Title: Harnessing longitudinal measurement of predictor variables to enhance prediction models in medicine.
Funder: Department of Health and Social Care

Abstract: Research Question How can we harness longitudinal medical data to improve the predictive performance and clinical usefulness of risk prediction models in medicine?BackgroundThe majority of recently developed risk prediction models, including those that are already clinically-established, only use baseline patient information to predict the risk of a future health outcome of interest, but this approach fails to utilise longitudinal medical data available in both electronic health records and observational cohort studies. Harnessing longitudinal measurements of predictor variables provides an opportunity to improve the predictive accuracy, and therefore clinical usefulness, of risk prediction models by accounting for changes in risk factors over time. Consequently, inference can be drawn about how these changes influence an individual's risk of a future health outcome, whilst also adjusting for other risk factors. In addition, single baseline measurements of predictor variables are more sensitive to measurement error which could lead to unreliable risk predictions in clinical practice. Therefore, it seems both wasteful and unethical not to investigate the potential influence of longitudinal information on the predictive accuracy of clinical risk prediction models where the data is available and/or routinely collected.Despite methods for longitudinal data analysis being well established, the methodology for harnessing longitudinal data for clinical risk prediction and personalised healthcare is still under-researched, with existing approaches either too numerically complex or inefficient to be clinically useful. Recent reviews of existing methods available do, however, provide evidence that predictive accuracy can be improved by harnessing changes in predictor variables over time.Key Aims and Objectives To identify and critically appraise existing methods for harnessing longitudinal information in statistical modelling. To develop and evaluate a new statistical approach to incorporating longitudinal measurements of predictor variables for risk prediction models. To use new methodology to construct enhanced models that can predict serious infection in biologic therapy and cardiovascular disease (CVD) risk for rheumatoid arthritis (RA) patients. Project PlanThe programme of work has been divided into five work packages. Methodological systematic review. Application of existing methods to RA cohort data. Development and evaluation of new modelling approach. Application of new approach to update a risk prediction model. Write-up and dissemination. Anticipated Impact:This project has the potential to deliver the following outputs and impact within 5 years of completion: Methods available to develop more reliable prediction models by harnessing longitudinal medical data applicable to all disease areas. Enhanced risk prediction models that can predict individual risk of serious infections on biologic therapy and CVD for RA patients. Reduced patient anxiety around RA biologic treatment. Reduced number of serious infections in RA patients on biologic therapy. Better understanding of CVD risk and RA. Patients and the public more involved in methodological research projects. Raised awareness among patients and clinicians of the importance of prediction models in the move towards personalised healthcare. In the long term with the use of the methods and models developed, this project has the potential to reduce morbidity, reduce mortality and deliver cost-savings for the NHS.

Lay abstract: An important aspect of healthcare is not only to treat diseases as they occur but to identify the people at risk of developing them so that treatment can be provided early on. This might prevent the disease developing or at least minimise its severity. Risk calculations can also help doctors and patients decide which treatment is likely to be best for the patient, i.e. the one that will give them the best outcome with the least harmful side effects.To evaluate individual patient risk, doctors can put patient test results and information (also known as patient data) into something called a risk prediction model or risk calculator. This is an equation, which combines all relevant patient information and produces a percentage chance that something will happen. An example of a risk prediction model currently being used by NHS doctors is the QRISK2 model, which predicts a patient's chance of developing cardiovascular disease (CVD) in the next ten years. The QRISK2 model helps doctors and patients decide whether they should prescribe statins, a treatment which can reduce this risk and create a better outcome for the patient. Unfortunately, many other models like QRISK2 have been created for different diseases but are not actually being used to improve our health because doctors often don't trust that they produce accurate enough risk predictions.One of the drawbacks of risk models is that they are currently limited to using only the most recent test results and patient information to calculate risk when, in reality, doctors also usually have access to patient data from previous medical appointments. Presenting this problem to a group of patients and carers recently revealed that patients also believe, as well as researchers, that it is both wasteful and unethical to not make the most of all available patient information to assess their risk. Taking account of more information about a patient over a longer period of time could also make risk predictions more accurate as how certain factors change over time can affect risk.My research project aims to provide a solution to this problem by developing a new way in which risk prediction models can use patients' previous test results and information, as well as the most recent set to predict the chance of something happening in the future.To ensure that I reach this goal, I will redesign a model used to predict the chances of developing a serious infection when taking rheumatoid arthritis (RA) drugs, by including more patient information. The methods used for this redesign will be ones created by other researchers, as well as a new one I will develop during this project. I will then see how much better these new models predict the risk of serious infections than the model which uses only the most recent patient data. From there, I will apply what I've learned to improve CVD risk predictions for RA patients.During the project, I will also host bi-annual advisory meetings with patients and carers to keep them informed about the project and receive their feedback on the research plan. I would also involve patients in the circulation of any research outputs to researchers, doctors and the wider public.This will be via regular online contact and during additional advisory meetings, as necessary. Methods projects, like this one, can only improve health if the new methods and the potential benefit for patients are well communicated to researchers and trusted by doctors.The new methods and models developed during this project will be circulated through providing training workshops, creating computer software, journal publications, patient newsletters, and attendance at national and international conferences.

Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE