Patrick Martineau1,2, Piotr Slomka3, Andrew Goertzen1, William D Leslie4,5. 1. Department of Nuclear Medicine, University of Manitoba, 820 Sherbrook Street GC321, Winnipeg, MB, R3A 1R9, Canada. 2. Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard University, Boston, MA, USA. 3. Cedars-Sinai Medical Center, Los Angeles, CA, USA. 4. Department of Nuclear Medicine, University of Manitoba, 820 Sherbrook Street GC321, Winnipeg, MB, R3A 1R9, Canada. bleslie@sbgh.mb.ca. 5. Department of Internal Medicine, University of Manitoba, C5121-409 Tache Ave, Winnipeg, MB, R2H 2A6, Canada. bleslie@sbgh.mb.ca.
Abstract
BACKGROUND: Determining the risk of cardiovascular events is essential to optimize patient management. METHODS AND RESULTS: 5842 individuals underwent SPECT myocardial perfusion imaging (MPI) with 4.4 ± 1.2 years of follow-up. Models (the CRAX tool) were derived to predict the cumulative risk of death and acute myocardial infarction (AMI) at 1, 3, and 5 years using clinical and MPI variables. Predictors of AMI and death included age, number of hospitalizations in the 3 years preceding MPI, and left ventricular ejection fraction (LVEF). Additional predictors of death were the use of pharmacological stress, and global stress total perfusion deficit (sTPD), while transient ischemic dilation (TID), and ischemic total perfusion deficit (iTPD) change were predictive of AMI. CRAX predictions were significantly (P < .001) more accurate than clinical variables or MPI results alone, resulting in a significant net reclassification improvement (NRI, 7.5% for AMI, 14.5% death) compared to clinical variables alone. Accuracy for predicting major adverse cardiac events (MACE, comprising all-cause death, AMI, unstable angina, late revascularization) was comparable to that of AMI or death. CONCLUSIONS: CRAX is a risk assessment tool that predicts the risk of AMI, death, or MACE, and improves prediction compared to clinical variables or MPI results alone.
BACKGROUND: Determining the risk of cardiovascular events is essential to optimize patient management. METHODS AND RESULTS: 5842 individuals underwent SPECT myocardial perfusion imaging (MPI) with 4.4 ± 1.2 years of follow-up. Models (the CRAX tool) were derived to predict the cumulative risk of death and acute myocardial infarction (AMI) at 1, 3, and 5 years using clinical and MPI variables. Predictors of AMI and death included age, number of hospitalizations in the 3 years preceding MPI, and left ventricular ejection fraction (LVEF). Additional predictors of death were the use of pharmacological stress, and global stress total perfusion deficit (sTPD), while transient ischemic dilation (TID), and ischemic total perfusion deficit (iTPD) change were predictive of AMI. CRAX predictions were significantly (P < .001) more accurate than clinical variables or MPI results alone, resulting in a significant net reclassification improvement (NRI, 7.5% for AMI, 14.5% death) compared to clinical variables alone. Accuracy for predicting major adverse cardiac events (MACE, comprising all-cause death, AMI, unstable angina, late revascularization) was comparable to that of AMI or death. CONCLUSIONS: CRAX is a risk assessment tool that predicts the risk of AMI, death, or MACE, and improves prediction compared to clinical variables or MPI results alone.
Authors: Leslee J Shaw; Peter W F Wilson; Rory Hachamovitch; Robert C Hendel; Salvador Borges-Neto; Daniel S Berman Journal: JACC Cardiovasc Imaging Date: 2010-11
Authors: Piotr J Slomka; Mathews B Fish; Santiago Lorenzo; Hidetaka Nishina; James Gerlach; Daniel S Berman; Guido Germano Journal: J Nucl Cardiol Date: 2006-09 Impact factor: 5.952
Authors: Piotr J Slomka; Hidetaka Nishina; Daniel S Berman; Cigdem Akincioglu; Aiden Abidov; John D Friedman; Sean W Hayes; Guido Germano Journal: J Nucl Cardiol Date: 2005 Jan-Feb Impact factor: 5.952
Authors: William D Leslie; Shawn A Tully; Marina S Yogendran; Linda M Ward; Khaled A Nour; Colleen J Metge Journal: J Nucl Med Date: 2005-02 Impact factor: 10.057
Authors: D S Berman; X Kang; K F Van Train; H C Lewin; I Cohen; J Areeda; J D Friedman; G Germano; L J Shaw; R Hachamovitch Journal: J Am Coll Cardiol Date: 1998-12 Impact factor: 24.094
Authors: Piotr J Slomka; Jonathan B Moody; Robert J H Miller; Jennifer M Renaud; Edward P Ficaro; Ernest V Garcia Journal: J Nucl Cardiol Date: 2020-10-16 Impact factor: 5.952