Can electronic health records improve the efficiency of large clinical trials? Supervisors: Associate Prof M. Angelyn Bethel 1 Associate Prof Louise Bowman 2 Prof Simon Griffin 3 Departments: 1 Diabetes Trials Unit, OCDEM, Radcliffe Department of Medicine Clinical Trial Service Unit, Nuffield Department of Population Health 3 University of Cambridge Institute of Public Health 2 Background In diabetes, new antidiabetic agents must demonstrate both glucose lowering and cardiovascular safety before approval. Clinical outcomes mega-trials are required to provide robust assessment of the risks and benefits of treatment. A typical mega-trial is initiated outside routine health care in selected cohorts and requires an extensive and expensive trial machinery to provide multicentre scientific, ethical, and regulatory oversight. To limit costs, the trial are large but with short durations of follow-up, measure surrogate or limited outcomes, and often leave important long-term riskbenefit balance questions unanswered. For change to be possible, more efficient trial methodologies are necessary. Electronic health records (EHR) and routine data sources are proposed tools to increase efficiency and reduce trial cost. EHR advantages include providing long-term multidimensional follow-up of disparate outcomes (e.g. cancer, fracture) and, when process data can be obtained (e.g vital signs, laboratory values), obviating the need for face to face visits. However, the datasets are complex and may not reliably ascertain events due to inconsistencies in diagnostic coding and treatment practices across a health care system. Use of EHR to replace established patient follow-up practices must be validated to ensure scientific integrity. Hypothesis EHR can be used to reliably identify important 1) cardiovascular outcomes, 2) diabetes-related outcomes and 3) safety outcomes, thus allowing cost-effective methods of follow-up in long-term outcomes trials in diabetes. Aims Using data from completed large scale outcomes trials, including but not limited to the United Kingdom Prospective Diabetes Study (UKPDS), the UKPDS post trial monitoring study (UKPDS-PTM), and the Anglo-Danish-Dutch Study of Intensive Treatment In People with Screen Detected Diabetes in Primary Care in the UK (ADDITION-UK), event ascertainment from traditional clinical trial practice will be compared with that achievable from EHR for cardiovascular events, microvascular complications of diabetes, and other safety events. The design of these trials will allow comparison of EHR ascertainment with face to face visit and/or questionnaire ascertainment protocols. Description of Work 1. Obtain EHR dataset and prepare it for research use. The fellow will participate in a workshop to facilitate the significant upfront learning required to understand and use EHR data. Issues addressed are the complex coding of data items, data provided at a level which is not immediately amenable to analysis, missing data, duplicates, and costing episodes via Healthcare Resource Groups (HRGs). Workshop learnings will be applied to data across multiple clinical trials to prepare datasets for research use. 2. Evaluate EHR for event ascertainment. Identified EHR outcomes of interest will be compared to data collected from completed clinical trials. Validation in multiple datasets will allow comparisons of EHR to clinic visits and/or questionnaire follow up protocols. Events of interest are cardiovascular, diabetes, and safety outcomes. Benchmarks for comparing EHR to traditional trial methods are: Ascertainment. The number of events of each type identified. Concordance. The agreement between investigator-reported event types and EHR. We will also examine concordance with adjudicated events, where available Cost. We will compare cost of the EHR event ascertainment approach (including approvals and application fees, data processing and analysis) to standard trial practice (including site payments, monitoring requirement, and adjudication activities). 3. Evaluate the performance of EHR in real time. In event-driven trials, the time taken for event ascertainment affects trial duration. If EHR follow up is shown to robustly ascertain important patient outcomes, these methods will be applied in an ongoing outcomes trial to ensure that event data can be collected in a timely manner. We will compare time lags between event occurrence and traditional investigator reporting dates to those between event occurrence and detection in the EHR to determine if delays or efficiencies are introduced. As above, the cost of using EHR for real time event identification, which will require multiple data downloads (e.g. monthly), will be evaluated. Recent relevant publications 1. Holman RR, Paul SK, et al. (2008) 10-Year Follow-up of Intensive Glucose Control in Type 2 Diabetes. N Eng J Med 359: 1577-89. 2. Griffin SJ, Borch-Johnsen K, et al. (2011) Effect of early intensive multifactorial therapy on 5-year cardiovascular outcomes in individuals with type 2 diabetes detected by screening (ADDITIONEurope): a cluster-randomised trial. Lancet. 378: 156–67. 3. Bulbulia R, Bowman L, et al. (2011) Effects on 11 year mortality and morbidity of lowering LDL cholesterol with simvastatin for about 5 years in 20,536 high-risk individuals: a randomised controlled trial. Lancet. 378(9808): 2013-2020. 4. Aung T, Cowan H, et al. (2011) Recruiting patients cost-effectively by mail. Trials. 12(Suppl 1): A117.
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