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Aims The aim of this study was to develop, validate, and illustrate an updated prediction model (SCORE2) to estimate 10-year fatal and non-fatal cardiovascular disease (CVD) risk in individuals without previous CVD or diabetes aged 40-69 years in Europe.Methods and results We derived risk prediction models using individual-participant data from 45 cohorts in 13 countries (677 684 individuals, 30 121 CVD events). We used sex-specific and competing risk-adjusted models, including age, smoking status, systolic blood pressure, and total- and HDL-cholesterol. We defined four risk regions in Europe according to country-specific CVD mortality, recalibrating models to each region using expected incidences and risk factor distributions. Region-specific incidence was estimated using CVD mortality and incidence data on 10 776 466 individuals. For external validation, we analysed data from 25 additional cohorts in 15 European countries (1 133 181 individuals, 43 492 CVD events). After applying the derived risk prediction models to external validation cohorts, C-indices ranged from 0.67 (0.65-0.68) to 0.81 (0.76-0.86). Predicted CVD risk varied several-fold across European regions. For example, the estimated 10-year CVD risk for a 50-year-old smoker, with a systolic blood pressure of 140 mmHg, total cholesterol of 5.5 mmol/L, and HDL-cholesterol of 1.3 mmol/L, ranged from 5.9% for men in low- risk countries to 14.0% for men in very high-risk countries, and from 4.2% for women in low-risk countries to 13.7% for women in very high-risk countries.Conclusion SCORE2-a new algorithm derived, calibrated, and validated to predict 10-year risk of first-onset CVD in European populations-enhances the identification of individuals at higher risk of developing CVD across Europe. Acknowledgements We thank investigators and participants of the several studies that contributed data to the Emerging Risk Factors Collaboration (ERFC). This research has been conducted using the UK Biobank Resource under Application Number 26865. Data from the Clinical Practice Research Datalink (CPRD) were obtained under licence from the UK Medicines and Healthcare products Regulatory Agency (protocol 162RMn2). CPRD uses data provided by patients and collected by the NHS as part of their care and support. We thank all EPIC participants and staff for their contribution to the study, the laboratory teams at the Medical Research Council Epidemiology Unit for sample management and Cambridge Genomic Services for genotyping, Sarah Spackman for data management and the team at the EPIC-CVD Coordinating Centre for study co-ordination and administration. Funding The ERFC co-ordinating centre was underpinned by programme grants from the British Heart Foundation (SP/09/002; RG/13/13/30194; RG/18/13/33946), BHF Centre of Research Excellence (RE/18/1/34212), the UK Medical Research Council (MR/L003120/1), and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC1215-20014), with project-specific support received from the UK NIHR [*], British United Provident Association UK Foundation and an unrestricted educational grant from GlaxoSmithKline. A variety of funding sources have supported recruitment, follow-up, and laboratory measurements in the studies contributing data to the ERFC, which are listed on the ERFC website (www.phpc.cam.ac.uk/ceu/erfc/list-of-studies). *The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome. The MORGAM Project has received funding from EU projects MORGAM (Biomed BMH4-CT98-3183), GenomEUtwin (FP5, QLG2-CT-2002-01254), ENGAGE (FP7, HEALTH-F4-2007-201413),CHANCES (FP7, HEALTH-F3-2010-242244), BiomarCaRE (FP7,HEALTH-F2-2011-278913), euCanSHare (Horizon 2020, No. 825903) and AFFECT-EU (Horizon 2020, No. 847770); and Medical Research Council, London (G0601463, No. 80983: Biomarkers in the MORGAM Populations). This has supported central coordination, workshops and part of the activities of the MORGAM Data Centre, the MORGAM Laboratories and the MORGAM Participating Centres EPIC-CVD was funded by the European Research Council (268834), and the European Commission Framework Programme 7 (HEALTH-F2-2012-279233). This work was supported by the Estonian Research Council grant PUTs (PRG687, PUT1660, PUT1665, PRG184), by European Union through the European Regional Development Fund project no. MOBERA5 (Norface Network project no 462.16.107), by the Green ICT programme under Norway Grants 2014 – 2021 (grant number EU53928), by the European Union through Horizon 2020 grant no. 810645 and through the European Regional Development Fund (Project No. 2014-2020.4.01.16-0125) and by the PRECISE4Q consortium. PRECISE4Q project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant agreement 777107. This work was partly funded through the CoMorMent project. CoMorMent has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant agreement 847776. The KORA study was initiated and financed by the Helmholtz Zentrum Mu¨nchen—German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. The KORA study was supported by a research grant from the Virtual Institute of Diabetes Research (Helmholtz Zentrum Mu¨nchen), the Clinical Cooperation Group Diabetes between Ludwig-Maximilians-Universita¨t Mu¨nchen and Helmholtz Zentrum Mu¨nchen, and by the German Diabetes Center (DDZ). The HAPIEE project, Institute, was supported by grants from the Wellcome Trust (064947/Z/01/Z; WT081081) and US National Institute on Aging (1R01 and AG23522). The co-ordination of EPIC is financially supported by International Agency for Research on Cancer (IARC) and also by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts are supported by: Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Ge´ne´rale de l’Education Nationale, Institut National de la Sante´ et de la Recherche Me´dicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), German Institute of Human Nutrition Potsdam Rehbruecke (DIfE), Federal Ministry of Education and Research (BMBF) (Germany); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy); Dutch 2448 SCORE2 working group and ESC Cardiovascular Risk Collaboration Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS)—Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucı´a, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology—ICO (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Ska˚ne and Va¨sterbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C8221/A29017 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk; MR/M012190/1 to EPIC-Oxford) (United Kingdom)

Background: Previous research has suggested that patients with peripheral artery disease (PAD) are not offered adequate risk factor modification, despite their high cardiovascular risk. The aim of this study was to assess the cardiovascular profiles of patients with PAD and quantify the survival benefits of target-based risk factor modification. Methods: The Vascular and Endovascular Research Network (VERN) prospectively collected cardiovascular profiles of patients with PAD from ten UK vascular centres (April to June 2018) to assess practice against UK and European goal-directed best medical therapy guidelines. Risk and benefits of risk factor control were estimated using the SMART-REACH model, a validated cardiovascular prediction tool for patients with PAD. Results: Some 440 patients (mean(s.d.) age 70(11) years, 24·8 per cent women) were included in the study. Mean(s.d.) cholesterol (4·3(1·2) mmol/l) and LDL-cholesterol (2·7(1·1) mmol/l) levels were above recommended targets; 319 patients (72·5 per cent) were hypertensive and 343 (78·0 per cent) were active smokers. Only 11·1 per cent of patients were prescribed high-dose statin therapy and 39·1 per cent an antithrombotic agent. The median calculated risk of a major cardiovascular event over 10 years was 53 (i.q.r. 44–62) per cent. Controlling all modifiable cardiovascular risk factors based on UK and European guidance targets (LDL-cholesterol less than 2 mmol/l, systolic BP under 140 mmHg, smoking cessation, antiplatelet therapy) would lead to an absolute risk reduction of the median 10-year cardiovascular risk by 29 (20–38) per cent with 6·3 (4·0–9·3) cardiovascular disease-free years gained. Conclusion: The medical management of patients with PAD in this secondary care cohort was suboptimal. Controlling modifiable risk factors to guideline-based targets would confer significant patient benefit.

Background: Previous research suggests patients with Peripheral Arterial Disease (PAD) are not offered adequate risk-factor modification, despite their high cardiovascular risk. We aimed to assess the cardiovascular profiles of patients with PAD in the UK and quantify the survival benefits of target-based risk-factor modification. Methods: The Vascular and Endovascular Research Network (VERN) prospectively collected cardiovascular profiles of patients with PAD from ten UK vascular centres (April - June 2018) to assess practice against UK and European goal-directed BMT guidelines. Risk and benefits of risk-factor control were estimated using the SMART-REACH model, a validated cardiovascular prediction tool for patients with PAD. Results: Overall, 440 patients (mean age: 70±11 years, 24% female) were included. Mean cholesterol (4.3±1.2 mmol/L) and LDL (2.7±1.1 mmol/L) levels were above recommended levels; 319 (73%) patients were hypertensive and 343 (78%) were smokers. Only 12% of patients were prescribed high-dose statin therapy and 39% an antithrombotic agent. The median risk of a major cardiovascular event over 10-years was 53% [Interquartile Range (IQR): 44-62%]. Controlling all modifiable cardiovascular risk-factors based on UK and European guidance targets (LDL