OBJECTIVES: The aim of this study was to derivate and validate a prediction model for cardiovascular events based on quantification of coronary and aortic calcium volume in lung cancer screening chest computed tomography (CT). BACKGROUND: CT-based lung cancer screening in heavy smokers is a very timely topic. Given that the heavily smoking screening population is also at risk for cardiovascular disease, CT-based screening may provide the opportunity to additionally identify participants at high cardiovascular risk. METHODS: Inspiratory screening CT of the chest was obtained in 3,648 screening participants. Next, smoking characteristics, patient demographics, and physician-diagnosed cardiovascular events were collected from 10 years before the screening CT (i.e., cardiovascular history) until 3 years after the screening CT (i.e., follow-up time). Cox proportional hazards analysis was used to derivate and validate a prediction model for cardiovascular risk. Age, smoking status, smoking history, and cardiovascular history, together with automatically quantified coronary and aortic calcium volume from the screening CT, were included as independent predictors. The primary outcome measure was the discriminatory value of the model. RESULTS: Incident cardiovascular events occurred in 145 of 1,834 males (derivation cohort) and 118 of 1,725 males and 2 of 89 females (validation cohort). The model showed good discrimination in the validation cohort with a C-statistic of 0.71 (95% confidence interval: 0.67 to 0.76). When high risk was defined as a 3-year risk of 6% and higher, 589 of 1,725 males were regarded as high risk and 72 of 118 of all events were correctly predicted by the model. CONCLUSIONS: Quantification of coronary and aortic calcium volumes in lung cancer screening CT images-information that is readily available-can be used to predict cardiovascular risk. Such an approach might prove useful in the reduction of cardiovascular morbidity and mortality and may enhance the cost-effectiveness of CT-based screening in heavy smokers.
OBJECTIVES: The aim of this study was to derivate and validate a prediction model for cardiovascular events based on quantification of coronary and aortic calcium volume in lung cancer screening chest computed tomography (CT). BACKGROUND: CT-based lung cancer screening in heavy smokers is a very timely topic. Given that the heavily smoking screening population is also at risk for cardiovascular disease, CT-based screening may provide the opportunity to additionally identify participants at high cardiovascular risk. METHODS: Inspiratory screening CT of the chest was obtained in 3,648 screening participants. Next, smoking characteristics, patient demographics, and physician-diagnosed cardiovascular events were collected from 10 years before the screening CT (i.e., cardiovascular history) until 3 years after the screening CT (i.e., follow-up time). Cox proportional hazards analysis was used to derivate and validate a prediction model for cardiovascular risk. Age, smoking status, smoking history, and cardiovascular history, together with automatically quantified coronary and aortic calcium volume from the screening CT, were included as independent predictors. The primary outcome measure was the discriminatory value of the model. RESULTS: Incident cardiovascular events occurred in 145 of 1,834 males (derivation cohort) and 118 of 1,725 males and 2 of 89 females (validation cohort). The model showed good discrimination in the validation cohort with a C-statistic of 0.71 (95% confidence interval: 0.67 to 0.76). When high risk was defined as a 3-year risk of 6% and higher, 589 of 1,725 males were regarded as high risk and 72 of 118 of all events were correctly predicted by the model. CONCLUSIONS: Quantification of coronary and aortic calcium volumes in lung cancer screening CT images-information that is readily available-can be used to predict cardiovascular risk. Such an approach might prove useful in the reduction of cardiovascular morbidity and mortality and may enhance the cost-effectiveness of CT-based screening in heavy smokers.
Authors: Jurica Šprem; Bob D de Vos; Nikolas Lessmann; Pim A de Jong; Max A Viergever; Ivana Išgum Journal: J Med Imaging (Bellingham) Date: 2018-12-11
Authors: Richard A P Takx; Rozemarijn Vliegenthart; Firdaus A A Mohamed Hoesein; Ivana Išgum; Harry J de Koning; Willem P Th M Mali; Carlijn M van der Aalst; Pieter Zanen; Jan-Willem J Lammers; Harry J M Groen; Eva M van Rikxoort; Michael Schmidt; Bram van Ginneken; Matthijs Oudkerk; Tim Leiner; Pim A de Jong Journal: Eur Radiol Date: 2014-09-03 Impact factor: 5.315
Authors: Caroline Chiles; Fenghai Duan; Gregory W Gladish; James G Ravenel; Scott G Baginski; Bradley S Snyder; Sarah DeMello; Stephanie S Desjardins; Reginald F Munden Journal: Radiology Date: 2015-03-09 Impact factor: 11.105
Authors: Michael A Winkler; Stephen B Hobbs; Richard J Charnigo; Ryan E Embertson; Michael W Daugherty; Michael P Hall; Michael A Brooks; Steve W Leung; Vince L Sorrell Journal: Acad Radiol Date: 2015-01-08 Impact factor: 3.173
Authors: Robbert W van Hamersvelt; Martin J Willemink; Richard A P Takx; Anouk L M Eikendal; Ricardo P J Budde; Tim Leiner; Christian P Mol; Ivana Isgum; Pim A de Jong Journal: Eur Radiol Date: 2014-05-10 Impact factor: 5.315
Authors: Adam Leigh; John W McEvoy; Parveen Garg; J Jeffrey Carr; Veit Sandfort; Elizabeth C Oelsner; Matthew Budoff; David Herrington; Joseph Yeboah Journal: JACC Cardiovasc Imaging Date: 2018-02-14
Authors: Björn A Blomberg; Anders Thomassen; Richard A P Takx; Mie H Vilstrup; Søren Hess; Anne L Nielsen; Axel C P Diederichsen; Hans Mickley; Abass Alavi; Poul F Høilund-Carlsen Journal: J Nucl Cardiol Date: 2013-12-05 Impact factor: 5.952