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BackgroundMultimorbidity poses major challenges to healthcare systems worldwide. Definitions with cut-offs in excess of ≥2 long-term conditions (LTCs) might better capture populations with complexity but are not standardised.AimTo examine variation in prevalence using different definitions of multimorbidity.Design and settingCross-sectional study of 1 168 620 people in England.MethodComparison of multimorbidity (MM) prevalence using four definitions: MM2+ (≥2 LTCs), MM3+ (≥3 LTCs), MM3+ from 3+ (≥3 LTCs from ≥3 International Classification of Diseases, 10th revision chapters), and mental–physical MM (≥2 LTCs where ≥1 mental health LTC and ≥1 physical health LTC are recorded). Logistic regression was used to examine patient characteristics associated with multimorbidity under all four definitions.ResultsMM2+ was most common (40.4%) followed by MM3+ (27.5%), MM3+ from 3+ (22.6%), and mental–physical MM (18.9%). MM2+, MM3+, and MM3+ from 3+ were strongly associated with oldest age (adjusted odds ratio [aOR] 58.09, 95% confidence interval [CI] = 56.13 to 60.14; aOR 77.69, 95% CI = 75.33 to 80.12; and aOR 102.06, 95% CI = 98.61 to 105.65; respectively), but mental–physical MM was much less strongly associated (aOR 4.32, 95% CI = 4.21 to 4.43). People in the most deprived decile had equivalent rates of multimorbidity at a younger age than those in the least deprived decile. This was most marked in mental–physical MM at 40–45 years younger, followed by MM2+ at 15–20 years younger, and MM3+ and MM3+ from 3+ at 10–15 years younger. Females had higher prevalence of multimorbidity under all definitions, which was most marked for mental–physical MM.ConclusionEstimated prevalence of multimorbidity depends on the definition used, and associations with age, sex, and socioeconomic position vary between definitions. Applicable multimorbidity research requires consistency of definitions across studies.

BackgroundMultimorbidity prevalence rates vary considerably depending on the conditions considered in the morbidity count, but there is no standardised approach to the number or selection of conditions to include.Methods and FindingsWe conducted a cross-sectional study using English primary care data for 1168260 participants who were all people alive and permanently registered with 149 included general practices. Outcome measures of the study were prevalence estimates of multimorbidity when varying the number and selection of conditions considered (≥two conditions) for 80 conditions. Included conditions featured in ≥one of the nine published lists of conditions examined in the study and/or phenotyping algorithms in the Health Data Research UK Phenotype Library. First, multimorbidity prevalence was calculated when considering the individually most common two conditions, three conditions, etc, up to 80 conditions. Second, prevalence was calculated using nine condition-lists from published studies. Analyses were stratified by dependent variables age, socioeconomic position, and sex. Prevalence when only the two commonest conditions were considered was 4.6% (95%CI [4.6,4.6] p 99% of that measured when considering all 80 conditions was 52 for the whole population but was lower in older people (29 in >80 years) and higher in younger people (71 in 0–9-year-olds). Nine published condition-lists were examined; these were either recommended for measuring multimorbidity, used in previous highly cited studies of multimorbidity prevalence, or widely applied measures of ‘comorbidity’. Multimorbidity prevalence using these lists varied from 11.1% to 36.4%. A limitation of the study is that conditions were not always replicated using the same ascertainment rules as previous studies to improve comparability across condition lists, but this highlights further variability in prevalence estimates across studies.ConclusionsIn this study we observed that varying the number and selection of conditions results in very large differences in multimorbidity prevalence, and different numbers of conditions are needed to reach ceiling rates of multimorbidity prevalence in certain groups of people. These findings imply that there is a need for a standardised approach to defining multimorbidity, and to facilitate this, researchers can use existing condition-lists associated with highest multimorbidity prevalence.Author summaryWhy was this study done?There is wide variety in the conditions considered by researchers when measuring multimorbidity prevalence.A systematic review of 566 studies, published in 2021, found lack of consensus in the selection of conditions considered.In half of studies only eight conditions (diabetes, stroke, cancer, chronic obstructive pulmonary disease, hypertension, coronary heart disease, chronic kidney disease, and heart failure) were consistently considered; and the number of conditions considered varied from 2 to 285 (median 17).A more consistent approach to measuring multimorbidity is needed to facilitate comparability and generalisability across studies.What did the researchers do and find?This study investigated the relationship between the number and selection of conditions considered and the impact on multimorbidity prevalence.There are large differences in prevalence, a range of 4.6% to 40.5%, when different numbers and selections of conditions are considered.Nine published condition-lists were examined; including those recommended for measuring multimorbidity, previously used to measure multimorbidity prevalence, or measures of ‘comorbidity’.Highest multimorbidity prevalence was found when using Ho always + usually (a list derived from a recent Delphi consensus study), Barnett (widely used to measure multimorbidity prevalence), and Fortin (a list recommended for use in measuring multimorbidity).People who are the oldest, living in the most deprived areas, and men require fewer conditions to be considered to reach close to multimorbidity prevalence when considering all 80 conditions (the ceiling effect, where the prevalence approaches the upper limit of prevalence possible in the study).What do these findings mean?All conditions were counted in the same way (the presence of the condition ever recorded) to improve comparability, however in previous studies conditions were counted according to varying rules, highlighting that further variability in prevalence estimates across studies will happen because of variation in how each condition is measured.There is a need for standardisation when measuring multimorbidity prevalence so that results across studies are comparable and population subgroups are accurately represented.To address this, researchers can consider using the Ho always + usually, Barnett, or Fortin condition lists.

BACKGROUND COVID-19 data have been generated across the UK as a by-product of clinical care and public health provision, and numerous bespoke and repurposed research endeavours. Analysis of these data has underpinned the UK’s response to the pandemic and informed public health policies and clinical guidelines. However, these data are held by different organisations and this fragmented landscape has presented challenges for public health agencies and researchers as they struggle to find, navigate permissions to access and interrogate the data they need to inform the pandemic response at pace. OBJECTIVE To transform UK COVID-19 diagnostic datasets to be Findable, Accessible, Interoperable and Reusable (FAIR). METHODS A federated infrastructure model was rapidly built to enable the automated and reproducible mapping of health Data Partners’ pseudonymised data to the OMOP common data model without the need for any data to leave the data controllers’ secure environments and to support federated cohort discovery queries and meta-analysis. RESULTS 56 datasets from 19 organisations are being connected to the federated network. The data includes research cohorts and COVID-19 data collected through routine health care provision linked to longitudinal healthcare records and demographics. The infrastructure is live, supporting aggregate level querying of data across the UK. CONCLUSIONS CO-CONNECT was developed by a multidisciplinary team enabling rapid COVID-19 data discovery, instantaneous meta-analysis across data sources, and is researching streamlined data extraction for egress into a Trusted Research Environment (TRE) for research and public health analysis. CO-CONNECT has the potential to make UK health data more interconnected and better able to answer national-level research questions whilst maintaining patient confidentiality and local governance procedures. CLINICALTRIAL

ContextIdentification of patients with endocrine forms of hypertension (EHT) (primary hyperaldosteronism [PA], pheochromocytoma/paraganglioma [PPGL], and Cushing syndrome [CS]) provides the basis to implement individualized therapeutic strategies. Targeted metabolomics (TM) have revealed promising results in profiling cardiovascular diseases and endocrine conditions associated with hypertension.ObjectiveUse TM to identify distinct metabolic patterns between primary hypertension (PHT) and EHT and test its discriminating ability.MethodsRetrospective analyses of PHT and EHT patients from a European multicenter study (ENSAT-HT). TM was performed on stored blood samples using liquid chromatography mass spectrometry. To identify discriminating metabolites a “classical approach” (CA) (performing a series of univariate and multivariate analyses) and a “machine learning approach” (MLA) (using random forest) were used.The study included 282 adult patients (52% female; mean age 49 years) with proven PHT (n = 59) and EHT (n = 223 with 40 CS, 107 PA, and 76 PPGL), respectively.ResultsFrom 155 metabolites eligible for statistical analyses, 31 were identified discriminating between PHT and EHT using the CA and 27 using the MLA, of which 16 metabolites (C9, C16, C16:1, C18:1, C18:2, arginine, aspartate, glutamate, ornithine, spermidine, lysoPCaC16:0, lysoPCaC20:4, lysoPCaC24:0, PCaeC42:0, SM C18:1, SM C20:2) were found by both approaches. The receiver operating characteristic curve built on the top 15 metabolites from the CA provided an area under the curve (AUC) of 0.86, which was similar to the performance of the 15 metabolites from MLA (AUC 0.83).ConclusionTM identifies distinct metabolic pattern between PHT and EHT providing promising discriminating performance.

Background Arterial hypertension represents a worldwide health burden and a major risk factor for cardiovascular morbidity and mortality. Hypertension can be primary (primary hypertension, PHT), or secondary to endocrine disorders (endocrine hypertension, EHT), such as Cushing's syndrome (CS), primary aldosteronism (PA), and pheochromocytoma/paraganglioma (PPGL). Diagnosis of EHT is currently based on hormone assays. Efficient detection remains challenging, but is crucial to properly orientate patients for diagnostic confirmation and specific treatment. More accurate biomarkers would help in the diagnostic pathway. We hypothesized that each type of endocrine hypertension could be associated with a specific blood DNA methylation signature, which could be used for disease discrimination. To identify such markers, we aimed at exploring the methylome profiles in a cohort of 255 patients with hypertension, either PHT (n = 42) or EHT (n = 213), and at identifying specific discriminating signatures using machine learning approaches. Results Unsupervised classification of samples showed discrimination of PHT from EHT. CS patients clustered separately from all other patients, whereas PA and PPGL showed an overall overlap. Global methylation was decreased in the CS group compared to PHT. Supervised comparison with PHT identified differentially methylated CpG sites for each type of endocrine hypertension, showing a diffuse genomic location. Among the most differentially methylated genes, FKBP5 was identified in the CS group. Using four different machine learning methods—Lasso (Least Absolute Shrinkage and Selection Operator), Logistic Regression, Random Forest, and Support Vector Machine—predictive models for each type of endocrine hypertension were built on training cohorts (80% of samples for each hypertension type) and estimated on validation cohorts (20% of samples for each hypertension type). Balanced accuracies ranged from 0.55 to 0.74 for predicting EHT, 0.85 to 0.95 for predicting CS, 0.66 to 0.88 for predicting PA, and 0.70 to 0.83 for predicting PPGL. Conclusions The blood DNA methylome can discriminate endocrine hypertension, with methylation signatures for each type of endocrine disorder.

Background The National COVID-19 Chest Imaging Database (NCCID) is a centralized database containing mainly chest X-rays and computed tomography scans from patients across the UK. The objective of the initiative is to support a better understanding of the coronavirus SARS-CoV-2 disease (COVID-19) and the development of machine learning technologies that will improve care for patients hospitalized with a severe COVID-19 infection. This article introduces the training dataset, including a snapshot analysis covering the completeness of clinical data, and availability of image data for the various use-cases (diagnosis, prognosis, longitudinal risk). An additional cohort analysis measures how well the NCCID represents the wider COVID-19–affected UK population in terms of geographic, demographic, and temporal coverage. Findings The NCCID offers high-quality DICOM images acquired across a variety of imaging machinery; multiple time points including historical images are available for a subset of patients. This volume and variety make the database well suited to development of diagnostic/prognostic models for COVID-associated respiratory conditions. Historical images and clinical data may aid long-term risk stratification, particularly as availability of comorbidity data increases through linkage to other resources. The cohort analysis revealed good alignment to general UK COVID-19 statistics for some categories, e.g., sex, whilst identifying areas for improvements to data collection methods, particularly geographic coverage. Conclusion The NCCID is a growing resource that provides researchers with a large, high-quality database that can be leveraged both to support the response to the COVID-19 pandemic and as a test bed for building clinically viable medical imaging models.

Background The National COVID-19 Chest Imaging Database (NCCID) is a centralized database containing mainly chest X-rays and computed tomography scans from patients across the UK. The objective of the initiative is to support a better understanding of the coronavirus SARS-CoV-2 disease (COVID-19) and the development of machine learning technologies that will improve care for patients hospitalized with a severe COVID-19 infection. This article introduces the training dataset, including a snapshot analysis covering the completeness of clinical data, and availability of image data for the various use-cases (diagnosis, prognosis, longitudinal risk). An additional cohort analysis measures how well the NCCID represents the wider COVID-19–affected UK population in terms of geographic, demographic, and temporal coverage. Findings The NCCID offers high-quality DICOM images acquired across a variety of imaging machinery; multiple time points including historical images are available for a subset of patients. This volume and variety make the database well suited to development of diagnostic/prognostic models for COVID-associated respiratory conditions. Historical images and clinical data may aid long-term risk stratification, particularly as availability of comorbidity data increases through linkage to other resources. The cohort analysis revealed good alignment to general UK COVID-19 statistics for some categories, e.g., sex, whilst identifying areas for improvements to data collection methods, particularly geographic coverage. Conclusion The NCCID is a growing resource that provides researchers with a large, high-quality database that can be leveraged both to support the response to the COVID-19 pandemic and as a test bed for building clinically viable medical imaging models.

BACKGROUND A Trusted Research Environment (also known as a Safe Haven) is an environment supported by trained staff and agreed processes (principles and standards) providing access to data for research whilst protecting patient confidentially. Accessing sensitive data without compromising the privacy and security of the data is a complex process. OBJECTIVE This paper presents the security measures, administrative procedures and technical approaches adopted by TREs. METHODS We contacted TRE operators, 20 of whom, in the UK and internationally, agreed to be interviewed remotely under a non-disclosure agreement and to complete a questionnaire about their TRE. RESULTS We observed many similar processes and standards which TREs follow to adhere to the Seven Safes principles. The security processes and TRE capabilities for supporting observational studies using classical statistical methods were mature and the requirements well understood. However, we identified limitations in the security measures and capabilities of TREs to support “next-generation” requirements such as wider ranges of data types, the ability to develop artificial intelligence algorithms and software within the environment, the handling of big data, and timely import and export of data. CONCLUSIONS We found a lack of software/automation tools to support the community and limited knowledge of how to meet next-generation requirements from the research community. Disclosure control for exporting artificial intelligence (AI) algorithms and software was found to be particularly challenging where there is a clear need for additional controls to support this capability within TREs.

UNSTRUCTURED 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.

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.

BackgroundScotland abolished the Quality and Outcomes Framework (QOF) in April 2016, before implementing a new Scottish GP contract in April 2018. Since 2016, groups of practices (GP clusters) have been incentivised to meet regularly to plan and organise quality improvement (QI) as part of this new direction in primary care policy.AimTo understand the organisation and perceived impact of GP clusters, including how they use quantitative data for improvement.Design and settingThematic analysis of semi-structured interviews with key stakeholders (n = 17) and observations of GP cluster meetings (n = 6) in two clusters.MethodThis analytical strategy was combined with a purposive (variation) sampling approach to the sources of data, to try to identify commonalities across diverse stakeholder experiences of working in or on the idea of GP clusters. Variation was sought particularly in terms of stakeholders’ level of involvement in improvement initiatives, and in their disciplinary affiliations.ResultsThere was uncertainty as to whether GP clusters should focus on activities generated internally or externally by the wider healthcare system (for example, from Scottish Health Boards), although the two observed clusters generally generated their own ideas and issues. Clusters operated with variable administrative/managerial and data support, and variable baseline leadership experience and QI skills. Qualitative approaches formed the focus of collaborative learning in cluster meetings, through sharing and discussion of member practices’ own understandings and experiences. Less evidence was observed of data analytics being championed in these meetings, partly because of barriers to accessing the analytics data and existing data quality.ConclusionCluster development would benefit from more consistent training and support for cluster leads in small-group facilitation, leadership, and QI expertise, and data analytics access and capacity. While GP clusters are up and running, their impact is likely to be limited without further investment in developing capacity in these areas.

ObjectiveTo monitor hospital activity for presentation, diagnosis and treatment of cardiovascular diseases during the COVID-19) pandemic to inform on indirect effects.MethodsRetrospective serial cross-sectional study in nine UK hospitals using hospital activity data from 28 October 2019 (pre-COVID-19) to 10 May 2020 (pre-easing of lockdown) and for the same weeks during 2018–2019. We analysed aggregate data for selected cardiovascular diseases before and during the epidemic. We produced an online visualisation tool to enable near real-time monitoring of trends.ResultsAcross nine hospitals, total admissions and emergency department (ED) attendances decreased after lockdown (23 March 2020) by 57.9% (57.1%–58.6%) and 52.9% (52.2%–53.5%), respectively, compared with the previous year. Activity for cardiac, cerebrovascular and other vascular conditions started to decline 1–2 weeks before lockdown and fell by 31%–88% after lockdown, with the greatest reductions observed for coronary artery bypass grafts, carotid endarterectomy, aortic aneurysm repair and peripheral arterial disease procedures. Compared with before the first UK COVID-19 (31 January 2020), activity declined across diseases and specialties between the first case and lockdown (total ED attendances relative reduction (RR) 0.94, 0.93–0.95; total hospital admissions RR 0.96, 0.95–0.97) and after lockdown (attendances RR 0.63, 0.62–0.64; admissions RR 0.59, 0.57–0.60). There was limited recovery towards usual levels of some activities from mid-April 2020.ConclusionsSubstantial reductions in total and cardiovascular activities are likely to contribute to a major burden of indirect effects of the pandemic, suggesting they should be monitored and mitigated urgently.

Aim To enable a world-leading research dataset of routinely collected clinical images linked to other routinely collected data from the whole Scottish national population. This includes more than 30 million different radiological examinations from a population of 5.4 million and >2 PB of data collected since 2010. Methods Scotland has a central archive of radiological data used to directly provide clinical care to patients. We have developed an architecture and platform to securely extract a copy of those data, link it to other clinical or social datasets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment. Results An extensive software platform has been developed to host, extract, and link data from cohorts to answer research questions. The platform has been tested on 5 different test cases and is currently being further enhanced to support 3 exemplar research projects. Conclusions The data available are from a range of radiological modalities and scanner types and were collected under different environmental conditions. These real-world, heterogenous data are valuable for training algorithms to support clinical decision making, especially for deep learning where large data volumes are required. The resource is now available for international research access. The platform and data can support new health research using artificial intelligence and machine learning technologies, as well as enabling discovery science.

Since the emergence of the novel coronavirus SARS-CoV-2 in Wuhan, China in 2019 [1], the resulting COVID-19 disease has rapidly transitioned into a global pandemic with over 31 000 deaths and 215 000 infections in the UK as of 10th May 2020 [2]. Confirmation of SARS-CoV-2 infection requires reverse-transcriptase polymerase chain reaction (RT-PCR) testing [3]. Chest radiographic and/or computed tomography imaging is also central to diagnosis and management [4]., The National COVID-19 Chest Imaging Database (NCCID) is a repository of chest X-Ray, CT and MRI images and clinical data from COVID-19 patients across the UK, to support research and development of AI technology that may proffer insights into the disease.

BackgroundThe coronavirus (COVID-19) pandemic affects cardiovascular diseases (CVDs) directly through infection and indirectly through health service reorganisation and public health policy. Real-time data are needed to quantify direct and indirect effects. We aimed to monitor hospital activity for presentation, diagnosis and treatment of CVDs during the pandemic to inform on indirect effects.MethodsWe analysed aggregate data on presentations, diagnoses and treatments or procedures for selected CVDs (acute coronary syndromes, heart failure, stroke and transient ischaemic attack, venous thromboembolism, peripheral arterial disease and aortic aneurysm) in UK hospitals before and during the COVID-19 epidemic. We produced an online visualisation tool to enable near real-time monitoring of trends.FindingsNine hospitals across England and Scotland contributed hospital activity data from 28 Oct 2019 (pre-COVID-19) to 10 May 2020 (pre-easing of lockdown), and for the same weeks during 2018-2019. Across all hospitals, total admissions and emergency department (ED) attendances decreased after lockdown (23 March 2020) by 57.9% (57.1-58.6%) and 52.9% (52.2-53.5%) respectively compared with the previous year. Activity for cardiac, cerebrovascular and other vascular conditions started to decline 1-2 weeks before lockdown, and fell by 31-88% after lockdown, with the greatest reductions observed for coronary artery bypass grafts, carotid endarterectomy, aortic aneurysm repair and peripheral arterial disease procedures. Compared with before the first UK COVID-19 (31 January 2020), activity declined across diseases and specialties between the first case and lockdown (total ED attendances RR 0.94, 0.93-0.95; total hospital admissions RR 0.96, 0.95-0.97) and after lockdown (attendances RR 0.63, 0.62-0.64; admissions RR 0.59, 0.57-0.60). There was limited recovery towards usual levels of some activities from mid-April 2020.InterpretationSubstantial reductions in total and cardiovascular activities are likely to contribute to a major burden of indirect effects of the pandemic, suggesting they should be monitored and mitigated urgently.FundingBritish Heart Foundation, Health Data Research UK

Extracting patient phenotypes from routinely collected health data (such as Electronic Health Records) requires translating clinically-sound phenotype definitions into queries/computations executable on the underlying data sources by clinical researchers. This requires significant knowledge and skills to deal with heterogeneous and often imperfect data. Translations are time-consuming, error-prone and, most importantly, hard to share and reproduce across different settings. This paper proposes a knowledge driven framework that (1) decouples the specification of phenotype semantics from underlying data sources; (2) can automatically populate and conduct phenotype computations on heterogeneous data spaces. We report preliminary results of deploying this framework on five Scottish health datasets.

Introduction There are multiple potential sources of information about medications taken by participants in large population and patient cohorts such as the UK Biobank (UKBB) study, including from self-reported information and from linkage to electronic health records. At the time of writing, only self-reported medication data from UKBB participants have been released, in the form of medication codes that are assigned at the time of assessment centre interviews. Prescribing data is due to be released by UKBB in the near future. UKBB self-reported medication codes and descriptions do not have any published structure, which means that grouping medications into broader categories to contribute to clinical phenotyping is not possible. The motivation for the software project described here was to develop an automated means of classifying UKBB self-reported medications, for clinical phenotyping purposes.Methods We describe software tools, developed to match UKBB medication descriptions with terms from drug classification systems. The WHO’s Anatomical Therapeutic Chemical (ATC) Classification System and the British National Formulary (BNF) were selected as classification systems and were matched separately against UKBB medication coded descriptions. Manual matches can be added, for the cases where matching either fails or results in ambiguity. Manual matched codes would need to be added from the target classification system.Results Of the 3,646 medications reported as having been used by UKBB participants, 2,935 (80.5%) were matched with ATC system codes and 3,338 (91.6%) were matched with BNF codes. In general, medications remaining unmatched after manually matching those considered important according to clinician opinion, were either over the counter medicines with general descriptions or had very low participant report counts. A case study was conducted in which genetic associations between individual medications as phenotypes versus a Blood Pressure Genetic Risk Score were found less significant than those found once the medications had been automatically grouped. Conclusion Use of the matching software has been proven to assist in building medication-based phenotype proxies and to increase association significance over single UKBB medication codes. The matching software developed is available for general use and should also be applicable to other classification problems where descriptive text can be matched. https://github.com/PhilAppleby/ukbb-srmed/

With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.

OBJECTIVE To investigate the impact of type 2 diabetes on incidence of major dementia subtypes, Alzheimer and vascular dementia, using electronic medical records (EMR) in the GoDARTS bioresource. RESEARCH DESIGN AND METHODS GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland) comprises a large case-control study of type 2 diabetes with longitudinal follow-up in EMR. Dementia case subjects after recruitment were passively identified in the EMR, and using a combination of case note review, an Alzheimer-specific weighted genetic risk score (wGRS), and APOE4 genotype, we validated major dementia subtypes. We undertook a retrospective matched cohort study to determine the risk of type 2 diabetes status for incident dementia accounting for competing risk of death. RESULTS Type 2 diabetes status was associated with a significant risk of any dementia (cause-specific hazard ratio [csHR] 1.46, 95% CI 1.31–1.64), which was attenuated, but still significant, when competing risk of death was accounted for (subdistribution [sd]HR 1.26, 95% CI 1.13–1.41). The accuracy of EMR-defined cases of Alzheimer or vascular dementia was high—positive predictive value (PPV) 86.4% and PPV 72.8%, respectively—and wGRS significantly predicted Alzheimer dementia (HR 1.23, 95% CI 1.12–1.34) but not vascular dementia (HR 1.02, 95% CI 0.91–1.15). Conversely, type 2 diabetes was strongly associated with vascular dementia (csHR 2.47, 95% C 1.92–3.18) but not Alzheimer dementia, particularly after competing risk of death was accounted for (sdHR 1.02, 95% CI 0.87–1.18). CONCLUSIONS Our study indicates that type 2 diabetes is associated with an increased risk of vascular dementia but not with an increased risk of Alzheimer dementia and highlights the potential value of bioresources linked to EMR to study dementia.

Clinical images are needed to train algorithms however obtaining such images can present a range of challenges not necessarily obvious to those with a pure computational science background. This chapter provides a summary of the data governance constraints around access to patient records and what controls should be considered for secure access to anonymized health data.

Backgroundamp; Objectives: Legacy laboratory test codes make it difficult to use clinical datasets for meaningful translational research, where populations are followed for disease risk and outcomes over many years. The Health Informatics Centre (HIC) at the University of Dundee hosts continuous biochemistry data from the clinical laboratories in Tayside and Fife dating back as far as 1987. However, the HIC-managed biochemistry dataset is coupled with incoherent sample types and unstandardised legacy local test codes, which increases the complexity of using the dataset for reasonable population health outcomes. The objective of this study was to map the legacy local test codes to the Scottish 5-byte Version 2 Read Codes using biochemistry data extracted from the repository of the Scottish Care Information (SCI) Store.Data mapping methodology was used to map legacy local test codes from clinical biochemistry laboratories within Tayside and Fife to the Scottish 5-byte Version 2 Read Codes.The methodology resulted in the mapping of 485 legacy laboratory test codes, spanning 25 years, to 124 Read Codes.The data mapping methodology not only facilitated the restructuring of the HIC-managed biochemistry dataset to support easier cohort identification and selection, but it also made it easier for the standardised local laboratory test codes, in the Scottish 5-byte Version 2 Read Codes, to be mapped to other health data standards such as Clinical Terms Version 3 (CTV3); LOINC; and SNOMED CT.

SNAPPI-DB, a high performance database of Structures, iNterfaces and Alignments of Protein-Protein Interactions, and its associated Java Application Programming Interface (API) is described. SNAPPI-DB contains structural data, down to the level of atom co-ordinates, for each structure in the Protein Data Bank (PDB) together with associated data including SCOP, CATH, Pfam, SWISSPROT, InterPro, GO terms, Protein Quaternary Structures (PQS) and secondary structure information. Domain-domain interactions are stored for multiple domain definitions and are classified by their Superfamily/Family pair and interaction interface. Each set of classified domain-domain interactions has an associated multiple structure alignment for each partner. The API facilitates data access via PDB entries, domains and domain-domain interactions. Rapid development, fast database access and the ability to perform advanced queries without the requirement for complex SQL statements are provided via an object oriented database and the Java Data Objects (JDO) API. SNAPPI-DB contains many features which are not available in other databases of structural protein-protein interactions. It has been applied in three studies on the properties of protein-protein interactions and is currently being employed to train a protein-protein interaction predictor and a functional residue predictor. The database, API and manual are available for download at: http://www.compbio.dundee.ac.uk/SNAPPI/downloads.jsp.

Clinical datasets are the most critical resources or assets in the repository of Electronic Health Records (EHRs) and their quality gains competitive advantages in translational research. Accurate, reliable, and consistent representation of clinical datasets are essential for answering key research questions. However, a major issue with carrying out research on routinely collected primary care datasets is that they are often not fit-for-purpose or research-ready. It often takes months (if not years) for researchers to clean and transform clinical datasets for meaningful translational research. Profiling clinical datasets provides a proactive approach to examining and understanding the content, context and structure of source system data. The objective of this study was to develop a profiling dashboard to monitor, measure, assess, and improve the quality of clinical datasets hosted and maintained by the Health Informatics Centre (HIC) at the University of Dundee. Preliminary results indicated that the dashboard affords the flexibility to perform objective assessment of data quality, in terms of accessibility, accuracy, appropriate amount of data, completeness, and consistency.

The analysis and prediction of protein-protein interaction sites from structural data are restricted by the limited availability of structural complexes that represent the complete protein-protein interaction space. The domain classification schemes CATH and SCOP are normally used independently in the analysis and prediction of protein domain-domain interactions. In this article, the effect of different domain classification schemes on the number and type of domain-domain interactions observed in structural data is systematically evaluated for the SCOP and CATH hierarchies. Although there is a large overlap in domain assignments between SCOP and CATH, 23.6% of CATH interfaces had no SCOP equivalent and 37.3% of SCOP interfaces had no CATH equivalent in a nonredundant set. Therefore, combining both classifications gives an increase of between 23.6 and 37.3% in domain-domain interfaces. It is suggested that if possible, both domain classification schemes should be used together, but if only one is selected, SCOP provides better coverage than CATH. Employing both SCOP and CATH reduces the false negative rate of predictive methods, which employ homology matching to structural data to predict protein-protein interaction by an estimated 6.5%.

In general proteins of similar sequence have a similarstructure. We investigate whether a similar correlationexists between sequence identity and domain-domaininteraction similarity. Understanding this relationship isimportant to the study of many aspects of protein-proteininteractions, for example, the prediction of interactioninformation based on homology to complexes observedin structural data. Here the relationship between sequenceidentity and interaction similarity is investigated with theinclusion of all interactions within a structure and withredundancy filtering based upon normalisation by thepairwise SCOP [1] family classification of the interactingdomains.

Administration of trials involving large numbers of participants, coordinating multi-centred sites or drug management can all result in major logistical challenges for any trial. The enormity of these challenges can be drastically reduced through the adoption of healthcare informatics. ECLS (Early Cancer detection test - Lung cancer Scotland) is a high impact study with the aim of detecting often-deadly lung cancer at an earlier and more treatable stage. This project, a collaboration between researchers at the Universities of Dundee, Glasgow and Nottingham, NHS Scotland, the Tayside Clinical Trials Unit (TCTU) and supported by the Health Informatics Services Centre (HIC), provides a prime example of how healthcare informatics can be successfully applied through the full life cycle of a trial: • A web based Patient Recruitment system is being utilised to invite ~50,000 potential participants to take part in this study. This provides the facility to track participant progress, generate online reports and record participant appointments. • A Letter App supports the generation of letters as required by the study (e.g. appointment/result letters). • The Tayside Randomisation System (TRuST) balances the randomisation allocation for this study based on a minimisation with stratification algorithm. TRuST can also be utilised for allocation clustering preventing potential contamination and drug management offering full accountability. • On completion of recruitment the study will employ the services of the HIC Data Linkage Service to follow up participants and link to their routinely collected electronic health records. • HIC Services further supports clinical trials through the creation of study websites and data collection tools.

Structural data as collated in the Protein Data Bank (PDB) have been widely applied in the study and prediction of protein-protein interactions. However, since the basic PDB Entries contain only the contents of the asymmetric unit rather than the biological unit, some key interactions may be missed by analysing only the PDB Entry. A total of 69,054 SCOP (Structural Classification of Proteins) domains were examined systematically to identify the number of additional novel interacting domain pairs and interfaces found by considering the biological unit as stored in the PQS (Protein Quaternary Structure) database. The PQS data adds 25,965 interacting domain pairs to those seen in the PDB Entries to give a total of 61,783 redundant interacting domain pairs. Redundancy filtering at the level of the SCOP family shows PQS to increase the number of novel interacting domain-family pairs by 302 (13.3%) from 2277, but only 16/302 (1.4%) of the interacting domain pairs have the two domains in different SCOP families. This suggests the biological units add little to the elucidation of novel biological interaction networks. However, when the orientation of the domain pairs is considered, the PQS data increases the number of novel domain-domain interfaces observed by 1455 (34.5%) to give 5677 non-redundant domain-domain interfaces. In all, 162/1455 novel domain-domain interfaces are between domains from different families, an increase of 8.9% over the PDB Entries. Overall, the PQS biological units provide a rich source of novel domain-domain interfaces that are not seen in the studied PDB Entries, and so PQS domain-domain interaction data should be exploited wherever possible in the analysis and prediction of protein-protein interactions.