Automatic Prediction of Coronary Heart Disease Events Using Coronary and Thoracic Aorta Calcium among African Americans in the Jackson Heart Study

Tuesday, Dec. 3 3:00PM - 3:10PM Room: N229

Sanne G. van Velzen, MSc, Utrecht , Netherlands (Presenter) Nothing to Disclose
James G. Terry, MS, Nashville, TN (Abstract Co-Author) Nothing to Disclose
Bob D. De Vos, MSc, Utrecht , Netherlands (Abstract Co-Author) Nothing to Disclose
Nikolas Lessmann, MSc, Nijmegen, Netherlands (Abstract Co-Author) Nothing to Disclose
Sangeeta Nair, DVM, Nashville, TN (Abstract Co-Author) Nothing to Disclose
Adolfo Correa, Jackson, MS (Abstract Co-Author) Nothing to Disclose
Helena Verkooijen, Utrecht, Netherlands (Abstract Co-Author) Nothing to Disclose
John J. Carr, MD, MS, Nashville, TN (Abstract Co-Author) Nothing to Disclose
Ivana Isgum, PhD, Utrecht, Netherlands (Abstract Co-Author) Research Grant, Pie Medical Imaging BV Research Grant, 3mensio Medical Imaging BV Research Grant, Koninklijke Philips NV

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Coronary artery calcium (CAC) and thoracic aorta calcium (TAC) are predictors of CHD events. Given that CAC and TAC identification is time-consuming, methods for automatic quantification in CT have been developed. Hence, we investigate whether subjects who will experience a CHD event within 5 years from acquisition of cardiac CT can be identified using automatically extracted calcium scores.


We included 2532 participants (age 5911, 31% male) of the Jackson Heart Study without CHD history: 111 participants had a CHD event within 5 years from CT acquisition, defined by death certificates and medical records. For each subject a cardiac CT scan(GE Healthcare Lightspeed 16Pro, 2.5mm slice thickness, 2.5mm increment, 120kVP, 400mAs, ECG-triggered, no contrast) was available. Per-artery Agatston CAC scores (left anterior descending, left circumflex, right coronary artery) and TAC volume were automatically extracted with a previously developed AI algorithm. Scores were log transformed, combined with age and sex and all continuous variables were normalized to zero-mean and unit variance. We evaluated 3 models with 3-fold cross-validation where subjects were classified according to occurrence of CHD event using LASSO regression with 1) age, sex and CAC scores, 2) age, sex and TAC scores, and 3) all variables. Performance was evaluated with the area under the ROC curve (AUC).


In 1468 (58%) subjects no CAC and in 1240 (49%) no TAC was found. In remaining scans, median (range) CAC score was 78.7(1.6-5562.1): 49.5(0.0-4569.4), 0.0(0.0-2735.3), 3.9(0.0-3242.7) in the LDA, LCX and RCA, respectively. Median TAC volume was 116.8(4.7-7275.9). Prediction of CHD events using Model 1, 2 and 3 resulted in an AUC (95% CI) of 0.721(0.672-0.771), 0.735(0.686-0.785) and 0.727(0.678-0.776). Differences between the ROC curves were not significant (Model 1 and 2: p=0.80; 1 and 3: p=0.29; 2 and 3: p=0.76).


Identification of subjects at risk of a CHD event can be performed using automatically extracted CAC or TAC scores from cardiac CT.


Prediction of CHD events from cardiac CT using TAC instead of CAC is feasible and may be advantageous in scans acquired without ECG-triggering or low image resolution.

Printed on: 10/29/20