We tested whether bisphosphonate treatment is associated with improved outcomes in older people undergoing inpatient rehabilitation., Methods: Analysis of prospectively collected, linked routine clinical datasets. Participants were divided into never users of bisphosphonates, use prior to rehabilitation only, use after rehabilitation only, and current users (use before and after rehabilitation). We calculated change in 20-point Barthel scores during rehabilitation, adjusting for comorbid disease and laboratory data using multivariable regression analysis. Cox regression analyses were performed to analyse the association between bisphosphonate use and time to death or hospitalisation., Results: 2797 patients were included in the analysis. Current bisphosphonate users showed greater improvement in Barthel score during rehabilitation than non-users (5.0 points [95%CI 4.3-5.7] vs 3.8 [95%CI 3.6-3.9]), but no difference compared to those receiving bisphosphonates only after discharge (5.1 [95%CI 4.6-5.5]). Previous bisphosphonate use was significantly associated with time to death (adjusted hazard ratio 1.41 [95%CI 1.15-1.73]) but less strongly with time to combined endpoint of hospitalisation or death (adjusted hazard ratio 1.18 [95%CI 0.98-1.48]). Use after discharge from rehabilitation was associated with reduced risk of death (adjusted hazard ratio 0.64 [95%CI 0.55-0.73]; hazard ratio per year of bisphosphonate prescription 0.98 [95%CI 0.97-0.99])., Conclusion: Bisphosphonate use is unlikely to be causally associated with improved physical function in older people, but continuing use may be associated with lower risk of death., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: Data capture is one of the most expensive phases during the conduct of a clinical trial and the increasing use of electronic health records (EHR) offers significant savings to clinical research. To facilitate these secondary uses of routinely collected patient data, it is beneficial to know what data elements are captured in clinical trials. Therefore our aim here is to determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems., Methods: Case report forms for 23 clinical trials in differing disease areas were analyzed. Through an iterative and consensus-based process of medical informatics professionals from academia and trial experts from the European pharmaceutical industry, data elements were compiled for all disease areas and with special focus on the reporting of adverse events. Afterwards, data elements were identified and statistics acquired from hospital sites providing data to the EHR4CR project., Results: The analysis identified 133 unique data elements. Fifty elements were congruent with a published data inventory for patient recruitment and 83 new elements were identified for clinical trial execution, including adverse event reporting. Demographic and laboratory elements lead the list of available elements in hospitals EHR systems. For the reporting of serious adverse events only very few elements could be identified in the patient records., Conclusions: Common data elements in clinical trials have been identified and their availability in hospital systems elucidated. Several elements, often those related to reimbursement, are frequently available whereas more specialized elements are ranked at the bottom of the data inventory list. Hospitals that want to obtain the benefits of reusing data for research from their EHR are now able to prioritize their efforts based on this common data element list.

Background: Recruitment of study participants is a challenging process for health professionals and patients. The Translational Medicine and Patient Safety in Europe (TRANSFoRm) clinical trial tools enable automated identification, recruitment and follow-up in clinical trials, potentially saving time, effort and costs for all parties involved., Objectives: This study evaluates the acceptability and feasibility of TRANSFoRm to improve clinical trial recruitment in primary care., Methods: A feasibility study was conducted in three general practices in Poland. Participants were physicians and patients with gastro-oesophageal reflux disease. Semi-structured interviews were held to obtain feedback about the usefulness, ease of use and overall experience with the TRANSFoRm tools and to identify potential usability issues. Data were analysed thematically., Results: A total of 5 physicians and 10 patients participated in the study. Physicians were satisfied with the usefulness of the system, as it enabled easier and faster identification, recruitment and follow-up of patients compared with existing methods. Patients found the TRANSFoRm apps easy to use to report patient outcomes. However, they also felt that the apps may not be useful for patients with limited exposure to smartphone and web technologies. Two main usability issues were identified: physicians could not access the result of the randomization at the end of each visit, and participants could not locate the follow-up reminder email., Conclusions: This study provides new evidence on the acceptability and feasibility of TRANSFoRm to enable automated identification, recruitment and follow-up of study participants in primary care trials. It also helps to better understand and address users' requirements in eHealth-supported clinical research., (© The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Allopurinol reduces oxidative stress and interacts with purinergic signalling systems important in bone metabolism and muscle function. We assessed whether allopurinol use was associated with a reduced incidence of hip fracture in older people., Methods: Analysis of prospective, routinely-collected health and social care data on patients undergoing health and social work assessment in a single geographical area over a 12year period. Exposure to allopurinol was derived from linked community prescribing data, and hospitalisation for hip fracture and comorbid disease was derived from linked hospitalisation data. Fine and Gray modelling was used to model time to hip fracture accounting for the competing risk of death, incorporating previous use of allopurinol, cumulative exposure to allopurinol as a time dependent variable, and covariate adjustments., Results: 17,308 patients were alive at the time of first social work assessment without previous hip fracture; the mean age was 73years. 10,171 (59%) were female, and 1155 (8%) had at least one exposure to allopurinol. 618 (3.6%) sustained a hip fracture, and 4226 (24%) died during a mean follow-up of 7.2years. In fully-adjusted analyses, each year of allopurinol exposure conferred a hazard ratio of 1.01 (95% CI 0.99, 1.02; p=0.37) for hip fracture and 1.00 (0.99, 1.01; p=0.47) for death. Previous use of allopurinol conferred a hazard ratio of 0.76 (0.45, 1.26; p=0.28) for hip fracture and 1.13 (0.99, 1.29; p=0.07) for death., Conclusion: Greater cumulative use of allopurinol was not associated with a reduced risk of hip fracture or death in this cohort., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Unlabelled: The Learning Health System (LHS) describes linking routine healthcare systems directly with both research translation and knowledge translation as an extension of the evidence-based medicine paradigm, taking advantage of the ubiquitous use of electronic health record (EHR) systems. TRANSFoRm is an EU FP7 project that seeks to develop an infrastructure for the LHS in European primary care., Methods: The project is based on three clinical use cases, a genotype-phenotype study in diabetes, a randomised controlled trial with gastroesophageal reflux disease, and a diagnostic decision support system for chest pain, abdominal pain, and shortness of breath., Results: Four models were developed (clinical research, clinical data, provenance, and diagnosis) that form the basis of the projects approach to interoperability. These models are maintained as ontologies with binding of terms to define precise data elements. CDISC ODM and SDM standards are extended using an archetype approach to enable a two-level model of individual data elements, representing both research content and clinical content. Separate configurations of the TRANSFoRm tools serve each use case., Conclusions: The project has been successful in using ontologies and archetypes to develop a highly flexible solution to the problem of heterogeneity of data sources presented by the LHS.

Background: Combining routinely collected health and social care data on older people is essential to advance both service delivery and research for this client group. Little data is available on how to combine health and social care data; this article provides an overview of a successful data linkage process and discusses potential barriers to executing such projects., Methods and Results: We successfully obtained and linked data on older people within Dundee from three sources: Dundee Social Work Department database (30,000 individuals aged 65 years and over), healthcare data held on NHS Tayside patients by the Health Informatics Centre (400,000 individuals), Dundee, and the Dundee of Medicine for the Elderly rehabilitation database (4300 individuals). Data were linked, anonymized and transferred to a Safe Haven environment to ensuring confidentiality and strict access control. Challenges were faced around workflows, culture and documentation. Exploiting the resultant data set raises further challenges centered on database documentation, understanding the way data were collected, dealing with missing data, data validity and collection at different time periods., Conclusion: Routinely collected health and social care data sets can be linked, but significant process barriers must be overcome to allow successful linkage and integration of data and its full exploitation.

The reuse of routinely collected clinical data for clinical research is being explored as part of the drive to reduce duplicate data entry and to start making full use of the big data potential in the healthcare domain. Clinical researchers often need to extract data from patient registries and other patient record datasets for data analysis as part of clinical studies. In the TRANSFoRm project, researchers define their study requirements via a Query Formulation Workbench. We use a standardised approach to data extraction to retrieve relevant information from heterogeneous data sources, using semantic interoperability enabled via detailed clinical modelling. This approach is used for data extraction from data sources for analysis and for pre-population of electronic Case Report Forms from electronic health records in primary care clinical systems.

The HL7 RIM (Reference Information Model) is a commonly used standard for the exchange of clinical data and can be employed for integrating the patient care and clinical research domains. Yet it is not sufficiently well specified to ensure a canonical representation of structured clinical data when used for the automated evaluation of eligibility criteria from a clinical trial protocol. We present an approach to further constrain the RIM to create a common information model to hold clinical data. In order to demonstrate our approach, we identified 132 distinct data elements from 10 rich clinical trails. We then defined a taxonomy to (i) identify the types of data elements that would need to be stored and (ii) define the types of predicate that would be used to evaluate them. This informed the definition of a pattern used to represent the data, which was shown to be sufficient for storing and evaluating the clinical statements required by the trials.

Background: Pharmaceutical clinical trials are primarily conducted across many countries, yet recruitment numbers are frequently not met in time. Electronic health records store large amounts of potentially useful data that could aid in this process. The EHR4CR project aims at re-using EHR data for clinical research purposes., Objective: To evaluate whether the protocol feasibility platform produced by the Electronic Health Records for Clinical Research (EHR4CR) project can be installed and set up in accordance with local technical and governance requirements to execute protocol feasibility queries uniformly across national borders., Methods: We installed specifically engineered software and warehouses at local sites. Approvals for data access and usage of the platform were acquired and terminology mapping of local site codes to central platform codes were performed. A test data set, or real EHR data where approvals were in place, were loaded into data warehouses. Test feasibility queries were created on a central component of the platform and sent to the local components at eleven university hospitals., Results: To use real, de-identified EHR data we obtained permissions and approvals from 'data controllers' and ethics committees. Through the platform we were able to create feasibility queries, distribute them to eleven university hospitals and retrieve aggregated patient counts of both test data and de-identified EHR data., Conclusion: It is possible to install a uniform piece of software in different university hospitals in five European countries and configure it to the requirements of the local networks, while complying with local data protection regulations. We were also able set up ETL processes and data warehouses, to re-use EHR data for feasibility queries distributed over the EHR4CR platform.

GS:SFHS is a family-based genetic epidemiology study with DNA and socio-demographic and clinical data from about 24 000 volunteers across Scotland aged 18-98 years, from February 2006 to March 2011. Biological samples and anonymized data form a resource for research on the genetics of health, disease and quantitative traits of current and projected public health importance. Specific and important features of GS:SFHS include the family-based recruitment, with the intent of obtaining family groups; the breadth and depth of phenotype information, including detailed data on cognitive function, personality traits and mental health; consent and mechanisms for linkage of all data to comprehensive routine health-care records; and 'broad' consent from participants to use their data and samples for a wide range of medical research, including commercial research, and for re-contact for the potential collection of other data or samples, or for participation in related studies and the design and review of the protocol in parallel with in-depth sociological research on (potential) participants and users of the research outcomes. These features were designed to maximize the power of the resource to identify, replicate or control for genetic factors associated with a wide spectrum of illnesses and risk factors, both now and in the future.

Objectives: To perform a requirements analysis of the barriers to conducting research linking of primary care, genetic and cancer data., Methods: We extended our initial data-centric approach to include socio-cultural and business requirements. We created reference models of core data requirements common to most studies using unified modelling language (UML), dataflow diagrams (DFD) and business process modelling notation (BPMN). We conducted a stakeholder analysis and constructed DFD and UML diagrams for use cases based on simulated research studies. We used research output as a sensitivity analysis., Results: Differences between the reference model and use cases identified study specific data requirements. The stakeholder analysis identified: tensions, changes in specification, some indifference from data providers and enthusiastic informaticians urging inclusion of socio-cultural context. We identified requirements to collect information at three levels: micro- data items, which need to be semantically interoperable, meso- the medical record and data extraction, and macro- the health system and socio-cultural issues. BPMN clarified complex business requirements among data providers and vendors; and additional geographical requirements for patients to be represented in both linked datasets. High quality research output was the norm for most repositories., Conclusions: Reference models provide high-level schemata of the core data requirements. However, business requirements' modelling identifies stakeholder issues and identifies what needs to be addressed to enable participation.

Background: Clinical data are collected for routine care in family practice; there are also a growing number of genetic and cancer registry data repositories. The Translational Research and Patient Safety in Europe (TRANSFoRm) project seeks to facilitate research using linked data from more than one source. We performed a requirements analysis which identified a wide range of data and business process requirements that need to be met before linking primary care and either genetic or disease registry data., Objectives: To develop a survey to assess the readiness of data repositories to participate in linked research - the Transform International Research Readiness (TIRRE) survey., Method: We develop the questionnaire based on our requirement analysis; with questions at micro-, meso- and macro levels of granularity, study-specific questions about diabetes and gastro-oesophageal reflux disease (GORD), and research track record. The scope of the data required was extensive. We piloted this instrument, conducting ten preliminary telephone interviews to evaluate the response to the questionnaire., Results: Using feedback gained from these interviews we revised the questionnaire; clarifying questions that were difficult to answer and utilising skip logic to create different series of questions for the various types of data repository. We simplified the questionnaire replacing free-text responses with yes/no or picking list options, wherever possible. We placed the final questionnaire online and encouraged its use (www.clininf.eu/jointirre/info.html)., Conclusion: Limited field testing suggests that TIRRE is capable of collecting comprehensive and relevant data about the suitability and readiness of data repositories to participate in linked data research.

Background: Sero-prevalence is a valuable indicator of prevalence and incidence of A/H1N1 2009 infection. However, raw sero-prevalence data must be corrected for background levels of cross-reactivity (i.e. imperfect test specificity) and the effects of immunisation programmes., Methods and Findings: We obtained serum samples from a representative sample of 1563 adults resident in Scotland between late October 2009 and April 2010. Based on a microneutralisation assay, we estimate that 44% (95% confidence intervals (CIs): 40-47%) of the adult population of Scotland were sero-positive for A/H1N1 2009 influenza by 1 March 2010. Correcting for background cross-reactivity and for recorded vaccination rates by time and age group, we estimated that 34% (27-42%) were naturally infected with A/H1N1 2009 by 1 March 2010. The central estimate increases to >40% if we allow for imperfect test sensitivity. Over half of these infections are estimated to have occurred during the study period and the incidence of infection in late October 2009 was estimated at 4.3 new infections per 1000 people per day (1.2 to 7.2), falling close to zero by April 2010. The central estimate increases to over 5.0 per 1000 if we allow for imperfect test specificity. The rate of infection was higher for younger adults than older adults. Raw sero-prevalences were significantly higher in more deprived areas (likelihood ratio trend statistic = 4.92,1 df, P = 0.03) but there was no evidence of any difference in vaccination rates., Conclusions: We estimate that almost half the adult population of Scotland were sero-positive for A/H1N1 2009 influenza by early 2010 and that the majority of these individuals (except in the oldest age classes) sero-converted as a result of natural infection with A/H1N1 2009. Public health planning should consider the possibility of higher rates of infection with A/H1N1 2009 influenza in more deprived areas.

The paper documents a series of data integration workshops held in 2006 at the UK National e-Science Centre, summarizing a range of the problem/solution scenarios in multi-site and multi-scale data integration with six HealthGrid projects using schizophrenia as a domain-specific test case. It outlines emerging strategies, recommendations and objectives for collaboration on shared ontology-building and harmonization of data for multi-site trials in this domain.

Background: Generation Scotland: the Scottish Family Health Study aims to identify genetic variants accounting for variation in levels of quantitative traits underlying the major common complex diseases (such as cardiovascular disease, cognitive decline, mental illness) in Scotland., Methods/design: Generation Scotland will recruit a family-based cohort of up to 50,000 individuals (comprising siblings and parent-offspring groups) across Scotland. It will be a six-year programme, beginning in Glasgow and Tayside in the first two years (Phase 1) before extending to other parts of Scotland in the remaining four years (Phase 2). In Phase 1, individuals aged between 35 and 55 years, living in the East and West of Scotland will be invited to participate, along with at least one (and preferably more) siblings and any other first degree relatives aged 18 or over. The total initial sample size will be 15,000 and it is planned that this will increase to 50,000 in Phase 2. All participants will be asked to contribute blood samples from which DNA will be extracted and stored for future investigation. The information from the DNA, along with answers to a life-style and medical history questionnaire, clinical and biochemical measurements taken at the time of donation, and subsequent health developments over the life course (traced through electronic health records) will be stored and used for research purposes. In addition, a detailed public consultation process will begin that will allow respondents' views to shape and develop the study. This is an important aspect to the research, and forms the continuation of a long-term parallel engagement process., Discussion: As well as gene identification, the family-based study design will allow measurement of the heritability and familial aggregation of relevant quantitative traits, and the study of how genetic effects may vary by parent-of-origin. Long-term potential outcomes of this research include the targeting of disease prevention and treatment, and the development of screening tools based on the new genetic information. This study approach is complementary to other population-based genetic epidemiology studies, such as UK Biobank, which are established primarily to characterise genes and genetic risk in the population.