OBJECTIVES: A young person with many risk factors may have the same level of risk as an older person with no risk factors. Thus a high-risk 40-year-old may have a risk age of 60 years or more. The aim of the study was to derive a generic equation for risk age, construct risk age charts, and explore the hypothesis that risk age is similar regardless of the cardiovascular disease (CVD) end point used. METHODS: The equation was generated by equating the generic formula for 10-year CVD risk (with unknown risk factor levels) to the generic formula for 10-year CVD risk in a person with age = x and ideal risk factor levels (total cholesterol 4 mmol/l, systolic blood pressure 120 mm Hg, and non-smoker) and solving for x. To examine the consistency between risk ages for different end points, a risk age based on risk of CVD fatal events and based on risk of fatal and non-fatal CVD events was derived for each of the participants in the FINRISK population study. The correlation between these risk ages was examined. RESULTS: A generic equation for risk age was derived. The generic equation could not be used for SCORE (Systematic COronary Risk Evaluation), because the age is included in the baseline. Therefore a table of SCORE risk ages was developed by looking up the risk age corresponding to each combination of risk factors in the chart. Risk age remains similar regardless of the cardiovascular end point used, which bypasses the dilemma of whether to use a risk-estimation system based on CVD mortality or on the more attractive but less reliable end point of total CVD events. On the basis of the equation, risk age is not dependent on baseline survival and therefore recalibration is not required. CONCLUSIONS: Risk age is an intuitive and easily understood method for communicating about risk, particularly in younger patients, and may facilitate lifestyle change in younger patients. However, treatment decisions should be based on absolute risk, as recommended by guidelines on CVD prevention.
OBJECTIVES: A young person with many risk factors may have the same level of risk as an older person with no risk factors. Thus a high-risk 40-year-old may have a risk age of 60 years or more. The aim of the study was to derive a generic equation for risk age, construct risk age charts, and explore the hypothesis that risk age is similar regardless of the cardiovascular disease (CVD) end point used. METHODS: The equation was generated by equating the generic formula for 10-year CVD risk (with unknown risk factor levels) to the generic formula for 10-year CVD risk in a person with age = x and ideal risk factor levels (total cholesterol 4 mmol/l, systolic blood pressure 120 mm Hg, and non-smoker) and solving for x. To examine the consistency between risk ages for different end points, a risk age based on risk of CVD fatal events and based on risk of fatal and non-fatal CVD events was derived for each of the participants in the FINRISK population study. The correlation between these risk ages was examined. RESULTS: A generic equation for risk age was derived. The generic equation could not be used for SCORE (Systematic COronary Risk Evaluation), because the age is included in the baseline. Therefore a table of SCORE risk ages was developed by looking up the risk age corresponding to each combination of risk factors in the chart. Risk age remains similar regardless of the cardiovascular end point used, which bypasses the dilemma of whether to use a risk-estimation system based on CVD mortality or on the more attractive but less reliable end point of total CVD events. On the basis of the equation, risk age is not dependent on baseline survival and therefore recalibration is not required. CONCLUSIONS: Risk age is an intuitive and easily understood method for communicating about risk, particularly in younger patients, and may facilitate lifestyle change in younger patients. However, treatment decisions should be based on absolute risk, as recommended by guidelines on CVD prevention.
Authors: Massimo F Piepoli; Arno W Hoes; Stefan Agewall; Christian Albus; Carlos Brotons; Alberico L Catapano; Marie-Therese Cooney; Ugo Corrà; Bernard Cosyns; Christi Deaton; Ian Graham; Michael Stephen Hall; F D Richard Hobbs; Maja-Lisa Løchen; Herbert Löllgen; Pedro Marques-Vidal; Joep Perk; Eva Prescott; Josep Redon; Dimitrios J Richter; Naveed Sattar; Yvo Smulders; Monica Tiberi; H Bart van der Worp; Ineke van Dis; W M Monique Verschuren; Simone Binno Journal: Eur Heart J Date: 2016-05-23 Impact factor: 29.983
Authors: Michael J Blaha; Isaac N Naazie; Miguel Cainzos-Achirica; Zeina A Dardari; Andrew P DeFilippis; Robyn L McClelland; Mohammadhassan Mirbolouk; Olusola A Orimoloye; Omar Dzaye; Khurram Nasir; John H Page Journal: J Am Heart Assoc Date: 2021-03-05 Impact factor: 5.501
Authors: Carissa Bonner; Jesse Jansen; Ben R Newell; Les Irwig; Paul Glasziou; Jenny Doust; Haryana Dhillon; Kirsten McCaffery Journal: J Med Internet Res Date: 2014-05-05 Impact factor: 5.428
Authors: Grunde Wibetoe; Joseph Sexton; Eirik Ikdahl; Silvia Rollefstad; George D Kitas; Piet van Riel; Sherine Gabriel; Tore K Kvien; Karen Douglas; Aamer Sandoo; Elke E Arts; Solveig Wållberg-Jonsson; Solbritt Rantapää Dahlqvist; George Karpouzas; Patrick H Dessein; Linda Tsang; Hani El-Gabalawy; Carol A Hitchon; Virginia Pascual-Ramos; Irazu Contreas-Yañes; Petros P Sfikakis; Miguel A González-Gay; Iris J Colunga-Pedraz; Dionicio A Galarza-Delgado; Jose Ramon Azpiri-Lopez; Cynthia S Crowson; Anne Grete Semb Journal: Arthritis Res Ther Date: 2020-04-23 Impact factor: 5.156
Authors: Holger Cramer; Helen Hall; Matthew Leach; Jane Frawley; Yan Zhang; Brenda Leung; Jon Adams; Romy Lauche Journal: Sci Rep Date: 2016-11-10 Impact factor: 4.379