Project 2: Evaluating the impact of cholesterol measures on risk of subsequent events in patients with established coronary heart disease

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1 Project 1: Missing data in longitudinal health records Clinical and administrative health databases have long been recognised as rich data sources in health research. Many of these databases have routinely collected data on major health indicators such as smoking status, blood pressure, cholesterol, weight and height, but there are still substantial issues surrounding missing data. Various approaches have been used to deal with missing data in clinical databases including complete case analysis, excluding variables with incomplete data or creating a missing data category. However, the issue of bias using these methods is well recognised. Therefore, multiple imputation emerges as a potential alternative to deal with missing data in large clinical databases. Yet the standard methods for multiple imputation do not account for the longitudinal and dynamic structures of large clinical databases. In this project the student will have an opportunity to further develop and evaluate multiple imputation methods to deal with missing data on a longitudinal scale in a large primary care database. In particular the project will focus on how longitudinal multiple imputation methods are able to deal with data that may be correlated over time such as weight, cholesterol and blood pressures measurements. This PhD project will be suitable for candidates with a background in statistics/mathematics and MSc in Medical Statistics or Epidemiology and an interest in working with electronic health records to address real life clinical questions. Supervisors: Dr Irene Petersen and Professor James Carpenter If you wish to be considered for this project please contact Dr Irene Petersen (i.petersen@ucl.ac.uk) Project 2: Evaluating the impact of cholesterol measures on risk of subsequent events in patients with established coronary heart disease With improvements in medical therapy, an increasing number of people are surviving initial heart attacks and living with established coronary heart disease (CHD). These patients however have very high rates of subsequent long term morbidity and mortality, yet the epidemiology of subsequent event risk is poorly studied in comparison to that of first CHD event risk. A greater understanding of determinants of subsequent event risk, will better inform guidelines and novel drug development for the secondary prevention of CHD. In this PhD project the student will work with epidemiologists and cardiologists to develop and implement strategies using electronic health records to explore the association between lipid markers, namely triglyceride and HDL levels, with risk of incident events in those patients who have previously been diagnosed with CHD, compared to those without baseline CHD. The population of interest will be derived from primary care, through the clinical practice research data-link (CPRD, n=10m) and the student will use electronic linkage to identify outcomes using national disease registries (MINAP), hospital episode statistics (HES) and national mortality data (ONS). This work will deliver the most comprehensive observational data on the association of lipid markers and subsequent event risk, which will in turn inform guidelines and trials.

2 An additional aim will be to perform similar analyses in the UK Biobank, for the same clinically measured lipid markers as well as novel and exploratory lipid markers such as ApoA & B. For interested candidates, there will also be opportunities to engage in parallel genetic association studies examining the impact of polymorphisms in lipid genes and pathways on subsequent event risk. The overarching theme of this and related work being developed in the department is to comprehensively assess the determinants of subsequent event risk in patients with CHD. The student will be expected to have an interest in cardiovascular disease and outcomes research along with a strong statistical and epidemiological background. Supervisors: Professor Harry Hemingway and Dr Riyaz Patel If you wish to be considered for this project please contact Professor Harry Hemingway (h.hemingway@ucl.ac.uk) Project 3: Linking disparate patient data sources via the automated application of semantic markup Key to gaining any insight into the possible treatment of patients or progress of a disease using informatics techniques is the availability of good data. The data on a single individual may emanate from many sources: systems run by a PCT, CVD outcomes database and so on. Where data is held in multiple systems, especially where those systems are run by different NHS trusts, mapping data between the systems may not be straightforward. Different terminology may be used by different systems, patients will likely be given different identifiers, and often important information is stored as free text inputted by clinical practitioners. All of these problems present hurdles when using data for research purposes. A key challenge therefore, before any informatics analysis techniques can be applied, is to link the data coming from these disparate sources. If this process were attempted manually it would be extremely time consuming and, as the number of patients involved increased, become intractable. The process is ripe for automation therefore, through the application of ontologies and tools designed with usability in mind, which require the minimal input from a user. Such tools can also be coupled with machine learning techniques to automatically classify data. Semantically annotating clinical data through the use of ontologies gives it meaning that can be used to improve the power of the analytics techniques applied to it. Applying machine learning classification techniques can help automate the process of ontological markup, reducing the need for human intervention. The ampoule-pi system developed in the UCL Centre for Computational Science is able to store data emanating from multiple clinical sources within a single data warehouse aimed at clinicians and researchers. This PhD research project will build on the ampoule-pi software to improve the linkage of data before it is committed to the warehouse. Supervisor: Professor Peter Coveney If you wish to be considered for this project please contact Professor Peter Coveney (p.coveney@ucl.ac.uk)

3 Project 4: Resolving uncertainties in the management of high blood pressure: assimilating evidence from observational studies, trials and electronic health record data. Data from large-scale observational studies, randomized trials and health surveys collectively indicate that high blood pressure (BP) is the major modifiable cause of death worldwide responsible for 7.6 million (13.5%) of deaths per year. Many people also live with the consequences of high BP including coronary disease, heart failure, stroke and chronic kidney disease. Despite the scale of prior research, there are numerous uncertainties in BP management and emerging inconsistencies in international guidelines. These encompass: 1) Optimal evaluation of BP. Technique: Controversy exists over whether clinic, home, or ambulatory BP measurement (ABPM) should be the gold standard. NICE guidance recommends ABPM, but the evidence base on the association of ABPM with cardiovascular (CVD) events is an order of magnitude smaller than for clinic BP, and the NICE recommendation was based in part on a systematic review of studies examining the extent to which home and clinic BP were able to predict ABPM, an analysis that implicitly sets ABPM as the standard. Frequency of measurement: A second issue is whether BP variability provides incremental information on the risk of future CVD, or CVD subtypes e.g. stroke, independent of mean BP. If it did, it would suggest that the frequency of BP measurement should become an important aspect of clinical care. However, some of the evidence on this matter comes from observational and trial datasets where group SD is used as a proxy for individual BP variability, an assumption whose theoretical basis has not been fully scrutinized or tested empirically. 2) Who to treat with blood pressure lowering drugs. Most international guidelines recommend the use of BP cut-points for the initiation of drug therapy, but the association of BP with CVD events is continuous and log-linear with no evidence of a threshold. Moreover, BP is one of a number of CV risk factors, the most influential being age. There is therefore uncertainty on whether initiation of BP-lowering treatment is optimally based on a BP cut-point, a CVD risk threshold or an age threshold, and the costs and consequences of the various options have not been adequately modeled. 3) The choice and combination of BP-lowering drugs NICE guidance on the choice of BP-lowering drug is based on the premise that certain drugs are more effective in lowering BP in the young than the old, that certain drug classes may be more or less effective in the prevention of stroke or CKD, for the same degree of BP-reduction, and that certain drug classes have a clinically important adverse effect on glucose regulation and diabetes risk. However, evidence from large-scale, systematic reviews of treatment trials provides only partial support for these recommendations and some of the evidence appears at odds with the current guidance. 4) The use of BP treatment targets Many guidelines propose drug treatment should be titrated to a target BP. However, the cost effectiveness of this approach has not been adequately compared with the alternatives of combining BP drugs with other interventions to achieve a CVD risk rather than BP target, or to a treat and forget approach.

4 Work on this project will and assimilate and integrate evidence on the four main areas of uncertainty. This will be through exploration of the statistical assumptions underpinning evidence on BP-variability as an independent CVD risk factor; through systematic reviews and (network) metaanalysis of diagnostic studies and trials (as appropriate), to determine the cost-effectiveness of different methods of BP measurement and different choice and combinations of BP-lowering drug classes (independent of BP-lowering). Critically, new analyses relevant to these areas will be undertaken where feasible, in linked primary care, hospital episode and disease registry data in CALIBER. A particularly important attribute of CALIBER is the availability of repeated BP measurements in the primary care record, information on BP-lowering treatments, and detailed information on CVD events. Supervisors: Professor Aroon Hingorani and Dr Reecha Sofat If you wish to be considered for this project please contact Professor Aroon Hingorani (a.hingorani@ucl.ac.uk) Project 5: Development of TranSMART informatics tools for innovation in a clinical and genomic data safe haven The Barts-UCLP-bridge is a pioneering clinical-academic datamart and data safe haven, enabling access to pseudoanonymised Barts Health (BH) cardiovascular clinical data. The datamart, which currently integrates clinical data on over 30,000 cardiovascular patients, was developed by Farr Institute staff at Barts NIHR cardiovascular biomedical research unit (CVBRU)to enable research in one of the largest cardiovascular centres in Europe. Building on the Barts-UCLP-Bridge infrastructure, the student will develop new analytical and data mart tools, using the open source TranSMART system ( towards the following key objectives. Enhancing the impact of Electronic Health Records: A TranSMART datamart will be developed to integrate primary and secondary-care data to better map cardiovascular disease trajectories in response to different therapeutic and device interventions. As a focus of clinical data mining and analysis it will also be used to feedback data to BH to improve patient clinical records and inform on clinical practice. Working in partnership with the BH-IT team, the student will also work directly with the BH Cerner IT infrastructure to improve data capture in BH source systems, thus improving NHS research and clinical data capture and ultimately clinical outcomes. New tools for genomic medicine: with a focus on cardiovascular disease in the Barts Health community, the student will develop innovative new TranSMART functionality to drive the use of genomic data in the NHS. The project will develop tools to allow better integration of clinically actionable genome variation data with clinical data to better understand common and rare forms of cardiovascular disease and cardiovascular drug response in East London populations. This will build on several world class genome sequencing initiatives on East London populations at Barts, funded by NIHR Genomics England, Wellcome Trust and the Barts charity. Supervisors: Dr Michael R. Barnes and Professor Adam Timmis If you wish to be considered for this project please contact Dr Michael R. Barnes (m.r.barnes@qmul.ac.uk)

5 Project 6: Cumulative missed opportunities for care after acute myocardial infarction: a linked national cardiovascular registries cohort study to identify preventable deaths There is global recognition of the need for comparative effectiveness and prognosis research to improve the quality and outcomes of cardiovascular (CV) care. There is also an increasing obligation to use cost-effective research methodologies to generate early healthcare impact by closing the second gap in translation. Our pilot analyses suggests >40% of acute myocardial infarction (AMI) patients are deemed not eligible for ST elevated myocardial infarction (STEMI) care components, including secondary prevention medications and emergency reperfusion therapies. Of those who are eligible for care, over half do not receive one or more care components (missed opportunities for care) which is significantly associated with increased risk of early and late mortality. At UCL, the National Institute of Cardiac Outcomes Research (NICOR) holds national CV audit and registry datasets (for example, datasets in acute coronary syndromes, adult cardiac surgery, heart failure and coronary intervention) with detailed outcome data. The PhD objective is to use linked NICOR datasets to investigate the association between AMI, cumulative missed opportunities for care (CMOC) and major cardiovascular and cerebrovascular events (MACCE). The PhD has 3 stages: Year1: Linked data cleaning, coding, linked data courses, manuscript 1: cohort profile paper Year2: Quantification of missed opportunities and attributable effects, handling of missing data, manuscript 2: CMOC and MACCE Year3: Quantification of missed opportunities and attributable effects, write-up, post-doctoral grant application We seek a PhD student who would relish the challenge of linkage and analysis of multiple national datasets, together with concomitant statistical challenges including imperfect capture and linkage, missing data and causal inference. This work is internationally competitive and has the potential to significantly contribute to future healthcare policy. The successful applicant will have access to a range of resources, clinical involvement/ leadership, statistical expertise and exposure to other work-streams. The student is expected to be fully integrated into the Farr PhD environment. Supervisors: Professor John E. Deanfield and Professor Harry Hemingway If you wish to be considered for this project please contact Professor John E. Deanfield (j.deanfield@ucl.ac.uk) Project 7: Diabetes and cardiovascular disease: National data on risk determinants, treatment effects and long term outcome The worldwide incidence of diabetes is increasing rapidly. It is estimated that 52million Europeans aged years had diabetes in 2011 and this will increase to >64million by Diabetes is a

6 known risk factor for cardiovascular (CV) disease but the mechanisms by which diabetes affects cardiac and vascular structure and function remain unclear. At UCL, the National Institute of Cardiac Outcomes Research (NICOR) holds national CV audit/registry datasets (for example, datasets in acute coronary syndromes, adult cardiac surgery, heart failure and coronary intervention) with detailed outcome data. We plan to link these datasets with the National Diabetes Audit database, primary care records (CPRD), Hospital Episode Statistics and mortality to create a unique research platform with 4 workstreams: Development of diabetes and CV risk in the population Development of CV events in patients with established diabetes Impact of diabetes on short-term outcomes after CV treatments/events Impact of diabetes on long-term outcomes and secondary prevention. The student will be expected (with appropriate statistical and clinical guidance) to identify and develop areas of her/his own interest within these themes. Potential research projects include, but are not limited to: The determinants of cardiovascular risk within the diabetic population, including identification of high and low risk groups for development of CV disease. The impact of diabetes on acute outcomes from cardiovascular events and revascularisation procedures in contemporary practice The impact of diabetes on long term outcomes as affected by revascularisation strategies and secondary prevention treatment. We seek an exceptional PhD student with the interest and capacity to contribute to this innovative field. The successful applicant will have access to a range of resources, considerable clinical involvement/leadership, statistical expertise and exposure to other work-streams. The student is expected to be fully integrated into the Farr PhD environment. Supervisors: Professor John E. Deanfield and Professor Adam Timmis If you wish to be considered for this project please contact Professor John E. Deanfield (j.deanfield@ucl.ac.uk) Project 8: Chronic inflammatory disorders and cardiovascular disease The importance of inflammation in cardiovascular disease is increasingly recognised. However, translating this knowledge into improved health has proved challenging because of a lack of detailed knowledge from humans in real world clinical settings. An association between a small number of chronic inflammatory diseases, such as rheumatoid arthritis, and coronary heart disease has been shown. However, for many other chronic inflammatory disorders the evidence base is incomplete. In addition, the extent to which chronic inflammation influences the risk of other vascular outcomes, such as stroke and venous thrombosis, is uncertain.

7 The exact focus of the PhD will be developed in collaboration with the successful student and will be shaped by their interests. One possible area is disentangling the impact of the underlying inflammatory disorder and drugs used to treat the disorder on vascular risk. Another is developing improved tools to estimate vascular risk among people with chronic inflammatory disorders. The PhD will provide an excellent opportunity to develop skills in the analysis of electronic health records applied to important clinical questions. This project will use a range of possible data sources including electronic health record data from the Clinical Practice Research Datalink linked to the Myocardial Ischaemia National Audit Project (MINAP), Hospital Episode Statistics (HES) and mortality data from the Office for National Statistics (ONS) mortality data. Supervisors: Professor Liam Smeeth and Dr Mar Pujades If you wish to be considered for this project please contact Professor Liam Smeeth (liam.smeeth@lshtm.ac.uk) Project 9: Do ambient temperature and outdoor air pollution affect the risk of stroke? There is considerable interest in the effects of environmental factors, particularly air temperature and pollution, on human health outcomes. Several large studies have shown that high pollution and extremes of temperature are associated with higher overall and cardiovascular mortality, but the specific drivers of these excess deaths, and thus the optimal strategies for prevention/mitigation, are unclear. There has been little investigation of the effects of such environmental exposures on risk of stroke, a major driver of mortality and morbidity in the UK and elsewhere. The proposed PhD would capitalise on the unprecedented new research opportunities arising from the availability of national stroke audit data. As part of the project, around 150,000 stroke records would be linked to environmental data from the UK s rich network of weather and pollution monitors. After first systematically reviewing and summarising the existing evidence, the student will be trained in time series and case-crossover methods, with a view to analysing these linked data to investigate whether temperature and key pollutant levels are related to subsequent acute stroke risk. This will include investigating possible delayed effects, non-linear effects, and synergism between weather and pollution effects. The findings will have implications for stroke prevention strategies, health care service organisation, public policy on climate and pollution, climate change impact assessments, and will further our understanding of cerebrovascular disease aetiology. Supervisors: Dr Krishnan Bhaskaran and Professor Liam Smeeth If you wish to be considered for this project please contact Dr Krishnan Bhaskaran (Krishnan.bhaskaran@lshtm.ac.uk) Project 10: Measuring the Effectiveness of Antihypertensives in Routine Clinical Care Clinical treatment guidelines are often developed based on evidence obtained from randomised clinical trials but efficacy findings in trials do not always translate to routine clinical care. Recent

8 changes in medicines regulations place increasing importance on measuring the real world effectiveness of drugs to inform better decision making about treatment risks and benefits and evidence of effectiveness has also been highlighted as a priority by the internationally recognised Observational Medical Outcomes Partnership (OMOP) initiative. Guidelines for pharmacological treatment of hypertension suggest either an ACE inhibitor (ACEi) or angiotensin receptor blocker (ARB) for first line therapy, largely due to the findings of trials, with further treatment intensification applied as required in a four step programme involving various combinations of drugs including ACEi, ARB, calcium channel blockers and thiazide-like diuretics. To date, there have been very few comparative effectiveness studies, with most limited to simply comparing ACEis with ARBs; moreover the programme steps involving combining several drugs have not been studied at all in trials or with observational data. This project will use routine primary care data from the Clinical Practice Research Datalink to determine the effects of treatment choices at each step in the four stage programme on blood pressure. Blood pressure is recorded opportunistically, depending on patients visiting their GP, having blood pressure measured, and the measure being recorded. Methodology will be developed to make the best use of these recordings. Other relevant clinical outcomes will also be studied, e.g. cardiovascular outcomes, new onset diabetes and mortality, providing the opportunity to work with linked data e.g. Myocardial Ischaemia National Audit Project (MINAP) and Office for National Statistics (ONS) mortality data. If linkage is available, data from the Stroke Improvement National Audit Programme (SINAP) will also be used. Supervisors: Dr Ian Douglas and Professor Liam Smeeth If you wish to be considered for this project please contact Dr Ian Douglas (ian.douglas@lshtm.ac.uk) Project 11: What are the health needs of children with Down syndrome? Over 650 children with Down syndrome are born each year in England, many of whom will have associated healthcare needs. Children with Down syndrome are known to be at higher risk of some illnesses than other children including heart anomalies, bowel abnormalities, digestive problems, hearing and vision impairments, thyroid dysfunctions, infections, cervical spine dislocation and blood disorders. This study aims to analyse data on all hospital stays in all children born with Down syndrome in England from birth until they are 11 years old. The data will also be linked to death and cancer registrations. This will provide important information to health care professionals and parents involved in the care of these children. This study will involve linking data from the National Down Syndrome Cytogenetic Register (NDSCR) with data from HES (Hospital Episode Statistics) and with data on Mortality and cancer. It is a unique project that will not only provide invaluable information on a national cohort of children with Down syndrome, but will also establish the methodology for linking these national data sets, will evaluate the accuracy of data in HES and will be able to extend the lessons to be applicable to other congenital anomalies. It will establish a cohort that will be available for further linkage with other national databases.

9 This studentship is an exciting opportunity for a graduate with a statistical background to gain experience in linking and analysing data from national databases. The applicant will be based in the Barts and the London School of Medicine and Dentistry School's Charterhouse Square Campus and will be part of a large team of analysts based at the Farr Institute. Supervisors: Professor Joan Morris and Professor Ruth Gilbert If you wish to be considered for this project please contact Professor Joan Morris (j.k.morris@qmul.ac.uk) Project 12: Changing patterns in the use of out-home-care for children in England England has relatively high rates of placement of children in out-of-home care. These rates are increasing for the youngest children. Reasons for placement include respite care for children with complex long-term health conditions, parent availability or capacity, for example due to the sudden death or serious illness of a parent. However, the majority of placements are for children at risk of harm due to maltreatment involving abuse and/or neglect. The evidence to-date on changing rates of out-of-home care is based on cross-sectional data collected by the Department for Education in England. No longitudinal analyses of children s entry in and out of care have been conducted on a national basis in England and there have been no detailed analyses of how trajectories of care vary according to reasons for placement. Information is lacking on whether the rise in earlier entry to care is associated with a reduced use of care in older children, fewer placement changes, or less childhood years spent in care. There is also a lack of information on outcomes, such as rates of reunification or adoption and how these vary between groups. Better understanding of the consequences of changing patterns in out of home care for children is important for policy. Placement in care is very expensive, of major public interest, and the experience can be both positive and negative for child and family health and wellbeing. Within England, information is lacking on how trajectories of care vary between local authorities and on how recent cuts in funding have affected use of care. Increasingly, there are also opportunities for comparing use of out of home care in England with other countries, where policies differ. The student will be part of an international working group developing methods for cross-country comparisons of out-of-home care. The studentship would be suitable for someone with strong quantitative skills and preferably a MSc in statistics, epidemiology, demography or equivalent experience. The student will analyse data for out-of-home care placements for children in the whole of England from 1992 onwards. S/he will gain experience of handling large datasets. S/he will undertake longitudinal analyses based on linked individuals over time, taking into account uncertainty and errors inherent in administrative data. This project is an exciting opportunity for a highly motivated individual to gain experience in analysing big data in an area of high public and policy interest. The student would join a large team of analysts and fellow students. S/he would be based at the Farr UCL Partners, a leading UK research institute in e-health and will work with a supervisory team led by Prof Ruth Gilbert, a paediatrician and clinical epidemiologist, Dr Arturo Gonzalez-Izquierdo, a statistician, Dr Mads Ubbesen (epidemiologist based in Denmark) and Prof June Thoburn, social scientist at UEA and

10 specialist in child protection. The student will also be expected to contribute to meetings of the Children s Policy Research Unit based at UCL Institute of Child Health. Supervisors: Professor Ruth Gilbert and Dr Arturo Gonzalez-Izquierdo If you wish to be considered for this project please contact Professor Ruth Gilbert (r.gilbert@ucl.ac.uk) Project 13: Respiratory syncytial virus in young children in England burden and risk factors for severe disease Respiratory syncytial virus (RSV) is a common cause of respiratory tract infection in babies and young children. Although usually mild, RSV can lead to bronchiolitis or pneumonia, which may require admission to hospital or an intensive care unit. An unknown proportion of infections are acquired in hospital. The burden of RSV in children in England is not well described, and the relative contribution of potential risk factors for severe disease such as co-infection with other viruses, specific underlying clinical conditions and deprivation are unclear. In this project, the student will use a national hospital database to link to national laboratory surveillance data to identify children with RSV admitted to hospital, determine the burden of RSV in children in England, and what proportion of infections is acquired during hospital admission. The student will use a large general practice database linked to hospital data to determine individual and family level risk factors for RSV associated hospital admissions. The student will also explore possibilities for using hospital prescription databases to map the use of monoclonal antibody (Palivizumab) for the prevention of severe RSV infection. The results will be used to inform interventions to reduce the impact of RSV in babies and children. This is an exciting opportunity for a person with some experience in epidemiology, statistics demography or similar to acquire specialist skills in analysing large, complex health databases and further develop their capacity for infectious disease surveillance and child health research. The project will be based jointly at the Centre for Paediatric Epidemiology and Biostatistics at the UCL Institute of Child Health, a leading department in the use of large administrative health databases in epidemiology, and Public Health England Centre for Infectious Disease Surveillance and Control (in Colindale, North London), the national agency responsible for infectious disease surveillance, prevention and control. Supervisors: Professor Ruth Gilbert, Dr Richard Pebody and Dr Pia Hardelid If you wish to be considered for this project please contact Professor Ruth Gilbert (r.gilbert@ucl.ac.uk) Project 14: Why are infant mortality rates in the UK so high? An inter-country comparison between England and Sweden England has some of the highest child mortality rates in western Europe. A large proportion of this is accounted for by high infant mortality rates (that is, deaths occurring in children aged less than one year old). Much media and policy debate is currently focussed on whether these differences are due

11 to the NHS failing to care for children. This project aims to determine the extent to which disparities in early child mortality (up to age 5 years) are explained by factors which could be health care amenable, and what proportion are due to socioeconomic or genetic factors.. The student will explore differences between England and Sweden in terms of the proportion of babies born with problems that increase their risk of dying, such as very low birth weight, prematurity, or serious long term conditions, stratified by socioeconomic indicators. The project will compare mortality rates in England and Sweden up to age 5 years. The student will analyse linked longitudinal data including birth and maternal details, death registration data and the whole hospitalisation history of children who die to determine the characteristics of children who die and the proportion with underlying long-term conditions. The student will analyse similar linked databases from the two countries to derive internationally comparable indicators of social and biological risk factors in mothers and children. The findings from the project will be used to inform policies to reduce child mortality in England relative to Sweden. The project will also develop an approach for cross-country comparisons of child outcomes, which is likely to be relevant to other countries. This project is an exciting opportunity for a highly motivated individual to gain experience in analysing big data in an area of high public and policy interest. The student would join a large team of analysts and fellow students at the UCL Institute of Child Health, part of the Farr UCL Partners, a leading UK research institute in e-health. The student will be supervised by Prof Gilbert, a paediatrician and clinical epidemiologist, Dr Hardelid, a statistician, and Prof Hjern, a paediatrician and epidemiologist at the Centre for Health Equity Studies at the Karolinska Institute in Stockholm. The studentship will include a limited number of visits to Sweden for mentoring and presentations. Supervisors: Professor Ruth Gilbert, Dr Pia Hardelid and Professor Anders Hjern If you wish to be considered for this project please contact Professor Ruth Gilbert (ruth.gilbert@ac.uk) Project 15: Using routine data linkage and national surveillance to understand the contemporary epidemiology and outcomes of infective endocarditis in UK children Infective endocarditis (IE) is a rare and life-threatening infection of the heart valves, affecting around children per year 1, and associated with significant morbidity and overall mortality of 10-30% 2. IE often occurs in children with congenital heart defects (CHDs), however intravascular catheters are increasingly recognised as an important predisposing factor for children with normal hearts. This project will use two sources of clinical data from children with IE: NICOR routine national endocarditis monitoring data collected since 2008, and a cohort of children diagnosed with IE during one year and prospectively ascertained through the British Paediatric Surveillance Unit (BPSU). Active national BPSU surveillance will identify IE in children with and without underlying CHD and will also provide rich clinical information that is unavailable from routine data, including Duke s diagnostic criteria, non-cardiac conditions and broad health outcomes at one year. Surveillance data will be linked to existing routine NICOR data, including cardiac audit for procedures and mortality, permitting in-depth evaluation of individual patient journeys. Voluntary surveillance schemes for laboratory-confirmed infections held by Public Health England provide a potential additional source

12 of surveillance data, which can be matched to BPSU cases to explore causative organisms and antibiotic resistance. The aims of this project are: 1. To improve understanding of the contemporary epidemiology of paediatric IE, including (i) changes in the relative importance of different predisposing conditions over time, (ii) the patient burden of subsequent cardiac surgical intervention, mortality and disability. 2. To evaluate the use of active surveillance, to obtain in-depth clinical diagnostic, management and outcome data for a national cohort, as a method to complement routine data collection and with a view to developing better reporting to routine monitoring and audit systems, such as NICOR. 1 Knirsch and Nadal. Eur J Pediatr 2011;170: NICE Clinical Guideline 64 Supervisors: Dr Rachel Knowles and Professor Carol Dezateux If you wish to be considered for this project please contact Dr Rachel Knowles (rachel.knowles@ucl.ac.uk) Project 16: Image analysis of temporal sequences of chest x-rays to improve the detection of TB In 2011, 8.7 million people fell ill with tuberculosis (TB), including 1.1 million with HIV andan estimated 1.4 million died from the disease. Although incidence is low in the UK, increasing mobility has led to a sharp rise and some London boroughs now have incidence rates of over 100 per 100,000. One of the primary tools in the diagnosis of TB is chest radiography. However there are difficulties with this technique: the interpretative skills it requires are often absent in low resource settings, and even when skilled radiologists are available, the images can be hard to interpret. We believe that these difficulties can be, at least in part, addressed with computer aided detection (CAD). We think CAD has several potential uses: supporting screening programmes where large numbers of images are taken from people in high risk populations with the aim of identifying TB early; supporting interpretation of radiographs taken to investigate suspected cases of TB; and supporting identification of TB from X rays taken for clinical purposes where a diagnosis of TB had not previously been suspected. We believe the sensitivity and specificity of CAD can be considerably enhanced by using temporal sequences of images. Such sequences are increasingly available for individuals who are screened more than once, or who have repeated X Rays to investigate clinical symptoms. Analysis of sequences of images allows temporal changes to be detected and used as cues in discriminating between disease and non-disease cases. Achieving this will require addressing a number of practical and theoretical difficulties. The key research challenges will be:

13 1) To create a research database of linked digital chest X-rays from different datasets, including two or more images of each patient and containing a large number of normal (n = 1000) cases and of TB cases (n = 200). This will involve linking existing datasets to which we already have access. 2) To apply existing approaches to image registration in order to highlight areas of abnormal change between successive images of the same patient. 3) To test the ability of CAD to identify previously unsuspected cases of TB by running the refined system against all digital chest X ray images held at UCLH and pseudonymously linking the UCLH radiographic database with national tuberculosis surveillance enabling identification of which patients went on to develop tuberculosis. Supervisors: Dr Paul Taylor and Dr Andrew Hayward If you wish to be considered for this project please contact Dr Paul Taylor (p.taylor@ucl.ac.uk) Project 17: Population based impact of GP antibiotic prescribing practices on E. coli bacteraemia in England Increasing antimicrobial resistance (AMR) in bacteria is a growing threat to modern medicine and public health. Almost 40% of all bloodstream infections (BSI) in England are currently caused by Enterobacteriaceae, with approximately 30,000 BSI related to Escherichia coli. Of particular concern is the significant increase in infections due to Extended Spectrum Beta Lactamase (ESBL)- and carbapenemase-producing E. coli over the last ten years as these antibiotics are the last-line drugs to treat those infections. Currently the information on the impact of antibiotic use on development of resistance and patient outcome is limited. We hypothesise that the E. coli bacteraemia incidence in England is associated with GP antibiotic prescribing practices, at an individual, practice or regional level. Inappropriate initial antibiotic treatment of an infection, such as urinary tract infection, may be causing the increase in E. coli bacteraemia reports. We propose to utilise the following datasets: Clinical Practice Research Database (CPRD) Hospital Episode Statistics (HES) Public Health England (PHE) voluntary laboratory surveillance database (LabBase2) PHE held mandatory E. coli surveillance (including enhanced surveillance) data We will link the HES and CPRD dataset to identify primary care prescribing, hospital admissions, length of hospital stay, co-morbidities and procedures. Antimicrobial susceptibility data in LabBase2 will be merged with the E. coli datasets. We will then link HES/CPRD database and the microbiology datasets using a previously developed algorithm (Blackburn et al. 2012). The linked dataset will allow us to record individual patient characteristics at different time points of the patient trajectory from primary to secondary/tertiary care. The student will develop a multi-level model to estimate the likelihood of an individual developing a BSI caused by an antibiotic resistant E. coli. This research will estimate the individual risk associated with prior antibiotic usage, adjusting for co-morbidities, and clustering due to infectious transmission within the community.

14 Supervisors: Dr Sarah Deeney and Dr Andrew Hayward If you wish to be considered for this project please contact Dr Sarah Deeney Project 18: Development of a clinical reporting pipeline for Hepatitis C drug resistance Treatment of hepatitis C (HCV) is currently the fastest growing pipeline in clinical medicine: there are an estimated 3-500,000 infected individuals in the UK alone. Treatment of HCV with antivirals selects for drug resistance which may lead to drug non-response. HCV full length gene sequencing is the next generation of viral diagnostics to guide therapy. We aim to create a tool which, given a query HCV sequence, will assist clinicians in selecting appropriate therapy. This will involve: Developing an automated pipeline to process next generation sequencing data for several thousand patient samples, generating consensus sequences, quantifying both majority and minority variants and identifying known drug resistance mutations. Building a database to store this information to facilitate downstream analysis. Using data-mining techniques to map this data to anonymised clinical data for each sample as part of collaboration with the emerging clinical databases on HCV. This should enable a deeper understanding of the relationship between drug-resistant minority variants and clinical outcomes. Creating a reporting algorithm which, given a query HCV sequence, is capable of extracting all relevant information from the database, short read and clinical datasets in real-time. Developing a web resource around this algorithm, with relevant information being presented in a user-friendly manner to clinicians in order to guide appropriate therapy. No such tools currently exist, and we expect this project to generate high impact and highly applicable outputs. This is an exciting opportunity to work within a multidisciplinary team of bioinformaticians, statisticians and epidemiologists based at the Farr Institute who will be linking the pipeline-derived sequencing data to key clinical data. Supervisors: Dr Andrew Hayward and Dr Paul Kellam If you wish to be considered for this project please contact Dr Andrew Hayward (a.hayward@ucl.ac.uk) Project 19: Infections as triggers for acute cardiovascular events Infections are implicated as triggers of acute cardiovascular events, for example we have demonstrated that respiratory tract infections and influenza are important triggers of acute myocardial infarction (AMI). This PhD project will use linked electronic health records to investigate the role of specific respiratory pathogens in the triggering of AMI and stroke. The successful PhD candidate will join a team of researchers at University College (London) the London School of Hygiene and Tropical Medicine, and Public Health England. S/he will further

15 develop methodology to link Labbase (a national dataset of laboratory confirmed infections identified in NHS laboratories, held by Public Health England) to Hospital Episode Statistics and the national Myocardial Infarction National Audit project. Analyses using the self-controlled case series method will enable investigation of specific infections as triggers for acute medical events helping to establish whether this is a generic infection related issue or whether it is specific to particular pathogens. The project will enable the candidate to develop strong skills in the use of electronic health data for infectious disease research. The work will provide important insights into the link between infection and diseases with a major population burden informing both public health interventions and more basic research into mechanisms. Supervisors: Dr Andrew Hayward and Professor Liam Smeeth If you wish to be considered for this project please contact Dr Andrew Hayward (a.hayward@ucl.ac.uk) Project 20: Genetic susceptibility to outdoor pollutants and the link with infections and cardiovascular death in people with chronic obstructive pulmonary disease Chronic obstructive pulmonary disease (COPD) is the sixth most common cause of death in the UK (~30,000 deaths per year). It affects approximately 900,000 people and costs the NHS 500m yearly. COPD patients are at risk of acute episodes of deterioration (exacerbations). Exacerbations are the commonest cause of medical hospital admission in the UK and are associated with increased mortality. Infections (bacterial and viral) are known to play a major role in exacerbations. COPD hospitalizations and mortality increase in periods of high air pollution and air pollutants at levels even within current guidelines may increase exacerbation risk. The prevalence of COPD is increased in those living close to traffic and COPD patients have substantial mortality risks associated with particles, with a lag time up to 4 years. Pollutants may trigger inflammation in the airways thus leading to an exacerbation, or may increase susceptibility to viral/bacterial infection, increasing severity and frequency of individual exacerbation episodes. Several genetic polymorphisms have been associated with increased inflammation/oxidative stress in response to pollutants; GSTT1, SOD2, GPX1, CAT, NQO1, HMOX1. Polymorphisms in oxidative stress genes (NQO1, GSTM1, GSTP1) modify respiratory symptoms, lung function, biomarkers, asthma risk and alter response to diesel exhaust particles. Inflammatory gene polymorphisms (TNF) influence lung function response to ozone, and development of asthma. There is also an established association between air pollution and cardiovascular outcomes. We hypothesise that some individuals with COPD are at increased risk of exacerbations and cardiovascular death due to genetic polymorphisms causing increased susceptibility to pollutants. We will investigate whether: 1) COPD patients with specific polymorphisms in oxidative stress and inflammatory genes are at increased risk of COPD exacerbations

16 2) These exacerbations are more severe (i.e. more likely to be associated with hospitalisation and mortality) 3) Mortality is more likely to be due to a cardiovascular cause Supervisors: Dr Jennifer Quint and Professor Liam Smeeth If you wish to be considered for this project please contact Dr Jennifer Quint (jennifer.quint@lshtm.ac.uk) Project 21: Reducing cardiovascular risk in people with COPD Chronic obstructive pulmonary disease (COPD) is the sixth most common cause of death in the UK. It affects approximately 900,000 people and costs the NHS 500m yearly. COPD patients are at risk of acute episodes of deterioration (exacerbations) which are associated with increased mortality. Up to 1/3 of people with COPD are thought to die of cardiovascular diseases and people with COPD are at increased risk of myocardial infarction (MI) and stroke compared to the general population. Exacerbations are intensified inflammatory events compared with stable COPD. It has been suggested that there is an increased period of risk of MI and stroke after an acute exacerbation and this may be particularly important in a subgroup of people with with frequent exacerbations. If frequent exacerbators are at increased risk of cardiovascular events even when stable, they may benefit from more aggressive prophylaxis or treatment. Once we have established the relationship between acute cardiovascular events and COPD, we will investigate methods to modify cardiovascular risk for example with the use of beta blockers, statins, aspirin and COPD inhaled medications. Original hypothesis We hypothesise that people with COPD have a higher incidence of MI and stroke compared to individuals without COPD and that people with frequent exacerbations of COPD are at increased risk of cardiovascular events compared to infrequent exacerbators. We will investigate whether: 1) People with COPD have a higher incidence of MI and stroke compared to individuals without COPD. 2) The incidence of cardiovascular events is greater around the time of an exacerbation 3) People with COPD who have frequent exacerbations are at increased risk of cardiovascular events compared to infrequent exacerbators. 4) The risk of cardiovascular events following COPD exacerbations and in frequent exacerbators is lower among people treated with beta blockers, statins, aspirin or various inhaled COPD medications. Supervisors: Dr Jennifer Quint and Professor Liam Smeeth If you wish to be considered for this project please contact Dr Jennifer Quint (jennifer.quint@lshtm.ac.uk)

17 Project 22: Infections and vaccinations in pregnancy There is a renewed interest in vaccinating mothers during pregnancy in order to help protect newborn babies against severe infections. For example, vaccination programmes against seasonal influenza and pertussis were recently introduced in the UK for pregnant women in their third trimester. This PhD project will use multiply linked electronic health records to investigate the burden of infections in pregnancy and the impact of vaccination on women and young children. The successful PhD candidate will join a team of researchers at the London School of Hygiene and Tropical Medicine, University College (London) and Public Health England. S/he will develop methods to analyse electronic health records to identify dates of conception and delivery. A range of study questions can then be explored. Detailed estimates of the burden of acute respiratory infections among pregnant women in each trimester of pregnancy can be determined. Further analyses and new linkages will allow assessment of the aetiology of these infections and exploration of their associations with adverse outcomes in early and late pregnancy, such as miscarriage and pre-term birth. Influenza vaccine uptake can be determined by maternal and general practice characteristics, and vaccine effectiveness estimated with careful exploration of potential biases. The project will enable the candidate to develop strong skills in the use of electronic health data for infectious disease research. The methods developed and the results of analyses will also provide useful data for cost-effectiveness analyses of maternal vaccinations, and will enable future screening for other diseases for which pregnant women are at higher risk and hence a potential target group for vaccination. Supervisors: Dr Sara Thomas and Dr Andrew Hayward If you wish to be considered for this project please contact Dr Sara Thomas (sara.thomas@lshtm.a.uk) Project 23: Use of linked clinical and genotypic databases to understand the genetic contributions to clinical outcomes in individuals with HIV A number of genome-wide association studies have addressed genetic correlates with HIV disease progression. The main driver for such studies has been to identify correlates of natural attenuation of infection, to inform vaccine development. However, little work has been undertaken in the context of combination antiretroviral treatment (cart) or related to specific risks for complications of HIV infection. There is evidence to suggest that suppression of viral replication by cart is insufficient to prevent premature acquisition of cardiovascular events, or hepato-renal pathology and the risk of non-aids cancers also appears to be raised. Further, there remains heterogeneity in the immunological response to cart. The UK Collaborative HIV Cohort (UK CHIC Study) is a longitudinal study of >45,000 HIV-positive individuals seen for care at many of the largest HIV clinics in the UK. Recently, we have received funding for host genome sequencing of 10,000 participants in this study through the UK BioResource, as well as capture of information on several non-aids clinical conditions. Using the information generated through linkage of the genotypic data that will be obtained, with extensive

18 phenotype data from UK CHIC (including longitudinal data on responses and outcomes of cart), this project will investigate the genetic contributions to several outcomes, including (but not limited to): 1. Discordant virological and immunological responses to cart; 2. Immune reconstitution syndrome; 3. Switches in antiretroviral therapy for toxicity reasons; 4. Renal disease progression and cart-associated nephrotoxicity; 5. Central nervous system diseases; 6. cart-associated hepatotoxicity; and 7. Long-term virological control in the absence of cart. Supervisors: Professor Deenan Pillay and Professor Caroline Sabin If you wish to be considered for this project please contact Professor Deenan Pillay Project 24: Combining ophthalmic imaging and clinical datasets to explore the impact of systemic conditions on retinal function The clinical significance of the relationship between signs detected on retinal imaging and our understanding of other diseases, such kidney or cardiovascular disease is an evolving area. Since microcirculation can be imaged directly in the retina, changes in microvasculature that convey important information about, for example, arterial hypertension or coronary heart disease can be quantified. We have built a collaborating network of 14 ophthalmic units who have pooled data from electronic medical records and associated retinal colour images and optical coherence tomography (OCT) scan to create a unique data set (80 to 100,000 patients) on eye disease. Discussions are underway to link this with two key datasets: NICOR and the UK Renal Registry Dataset. This will allow us to explore a variety of hypotheses about the link about, for example, kidney function and the progression of diabetic eye disease. In order to extend the potential of this work we want to create a library of validated algorithms for the accurate quantification of key signs of changes to the microvasculature, and macular oedema as identified on retinal and OCT images. This will involve the segmentation of retinal vessels, accurate measurement of vessel diameter and the measurement of vessel tortuosity and branching patterns as well as the segmentation of OCT scans. The development of efficient and accurate algorithms for the quantification of these features, coupled with a large database of eye disease linked to other key national resources will be a unique asset for the Farr Institute, and can be used to answer important clinical questions. Supervisors: Dr Paul Taylor and Mr Adnan Tufail If you wish to be considered for this project please contact Dr Paul Taylor (p.taylor@ucl.ac.uk) Project 25: The contribution of Patient and Public Engagement to improving health research outcomes Patient and Public Engagement (PPE) in health research has grown rapidly in the last decade to the point where major research funding bodies, including UK Research Councils, the Wellcome Trust and the National Institute of Health Research (NIHR) place strong emphasis on PPE. Despite this

19 increasing recognition, there is still a dearth of evidence regarding the impact of PPE activities on research outcomes. The Farr Institute at UCL Partners provides an opportunity to examine the evidence more closely. The Farr Institute encompasses several broad areas of electronic health research and provides great opportunities of scale. There are also specific issues relating to electronic health record linkage that provoke the interest of public and patients. The Farr Institute probably represents the first attempt to combine PPE and Citizen Science in a health setting at an institutional level. The overarching question for PPE in the Farr Institute is how can high quality patient and public engagement improve health (research) outcomes? Other questions involved in addressing this are: Why, how and when does patient and public engagement bring the greatest benefits? What type of involvement is likely to bring added value within any particular research project? The proposed PhD would address these questions in four clear stages: 1) Reviewing the evidence base that PPE improves research quality / outcomes in healthcare (Year 1) 2) Developing a model or hypothesis based on the findings from stage 1), of how PE might work i.e. achieve desired outcomes in the Farr Institute. (Year 2) 3) Mapping the range and nature of PPE activities against research outputs in Farr. (Year 2 / 3) 4) Investigating the impact of PE activities on research outcomes / testing the model or hypothesis constructed in stage 2) through an in-depth case study (Year 2 / 3) Supervisors: Professor Judith Stephenson and Professor Simon Lock If you wish to be considered for this project please contact Professor Judith Stephenson (j.stephenson@ucl.ac.uk)