Title: Accounting for multimorbidity, competing risk and direct treatment disutility in risk prediction tools and model-based cost-effectiveness analysis for the primary prevention of cardiovascular disease and osteoporotic fracture
Funder: Chief Scientist Office, Scotland

Abstract: Background: Medicines for the primary prevention of disease are taken long-term or for life. Decisions to start such medicines are therefore very important to patients and the NHS. Clinical guidelines increasingly make risk-stratified recommendations, where treatment recommendations vary by baseline risk of the outcome to be prevented (for example, cardiovascular disease (CVD) or osteoporotic fracture). Guideline recommended treatment thresholds are usually informed by model-based cost-effectiveness analysis (CEA), and implementing risk-stratified recommendations requires clinicians to use risk prediction models. These models are therefore increasingly central to both guideline development and clinical decision making. However, existing models are potentially problematic in older people and those with life-limiting multimorbidity. Risk prediction models do not account for competing risks of death from other causes. This means that they are likely to overpredict CVD or fracture risk in people with multimorbidity, who will often die of other causes. Existing cost-effectiveness models do account for average competing risk but average population cost-effectiveness may not apply to people with multimorbidity in whom competing mortality risks are much higher, and additionally the relatively small lifetime QALY gains from primary prevention may be offset by unaccounted for disutility relating to treatment burden. Overall aim: To improve the evidence generated from risk prediction models and model-based CEA to inform decision-making when selecting primary prevention treatments for CVD and osteoporotic fracture in people with multimorbidity. Methods: The study has three elements. (i) Derivation of new models for predicting the risk of incident CVD and fracture using Clinical Practice Research Datalink (CPRD) data to model both the targeted outcomes (incident CVD and fracture) and unrelated death (the competing risk), with internal cross-validation in CPRD to examine discrimination and calibration. We will compare the new model with the most widely used existing CVD and fracture risk prediction models (QRisk2 and QFracture) neither of which accounts for competing risks. ii) Elicitation of Direct Treatment Disutilities (DTD) for the general population and for people treated with statins or bisphosphonates, using time-trade off and best-worst scaling, with design informed using systematic literature reviews and semi-structured interviews with 15 patients and clinicians. (iii) Adaptation of the model-based CEA for lipid modification and bisphosphates used by NICE to quantify the impact of accounting for competing risks and/or DTD on relative cost-effectiveness and effectiveness measured by lifetime QALY gain. Expected outputs and impact: Better risk prediction in people with multimorbidity by accounting for competing risks will improve the targeting of preventive medicines to those who are most likely to benefit, and reduce low benefit and potential harmful medicines use. Accounting for variation in effectiveness and cost-effectiveness by appropriate subgroup analysis in model-based CEA to account for competing risks and DTD will ensure that guideline recommendations for preventive medicines are more appropriately nuanced to the important population of people with multimorbidity. We will ensure that the findings are appropriately disseminated to the full range of interested clinical, guideline developer, public and academic audiences.

Lay abstract: Background: This study will develop better methods for making recommendations for when people should take long-term drugs to prevent future heart disease/stroke or fracture due to osteoporosis (bone thinning). These recommendations are informed by two different types of mathematical model that are used to help predict what will happen in the future. Guideline developers use economic models to identify groups of patients who are likely to benefit from treatment. Doctors use risk-prediction models to identify who should be offered treatment. For example, based on an economic model, the NICE guideline on high cholesterol recommends only treating people with more than a 1 in 10 chance of heart attack/stroke in the next 10 years. Doctors work out individual patient's future risk of a heart attack/stroke by entering information about them into a risk-prediction calculator and only offer statin treatment to patients at high risk. The design of these models therefore directly affects the treatments which doctors recommend to patients. These treatments are taken long-term, often for the rest of someone’s life. The accuracy of these two types of mathematical model is therefore important because large numbers of people are potentially affected by decisions based on them. However, risk prediction tools and economic models may not be accurate for people with multiple health conditions (multimorbidity), because this group may die from these other conditions before they can develop heart disease or stroke or have a fracture. Economic models also do not account for the burden of taking a medicine for many years, which really matters to some patients and which may outweigh the relatively small benefit of preventive drugs. Overall aim: To improve the evidence generated from risk prediction and economic models to better inform decision-making for selecting medicines to prevent heart disease/stroke and fracture in people with multimorbidity. Design and methods: Study 1 will use information from general practice records to develop two new tools to predict the risk of developing heart disease/stroke and fracture in people with multimorbidity who do not have either condition. We will carry out initial checks of the accuracy of these tools, and compare them to risk prediction tools already being used in the NHS. Study 2 will use a specially designed survey to find out the effect on quality of life from taking different long-term medicines. Some people find taking medicines a significant burden but this is not taken into account in economic models at the moment. Study 3 will use the results from the first two studies to examine the expected benefit and value for money of statins to prevent heart disease/stroke and bisphosphonates to prevent fracture in people with multimorbidity and/or limited life expectancy. Public involvement: Interviews with patients and discussion with an NHS public partners group informed study design. Two public partners have agreed to join the advisory group, have commented on the proposal, and will contribute to the study 2 survey design and the interpretation of the findings. Dissemination: We will use a range of methods to ensure that all interested people (eg academics, clinicians, guideline developers, the public) get to know about the results, including the final report to NIHR, academic papers, clinical and public summaries, and the offer of workshops to key groups such as NICE.

Research activity codes: 7.1 INDIVIDUAL CARE NEEDS;7.3 MANAGEMENT AND DECISION MAKING
Health categories: CARDIOVASCULAR;MUSCULOSKELETAL;STROKE