Gijs F N Berkelmans1, Soffia Gudbjörnsdottir2, Frank L J Visseren1, Sarah H Wild3, Stefan Franzen2, John Chalmers4, Barry R Davis5, Neil R Poulter6, Annemieke M Spijkerman7, Mark Woodward4,8,9, Sara L Pressel5, Ajay K Gupta6,10, Yvonne T van der Schouw11, Ann-Marie Svensson2, Yolanda van der Graaf11, Stephanie H Read3, Bjorn Eliasson2, Jannick A N Dorresteijn1. 1. Department of Vascular Medicine, University Medical Center Utrecht, GA Utrecht, the Netherlands. 2. Swedish National Diabetes Register, Center of Registers in Region, Medicinaregatan 18C, Gothenburg, Sweden. 3. Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Old Medical School, Teviot place, EH89AG Edinburgh, UK and the Scottish Diabetes Research Network Epidemiology Group. 4. The George Institute for Global Health, University of New South Wales, Sydney, Level 5, 1 King Street, Newtown NSW, Australia. 5. Department of Biostatistics, University of Texas School of Public Health, Houston, TX, USA. 6. ICCH, Imperial College London, Level 2 Faculty building, South Kensington campus, London, UK. 7. National Institute for Public Health and the Environment (RIVM), 3720 BA, Bilthoven, the Netherlands. 8. Department of Epidemiology, Johns Hopkins University, 615 North Wolfe Street, Baltimore, MD, USA. 9. The George Institute for Global Health, University of Oxford, Hayes House, 75 George Street, Oxford, UK. 10. William Harvey Research Institute, Queen Mary University of London, Mile End Road, London, UK. 11. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, HP: str 6.131, GA Utrecht, the Netherlands.
Abstract
AIMS: Although group-level effectiveness of lipid, blood pressure, glucose, and aspirin treatment for prevention of cardiovascular disease (CVD) has been proven by trials, important differences in absolute effectiveness exist between individuals. We aim to develop and validate a prediction tool for individualizing lifelong CVD prevention in people with Type 2 diabetes mellitus (T2DM) predicting life-years gained without myocardial infarction or stroke. METHODS AND RESULTS: We developed and validated the Diabetes Lifetime-perspective prediction (DIAL) model, consisting of two complementary competing risk adjusted Cox proportional hazards functions using data from people with T2DM registered in the Swedish National Diabetes Registry (n = 389 366). Competing outcomes were (i) CVD events (vascular mortality, myocardial infarction, or stroke) and (ii) non-vascular mortality. Predictors were age, sex, smoking, systolic blood pressure, body mass index, haemoglobin A1c, estimated glomerular filtration rate, non- high-density lipoprotein cholesterol, albuminuria, T2DM duration, insulin treatment, and history of CVD. External validation was performed using data from the ADVANCE, ACCORD, ASCOT and ALLHAT-LLT-trials, the SMART and EPIC-NL cohorts, and the Scottish diabetes register (total n = 197 785). Predicted and observed CVD-free survival showed good agreement in all validation sets. C-statistics for prediction of CVD were 0.83 (95% confidence interval: 0.83-0.84) and 0.64-0.65 for internal and external validation, respectively. We provide an interactive calculator at www.U-Prevent.com that combines model predictions with relative treatment effects from trials to predict individual benefit from preventive treatment. CONCLUSION: Cardiovascular disease-free life expectancy and effects of lifelong prevention in terms of CVD-free life-years gained can be estimated for people with T2DM using readily available clinical characteristics. Predictions of individual-level treatment effects facilitate translation of trial results to individual patients. Published on behalf of the European Society of Cardiology. All rights reserved.
AIMS: Although group-level effectiveness of lipid, blood pressure, glucose, and aspirin treatment for prevention of cardiovascular disease (CVD) has been proven by trials, important differences in absolute effectiveness exist between individuals. We aim to develop and validate a prediction tool for individualizing lifelong CVD prevention in people with Type 2 diabetes mellitus (T2DM) predicting life-years gained without myocardial infarction or stroke. METHODS AND RESULTS: We developed and validated the Diabetes Lifetime-perspective prediction (DIAL) model, consisting of two complementary competing risk adjusted Cox proportional hazards functions using data from people with T2DM registered in the Swedish National Diabetes Registry (n = 389 366). Competing outcomes were (i) CVD events (vascular mortality, myocardial infarction, or stroke) and (ii) non-vascular mortality. Predictors were age, sex, smoking, systolic blood pressure, body mass index, haemoglobin A1c, estimated glomerular filtration rate, non- high-density lipoprotein cholesterol, albuminuria, T2DM duration, insulin treatment, and history of CVD. External validation was performed using data from the ADVANCE, ACCORD, ASCOT and ALLHAT-LLT-trials, the SMART and EPIC-NL cohorts, and the Scottish diabetes register (total n = 197 785). Predicted and observed CVD-free survival showed good agreement in all validation sets. C-statistics for prediction of CVD were 0.83 (95% confidence interval: 0.83-0.84) and 0.64-0.65 for internal and external validation, respectively. We provide an interactive calculator at www.U-Prevent.com that combines model predictions with relative treatment effects from trials to predict individual benefit from preventive treatment. CONCLUSION:Cardiovascular disease-free life expectancy and effects of lifelong prevention in terms of CVD-free life-years gained can be estimated for people with T2DM using readily available clinical characteristics. Predictions of individual-level treatment effects facilitate translation of trial results to individual patients. Published on behalf of the European Society of Cardiology. All rights reserved.
Authors: David C Goff; Donald M Lloyd-Jones; Glen Bennett; Sean Coady; Ralph B D'Agostino; Raymond Gibbons; Philip Greenland; Daniel T Lackland; Daniel Levy; Christopher J O'Donnell; Jennifer G Robinson; J Sanford Schwartz; Susan T Shero; Sidney C Smith; Paul Sorlie; Neil J Stone; Peter W F Wilson; Harmon S Jordan; Lev Nevo; Janusz Wnek; Jeffrey L Anderson; Jonathan L Halperin; Nancy M Albert; Biykem Bozkurt; Ralph G Brindis; Lesley H Curtis; David DeMets; Judith S Hochman; Richard J Kovacs; E Magnus Ohman; Susan J Pressler; Frank W Sellke; Win-Kuang Shen; Sidney C Smith; Gordon F Tomaselli Journal: Circulation Date: 2013-11-12 Impact factor: 29.690
Authors: Hertzel C Gerstein; Michael E Miller; Robert P Byington; David C Goff; J Thomas Bigger; John B Buse; William C Cushman; Saul Genuth; Faramarz Ismail-Beigi; Richard H Grimm; Jeffrey L Probstfield; Denise G Simons-Morton; William T Friedewald Journal: N Engl J Med Date: 2008-06-06 Impact factor: 91.245
Authors: John B Buse; J Thomas Bigger; Robert P Byington; Lawton S Cooper; William C Cushman; William T Friedewald; Saul Genuth; Hertzel C Gerstein; Henry N Ginsberg; David C Goff; Richard H Grimm; Karen L Margolis; Jeffrey L Probstfield; Denise G Simons-Morton; Mark D Sullivan Journal: Am J Cardiol Date: 2007-04-16 Impact factor: 2.778
Authors: K M Venkat Narayan; James P Boyle; Theodore J Thompson; Stephen W Sorensen; David F Williamson Journal: JAMA Date: 2003-10-08 Impact factor: 56.272
Authors: Tamar I de Vries; Jannick A N Dorresteijn; Yolanda van der Graaf; Frank L J Visseren; Jan Westerink Journal: Diabetes Care Date: 2019-08-15 Impact factor: 19.112
Authors: Bart S Ferket; M G Myriam Hunink; Umesh Masharani; Wendy Max; Joseph Yeboah; Gregory L Burke; Kirsten E Fleischmann Journal: Diabetes Care Date: 2022-04-01 Impact factor: 17.152