4-Year PhD Programme Cardiovascular & Diabetes Project proposal

Transcription

1 4-Year PhD Programme Cardiovascular & Diabetes Project proposal Title: Development of risk prediction methods for cardiovascular diseases and diabetes in patients with musculoskeletal diseases and vasculitis Supervisor: Mar Pujades Rodriguez Key words: Biostatistics / Medical statistics, Risk prediction, Prognostic models, Epidemiology BACKGROUND The importance of both chronic inflammation(1) and long-term steroid therapy(2) in the development of cardiovascular disease (CVD) and diabetes is increasingly recognised. An association between rheumatoid arthritis and heart disease and diabetes is well documented(3) but the physiopathological mechanisms and the relative importance of traditional cardiovascular risk factors, chronic inflammation and medication used to treat this disease have not been fully elucidated. Evidence for an association between CVDs and other chronic inflammatory disorders such as polymyalgia rheumatica and giant cell arteritis (also known as large-vessel vasculitis) is weaker(4). These conditions are commonly treated with high doses of steroids for prolonged periods of time. The relative contribution of steroids (e.g. type, dose, administration route and duration), established cardiovascular and diabetes risk factors and the level of disease activity of the underlying condition on the risk of vascular and diabetes are poorly understood(5). Steroids can also lead to the gradual development of toxicity (e.g. diabetes(6), hypertension(7)), which might have a major impact on quality of life and on psychological wellbeing, particularly in the elderly population, resulting in high personal and economic burden(8). Several studies have reported poor performance of common tools used for prediction of vascular risk among people with rheumatoid arthritis(9, 10). Disentangling the impact of underlying chronic inflammatory diseases such as rheumatoid disease and large-vessel vasculitis, medication and known traditional risk factors on vascular and diabetes risk, and developing improved tools to estimate vascular and diabetes risk among people with these diseases in a real-world setting are needed to guide clinical practice and health policy.

2 The development of well calibrated risk prediction tools that use data routinely collected as part of clinical care are vital to enable their automatic implementation in electronic health record systems. This would lead to the improvement of data collected by clinicians (e.g. improving completeness and accuracy of the information required in the developed equations) and facilitate more effective provision of health care. This PhD studentship will provide an excellent opportunity to develop skills in the analysis of electronic health records applied to important clinical questions and in risk prediction research. The proposed research will use a range of linked data sources including electronic health record data from primary care, hospital and mortality data, as well as UK Biobank data. HYPOTHESES In patients with chronic inflammatory diseases such as rheumatoid arthritis and large-vessel vasculitis, predictors of common CVDs and diabetes that are routinely recorded in electronic health records can be identified and be used to develop risk prediction models that classify patients according to their risk of developing toxicity. Inclusion of time-dependent treatment data in the risk prediction models (e.g. dose and duration of medication) helps to improve model performance in patients with chronic inflammatory diseases. AIMS The aim of this research is to develop new tools that enable clinicians to estimate cardiovascular and diabetes risk among people with rheumatoid arthritis and largevessel vasculitis treated with steroids. OBJECTIVES 1. To study the relative importance of steroid toxicity: To characterise steroid toxicity profiles in patients with common chronic inflammatory diseases, overall and according to the primary pathology treated and to the age of the patient. 2. To develop risk prediction models for CVDs: To identify predictors of steroid toxicity and develop risk prediction models to recognise patients at increased risk of CVDs. 3. To develop risk prediction models for type 2 diabetes PLAN OF INVESTIGATION Study population This will be an observational cohort study of all available patients eligible for data linkage that have been diagnosed with rheumatoid arthritis and large-vessel vasculitis

3 in the period between 2nd Jan 1998 and 30th September 2015 in the Clinical Practice Research Datalink (CPRD). Patients with these diseases will be identified through diagnosis codes in CPRD, the Hospital Episode Statistics (HES) and the Office for National Statistics (ONS) mortality register. Patients with less than one year of up-to-standard (UTS) registration in CPRD, those who are under eighteen years of age at the date of the first recorded chronic inflammatory disease diagnosis code, those who were not eligible for data linkage and those who have less than 3 years of information in CPRD prior to the start of followup (to assess prior medication exposure) will be excluded. Furthermore, patients diagnosed with CVD or type 2 diabetes before or on the date of study entry (prevalent disease) will also be excluded in the corresponding study. Endpoints The primary endpoints investigated will be a composite of major adverse cardiac and vascular events (MACCE) and type 2 diabetes. Secondary endpoints will include composites of fatal and non-fatal cardiovascular endpoints that take into account findings from recent relevant studies(3). Study outcomes will be identified in CPRD, HES and ONS data using validated disease algorithms developed to conduct research using electronic health records. Predictors Potential predictors considered in this study will be: 1) established cardiovascular(11) and type 2 diabetes(12) risk factors: age, sex, index of multiple deprivation, body mass index, smoking, blood pressure, lipid levels; 2) underlying chronic inflammatory disease (type and duration) and steroid and non-steroid medication provided for its treatment [including non-steroidal anti-inflammatory drugs, disease modifying antirheumatic drugs (DMARDs, both conventional synthetic and biologic)]; 3) medication prescribed for prevention of endpoints (e.g. blood pressure and lipid lowering medication); 4) co-morbidities (e.g. dementia, cancer, renal disease, high blood pressure); 5) inflammatory and renal biomarkers (e.g. creatinine, c-reactive protein, erythrocyte sedimentation rate, cardiac MRI); 6) other medication (e.g. hormone replacement therapy, hormonal contraceptives). Statistical analyses Statistical analyses steps will include: 1) development of a risk prediction model using appropriate multivariable survival models to estimate associations of CVD or type 2 diabetes (depending on model assumptions. E.g. Cox proportional hazard model, flexible weighted cumulative medication dose models, which account for dose, duration and timing of treatment, will be explored). To minimise bias, the directed acyclic graph adjustment variable selection approach will be used and the following a priory predictors will be included:

4 age, sex, year of entry, use of steroids in the three years before study entry, concomitant time dependent use of chronic inflammatory disease medication (e.g. DMARDs and biologic drugs) and chronic inflammatory disease duration (included as time dependent covariates). Multiple imputation of baseline covariates with noninformative missing patterns (e.g. smoking status and BMI) will be implemented following the approach described by Carpenter and Kenward(13). GP practice correlation will be modelled using random effects (shared frailty models across practices). 2) Goodness of fit of alternative models will be compared using the Akaike s information criterion (AIC) to select the best-fitting model. 3) Internal model performance will be evaluated (with cross-validation to correct for optimism ) in terms of calibration and discrimination (Harrell s C-index). 4) Model performance will also be compared with established risk prediction tool currently implemented in clinical practice (e.g. QRISK and Framingham). The results will be recorded according to the research reporting guidelines for observational (STROBE guidelines(14)) and for prediction model studies (TRIPOD guidelines(15)), as appropriate. MINI PROJECT 1 Literature review of risk prediction tools of vascular diseases and type 2 diabetes in patients with musculoskeletal diseases and vasculitis Description The PhD student will do a review of the literature in order to identify studies reporting development and/or validation of risk prediction tools of vascular diseases and type 2 diabetes in patients with the chronic inflammatory diseases under study. The project will enable an understanding of the principles and steps required to conduct this type of study namely: its design (e.g. inclusion and exclusion criteria), documentation of the search terms used to conduct the literature search in each literature search engine used, documentation of included and excluded studies and the reasons for exclusion, the identification and selection of the information to be extracted (e.g. characteristics of the studies and of the models, input parameters, outcomes, modelling and performance indicators and values), the preparation of a data extraction template, the extraction of the information and the writing up of a short report reporting the methodology and results.

5 Training plan General Starting your Research Degree in Medicine Working effectively with your supervisor An introduction to effective research writing Office use (if required) Regular attendance to meetings of the LIDA journal club and to the e-health records research group Research related Follow UoL course on Literature searching for your research degree for Faculty of Medicine and Health researchers and/or Advance literature searching (if available) Follow UoL course on How to read a scientific article (if required) Follow UoL course on Referencing with EndNote Follow online course on Introduction to systematic review and meta-analysis ( Follow relevant elements of UoL study units: Advanced epidemiology techniques ( 5038M; EPIB5038M) or Professional spine (EPIB5030M; ) Read PRISMA guidelines, guidance from the Cochrane Collaboration and from the Centre for Reviews and Dissemination at York. Read TRIPOD guidelines This work will be conducted under the supervision of: Paul Baxter and Mar Pujades. MINI PROJECT 2 Preparation of the first analytical protocol and cohort construction Description With the help of his/her supervisor, the student will first identify and read key review articles and methodological papers conducted to date and study the accuracy and completeness of health record datasets. He/she will explore sources of bias frequently encountered when working with electronic health records and the methods that can be applied to minimise their risk and/or correct estimations. Second, he/she will prepare the first analytical protocol of the study to develop risk prediction tools of

6 CVDs, providing details of study design, definition of endpoints and covariates and statistical methods (primary, secondary and sensitivity analyses). Third, he/she will register the analytical protocol in the clinicaltrial.gov website. Fourth, he/she will ensure that all the necessary information is available (and liaise with the MRC Medical Bioinformatics team if supplementary variables need to be extracted). Finally, he/she will perform the data management for the preparation of the cohort. The project will develop the student s understanding of the principles of code list and algorithm construction (electronic phenotyping) using linked primary and hospital care data and the importance of internal validation. He/she will also gain knowledge of the data coding systems currently used (e.g. Read, ICD, OPCS) and practical experience in implementing electronic algorithms in the data and conducting data management using long data files (multiple rows of data per patient). In addition, he/she will gain knowledge of potential bias introduced when working with electronic health records and of the methods that can be applied to minimise them through appropriate study design and/or correction of estimations. Training plan General: Project managing your research degree Preparing for your transfer Writing for research students in the Science Continue regular attendance to meetings of the LIDA journal club (every 2 weeks) and the e-health records research group (weekly) Project specific: or YCHI5015M. The Legal, Ethical and Professional Considerations in Healthcare Data Research Data protection and research Understand the PICO approach that facilitates the formulation of researchable questions Statistical research software use (if required) Buddying with named experienced post-docs A suitable post-doc highly experienced in the same statistical package used by the PhD student will be identified. REFERENCES 1. Roifman I, Beck PL, Anderson TJ, Eisenberg MJ, Genest J. Chronic inflammatory diseases and cardiovascular risk: a systematic review. The Canadian journal of cardiology. 2011;27(2): Fardet L, Kassar A, Cabane J, Flahault A. Corticosteroid-induced adverse events in adults: frequency, screening and prevention. Drug safety. 2007;30(10):

7 3. Pujades-Rodriguez M, Duyx B, Thomas SL, Stogiannis D, Rahman A, Smeeth L, et al. Rheumatoid Arthritis and Incidence of Twelve Initial Presentations of Cardiovascular Disease: A Population Record-Linkage Cohort Study in England. PloS one. 2016;11(3):e Pujades-Rodriguez M, Duyx B, Thomas SL, Stogiannis D, Smeeth L, Hemingway H. Associations between polymyalgia rheumatica and giant cell arteritis and 12 cardiovascular diseases. Heart. 2016;102(5): Gabriel SE, Crowson CS. Risk factors for cardiovascular disease in rheumatoid arthritis. Current opinion in rheumatology. 2012;24(2): Movahedi M, Beauchamp ME, Abrahamowicz M, Ray DW, Michaud K, Pedro S, et al. Risk of Incident Diabetes Mellitus Associated With the Dosage and Duration of Oral Glucocorticoid Therapy in Patients With Rheumatoid Arthritis. Arthritis & rheumatology. 2016;68(5): van Everdingen AA, Jacobs JW, Siewertsz Van Reesema DR, Bijlsma JW. Low-dose prednisone therapy for patients with early active rheumatoid arthritis: clinical efficacy, disease-modifying properties, and side effects: a randomized, double-blind, placebo-controlled clinical trial. Annals of internal medicine. 2002;136(1): Manson SC, Brown RE, Cerulli A, Vidaurre CF. The cumulative burden of oral corticosteroid side effects and the economic implications of steroid use. Respiratory medicine. 2009;103(7): Arts EE, Popa C, Den Broeder AA, Semb AG, Toms T, Kitas GD, et al. Performance of four current risk algorithms in predicting cardiovascular events in patients with early rheumatoid arthritis. Annals of the rheumatic diseases. 2015;74(4): Arts EE, Popa CD, Den Broeder AA, Donders R, Sandoo A, Toms T, et al. Prediction of cardiovascular risk in rheumatoid arthritis: performance of original and adapted SCORE algorithms. Annals of the rheumatic diseases. 2016;75(4): Schnohr P, Jensen JS, Scharling H, Nordestgaard BG. Coronary heart disease risk factors ranked by importance for the individual and community. A 21 year follow-up of men and women from The Copenhagen City Heart Study. European heart journal. 2002;23(8): Chen L, Magliano DJ, Zimmet PZ. The worldwide epidemiology of type 2 diabetes mellitus-- present and future perspectives. Nature reviews Endocrinology. 2012;8(4): Carpenter J, Kenward M. Multiple imputation and its application. 1st ed. Chichester, West Sussex: John Wiley & Sons; von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Epidemiology. 2007;18(6): Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Bmj. 2015;350:g7594.