BACKGROUND: Identification of individuals at high risk for cardiovascular disease (CVD) is important to initiate adequate treatment and to prevent future events. Moreover, identification of low-risk individuals is important to refrain from unneeded therapy. Current risk prediction models do not accurately predict the risk of CVD in individuals, and new markers have been sought to improve the risk assessment in individuals. Coronary artery calcification (CAC) is a marker of atherosclerosis that might improve current risk assessment when added to traditional risk factors. MATERIALS AND METHODS: We performed a systematic review on PubMed search (1 February 2011) on studies reporting on the added value of CAC in risk prediction in asymptomatic individuals. RESULTS: Of 39 publications on CAC and CVD, nine studies were carried out in asymptomatic individuals. All studies showed an increase in area under the curve ranging from 0.05 to 0.20 when CAC was added to the risk model. Four studies reported on improvements of individuals in low-, intermediate-, and high-risk categories. Addition of CAC to the risk model resulted in a net reclassification improvement ranging from 14% to 30%, meaning that CAC measurement reclassified a substantial proportion of individuals into correct risk categories. This improvement was most pronounced in those at intermediate Framingham risk. CONCLUSIONS: The available studies consistently showed that CAC scoring improves risk stratification in CVD risk categories when added to traditional risk factors only, especially among individuals at intermediate risk for CVD. Cost-effectiveness analyses together with a randomized controlled trial are needed before widespread introduction of CAC in clinical care.
BACKGROUND: Identification of individuals at high risk for cardiovascular disease (CVD) is important to initiate adequate treatment and to prevent future events. Moreover, identification of low-risk individuals is important to refrain from unneeded therapy. Current risk prediction models do not accurately predict the risk of CVD in individuals, and new markers have been sought to improve the risk assessment in individuals. Coronary artery calcification (CAC) is a marker of atherosclerosis that might improve current risk assessment when added to traditional risk factors. MATERIALS AND METHODS: We performed a systematic review on PubMed search (1 February 2011) on studies reporting on the added value of CAC in risk prediction in asymptomatic individuals. RESULTS: Of 39 publications on CAC and CVD, nine studies were carried out in asymptomatic individuals. All studies showed an increase in area under the curve ranging from 0.05 to 0.20 when CAC was added to the risk model. Four studies reported on improvements of individuals in low-, intermediate-, and high-risk categories. Addition of CAC to the risk model resulted in a net reclassification improvement ranging from 14% to 30%, meaning that CAC measurement reclassified a substantial proportion of individuals into correct risk categories. This improvement was most pronounced in those at intermediate Framingham risk. CONCLUSIONS: The available studies consistently showed that CAC scoring improves risk stratification in CVD risk categories when added to traditional risk factors only, especially among individuals at intermediate risk for CVD. Cost-effectiveness analyses together with a randomized controlled trial are needed before widespread introduction of CAC in clinical care.
Authors: Jing Chen; Matthew J Budoff; Muredach P Reilly; Wei Yang; Sylvia E Rosas; Mahboob Rahman; Xiaoming Zhang; Jason A Roy; Eva Lustigova; Lisa Nessel; Virginia Ford; Dominic Raj; Anna C Porter; Elsayed Z Soliman; Jackson T Wright; Myles Wolf; Jiang He Journal: JAMA Cardiol Date: 2017-06-01 Impact factor: 14.676
Authors: Robyn L McClelland; Neal W Jorgensen; Matthew Budoff; Michael J Blaha; Wendy S Post; Richard A Kronmal; Diane E Bild; Steven Shea; Kiang Liu; Karol E Watson; Aaron R Folsom; Amit Khera; Colby Ayers; Amir-Abbas Mahabadi; Nils Lehmann; Karl-Heinz Jöckel; Susanne Moebus; J Jeffrey Carr; Raimund Erbel; Gregory L Burke Journal: J Am Coll Cardiol Date: 2015-10-13 Impact factor: 24.094
Authors: Cilie C van 't Klooster; Yolanda van der Graaf; Hendrik M Nathoe; Michiel L Bots; Gert J de Borst; Frank L J Visseren; Tim Leiner Journal: Int J Cardiovasc Imaging Date: 2021-02-12 Impact factor: 2.357
Authors: Wei Chen; Jessica Fitzpatrick; Stephen M Sozio; Bernard G Jaar; Michelle M Estrella; Dario F Riascos-Bernal; Tong Tong Wu; Yunping Qiu; Irwin J Kurland; Ruth F Dubin; Yabing Chen; Rulan S Parekh; David A Bushinsky; Nicholas E S Sibinga Journal: Kidney360 Date: 2021-02
Authors: David M Maahs; Diana Jalal; Michel Chonchol; Richard J Johnson; Marian Rewers; Janet K Snell-Bergeon Journal: Diabetes Care Date: 2013-07-08 Impact factor: 19.112
Authors: Marcelle G A van Eupen; Miranda T Schram; Helen M Colhoun; Jean L J M Scheijen; Coen D A Stehouwer; Casper G Schalkwijk Journal: Cardiovasc Diabetol Date: 2013-10-17 Impact factor: 9.951