ObjectivesTo estimate the cost savings and health benefits of improving detection of individuals at high risk of cardiovascular disease (CVD) in England, to determine to which patient subgroups these benefits arise, and to compare different strategies for subsequent management.DesignAn economic analysis using the School for Public Health Research CVD Prevention Model.SettingEngland 2018.ParticipantsAdults aged 16 and older with one or more high cardiovascular risk conditions, including hypertension, diabetes, non-diabetic hyperglycaemia, atrial fibrillation, chronic kidney disease and high cholesterol.InterventionsDetection of 100% of individuals with CVD high risk conditions compared with current levels of detection in England. Detected individuals are assumed to be managed either according to current levels of care or National Institute of Health and Care Excellence (NICE) guidelines.Main outcome measuresIncremental and cumulative costs, savings, quality adjusted life years (QALYs), CVD cases, and net monetary benefit, from a UK NHS and Personal Social Services perspective.Results£68 billion could be saved, 4.9 million QALYs gained and 3.4 million cases of CVD prevented over 25 years if all individuals in England with the six CVD high risk conditions were diagnosed and subsequently managed at current levels. Additionally, if all detected individuals were managed according to NICE guidelines, total savings would be £61 billion, 8.1 million QALYs would be gained and 5.2 million CVD cases prevented. Most benefits come from detection of high cholesterol in the short term and diabetes in the long term.ConclusionsSubstantial cost savings and health benefits would accrue if all individuals with conditions that increase CVD risk could be diagnosed, with detection of undiagnosed diabetes producing greatest benefits. Ensuring all conditions are managed according to NICE guidelines would further increase health benefits. Projected cost-savings could be invested in developing acceptable and cost-effective solutions for improving detection and management.