A National Network of Safe Havens: Scottish Perspective.

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.

Serum sodium level variability as a prognosticator in older adults.

Our aim was to explore biological variation of serum sodium levels as a method of quantifying health risk in older adults. We investigated whether dynamic changes in serum sodium levels could provide additional prognostic information to standard predictors of mortality in older people. Analysis of routinely collected clinical datasets containing information on demographics, hospitalisation, biochemistry, haematology and physical function for Dundee in-patient rehabilitation services, between 1999 and 2011. Older people admitted to inpatient rehabilitation following an acute medical or surgical hospitalisation. Five dynamic measures of sodium levels homeostasis - minimum, maximum, standard deviation, and minimum and maximum deviation from mean - were derived for each individual, using biochemistry data from the year preceding their rehabilitation discharge. Cox regression models tested for associations with time to death. Covariates included age, sex, discharge Barthel score, co-morbid diagnoses, haemoglobin, albumin and eGFR. 3021 patients were included (mean age 84 years, 1776 (58.8%) females). 1651 (54.7%) patients experienced hyponatraemia and 446 (14.8%) became hypernatraemic. Mean sodium was correlated with all mean, minimum and SD of sodium. Kaplan-Meier survival curves showed that those without sodium perturbations had the best mortality outcomes, whilst those with both hyponatremia and hypernatremia had the worst. Multivariate Cox regression showed that standard deviation and hypernatraemia were significant predictors of death in non-adjusted models, but not fully adjusted models. All dynamic measures of dysnatraemia were associated with increased mortality risk, but failed to add predictive value to established static measures after adjusting for covariates.

Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse.

BACKGROUND: Enormous amounts of data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems. OBJECTIVE: In this paper, we discuss whether data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of data. METHODS: This paper is based on the authors' experience as users of electronic health records data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records. RESULTS: We first describe the different stages in the processing of electronic health record data, as well as the different purposes for which these data are used. Given the different data processing steps and purposes, we then discuss the possible mechanisms for each individual data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature. CONCLUSIONS: There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated data collection process and environmental influences that can affect the quality of the data. Our stepwise, actor- and purpose-oriented approach may help to identify these possible sources of bias. Unless data quality issues are better understood and unless adequate controls are embedded throughout the data lifecycle, data-driven health care will not live up to its expectations. We need a data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda.

Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting.

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.

Feasibility and acceptability of TRANSFoRm to improve clinical trial recruitment in primary care.

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.

Association between kidney function, rehabilitation outcome, and survival in older patients discharged from inpatient rehabilitation.

BACKGROUND: Chronic kidney disease (CKD) is common in older people, but it is unclear if it affects survival and rehabilitation outcomes independent of comorbid conditions and physical function in this population. STUDY DESIGN: Cohort analysis of prospective, routinely collected, linked clinical data sets. SETTING & PARTICIPANTS: Patients discharged from a single inpatient geriatric rehabilitation center over a 12-year period. PREDICTORS: Admission estimated glomerular filtration rate (eGFR) category as a predictor of improvement in the 20-point Barthel score (activities of daily living measure) during rehabilitation; discharge eGFR category and Barthel score as predictors of survival postdischarge. OUTCOMES: Survival postdischarge was modeled using Cox regression analyses, unadjusted and adjusted for age, sex, morbidities (ischemic heart disease, chronic obstructive pulmonary disease, stroke, diabetes, and heart failure), Barthel score and eGFR category on discharge, and serum calcium, hemoglobin, and albumin levels. The effect of admission eGFR category on change in Barthel score during admission was modeled using analysis of covariance, adjusted for admission, Barthel score, and comorbid conditions. RESULTS: 3,012 patients were included; mean age, 84 years. 2,394 patients died during a mean follow-up of 8.3 years. Compared with patients with eGFR of 60 to 89mL/min/1.73m(2), adjusted HRs for death were 1.26 (95% CI, 1.13-1.40), 1.45 (95% CI, 1.29-1.63), and 1.68 (95% CI, 1.42-1.99) for eGFR categories of 45 to 59, 30 to 44, and <30mL/min/1.73m(2), respectively. The relationship between discharge Barthel score and survival was similar within each discharge eGFR category (HRs of 0.95, 0.93, 0.92, 0.95, and 0.90 per Barthel score point within eGFR categories of ≥90, 60-89, 45-59, 30-44, and <30mL/min/1.73m(2); P for interaction = 0.2). Similar improvements in Barthel score between admission and discharge were seen for each admission eGFR category. LIMITATIONS: Single-center study using routinely collected clinical data. CONCLUSIONS: eGFR category and Barthel score are independent risk markers for survival in older rehabilitation patients, but advanced CKD does not preclude successful rehabilitation.

Developing a survey instrument to assess the readiness of primary care data, genetic and disease registries to conduct linked research: TRANSFoRm International Research Readiness (TIRRE) survey instrument.

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.

The TRANSFoRm project: Experience and lessons learned regarding functional and interoperability requirements to support primary care.

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.

Data integration in eHealth: a domain/disease specific roadmap.

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.

Association between bisphosphonate therapy and outcomes from rehabilitation in older people.

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.

eSource for clinical trials: Implementation and evaluation of a standards-based approach in a real world trial.

OBJECTIVE: The Learning Health System (LHS) requires integration of research into routine practice. 'eSource' or embedding clinical trial functionalities into routine electronic health record (EHR) systems has long been put forward as a solution to the rising costs of research. We aimed to create and validate an eSource solution that would be readily extensible as part of a LHS. MATERIALS AND METHODS: The EU FP7 TRANSFoRm project's approach is based on dual modelling, using the Clinical Research Information Model (CRIM) and the Clinical Data Integration Model of meaning (CDIM) to bridge the gap between clinical and research data structures, using the CDISC Operational Data Model (ODM) standard. Validation against GCP requirements was conducted in a clinical site, and a cluster randomised evaluation by site nested into a live clinical trial. RESULTS: Using the form definition element of ODM, we linked precisely modelled data queries to data elements, constrained against CDIM concepts, to enable automated patient identification for specific protocols and pre-population of electronic case report forms (e-CRF). Both control and eSource sites recruited better than expected with no significant difference. Completeness of clinical forms was significantly improved by eSource, but Patient Related Outcome Measures (PROMs) were less well completed on smartphones than paper in this population. DISCUSSION: The TRANSFoRm approach provides an ontologically-based approach to eSource in a low-resource, heterogeneous, highly distributed environment, that allows precise prospective mapping of data elements in the EHR. CONCLUSION: Further studies using this approach to CDISC should optimise the delivery of PROMS, whilst building a sustainable infrastructure for eSource with research networks, trials units and EHR vendors.

Generation Scotland: the Scottish Family Health Study; a new resource for researching genes and heritability.

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.

Association between allopurinol use and hip fracture in older patients.

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.

Slower Decline in C-Reactive Protein after an Inflammatory Insult Is Associated with Longer Survival in Older Hospitalised Patients.

BACKGROUND: 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. METHODS: 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 <50mg/L if peak values were greater than 50mg/L) was derived from biochemistry data. 6-month survival plots were conducted on quartiles of CRP recovery indices. Cox proportional hazards models were used to test univariate and multivariate predictors of 6-month mortality. Covariates included age, sex, number of medications, serum calcium, haemoglobin level, renal function, and the presence of previous myocardial infarction, stroke, chronic heart failure, COPD and diabetes. RESULTS: 3723 patients, mean age 84 years, were included. 1535 (41%) were male and 733 (20%) died during six-month follow-up. The lower an individual's peak CRP reading, and the longer the time taken for their CRP to fall, the better their 6-month survival. The time for CRP to reach half of its maximum value was the best dynamic CRP index of survival (HR 0.93 per week, 95% CI 0.89 to 0.98; p = 0.004); this remained significant even after adjustment for maximum CRP level and covariates listed above. CONCLUSION: CRP recovery indices are associated with survival in older people; further work is required to explain differences in physiology between patients with a fast and slow CRP recovery.

Construction of a linked health and social care database resource--lessons on process, content and culture.

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.

Translational Medicine and Patient Safety in Europe: TRANSFoRm--Architecture for the Learning Health System in Europe.

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.

An approach for utilizing clinical statements in HL7 RIM to evaluate eligibility criteria.

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.

Detailed clinical modelling approach to data extraction from heterogeneous data sources for clinical research.

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.

Cardiovascular risk factors associated with the metabolic syndrome are more prevalent in people reporting chronic pain: results from a cross-sectional general population study.

To explore whether chronic pain is associated with cardiovascular risk factors and identify whether increased distribution or intensity of pain is associated with cardiovascular risk, participants in Generation Scotland: The Scottish Family Health study completed pain questionnaires recording the following: presence of chronic pain, distribution of pain, and intensity of chronic pain. Blood pressure, lipids, blood glucose, smoking history, waist-hip ratio, and body mass index were recorded; Framingham 10-year coronary heart disease (CHD) risk scores were calculated and a diagnosis of metabolic syndrome derived. Associations between chronic pain and cardiovascular risk were explored. Of 13,328 participants, 1100 (8.3%) had high CHD risk. Chronic pain was reported by 5209 (39%), 1294 (9.7%) reported widespread chronic pain, and 707 (5.3%) reported high-intensity chronic pain. In age- and gender-adjusted analyses, chronic pain was associated with elevated CHD risk scores (odds ratio 1.11, 95% confidence interval 1.01-1.23) and the metabolic syndrome (odds ratio 1.42, 95% confidence interval 1.24-1.62). Multivariate analyses identified dyslipidaemia, age, gender, smoking, obesity, and high waist-hip ratio as independently associated with chronic pain. Within the chronic pain subgroup, widespread pain did not confer any additional cardiovascular disease risk. However, cardiovascular disease risk factors contributing to metabolic syndrome were more prevalent in those reporting high-intensity chronic pain. This large population-based study has demonstrated that chronic pain, and in particular high-intensity chronic pain, is associated with an increased prevalence of cardiovascular risk factors and metabolic syndrome. The 10-year CHD risk score and metabolic syndrome correlate well with increased pain intensity, but not with widespread pain.

A unified structural/terminological interoperability framework based on LexEVS: application to TRANSFoRm.

OBJECTIVE: Biomedical research increasingly relies on the integration of information from multiple heterogeneous data sources. Despite the fact that structural and terminological aspects of interoperability are interdependent and rely on a common set of requirements, current efforts typically address them in isolation. We propose a unified ontology-based knowledge framework to facilitate interoperability between heterogeneous sources, and investigate if using the LexEVS terminology server is a viable implementation method. MATERIALS AND METHODS: We developed a framework based on an ontology, the general information model (GIM), to unify structural models and terminologies, together with relevant mapping sets. This allowed a uniform access to these resources within LexEVS to facilitate interoperability by various components and data sources from implementing architectures. RESULTS: Our unified framework has been tested in the context of the EU Framework Program 7 TRANSFoRm project, where it was used to achieve data integration in a retrospective diabetes cohort study. The GIM was successfully instantiated in TRANSFoRm as the clinical data integration model, and necessary mappings were created to support effective information retrieval for software tools in the project. CONCLUSIONS: We present a novel, unifying approach to address interoperability challenges in heterogeneous data sources, by representing structural and semantic models in one framework. Systems using this architecture can rely solely on the GIM that abstracts over both the structure and coding. Information models, terminologies and mappings are all stored in LexEVS and can be accessed in a uniform manner (implementing the HL7 CTS2 service functional model). The system is flexible and should reduce the effort needed from data sources personnel for implementing and managing the integration.