Development and Validation of Generalized Linear Regression Models to Predict Vessel Enhancement on Coronary CT...

We evaluated the effect of various patient characteristics and time-density curve (TDC)-factors on the test bolus-affected vessel enhancement on coronary computed tomography angiography (CCTA). We also assessed the value of generalized linear regression models (GLMs) for predicting enhancement on CCTA.

We performed univariate and multivariate regression analysis to evaluate the effect of patient characteristics and to compare contrast enhancement per gram of iodine on test bolus (ΔHUTEST) and CCTA (ΔHUCCTA). We developed GLMs to predict ΔHUCCTA. GLMs including independent variables were validated with 6-fold cross-validation using the correlation coefficient and Bland-Altman analysis.

In multivariate analysis, only total body weight (TBW) and ΔHUTEST maintained their independent predictive value (p < 0.001). In validation analysis, the highest correlation coefficient between ΔHUCCTA and the prediction values was seen in the GLM (r = 0.75), followed by TDC (r = 0.69) and TBW (r = 0.62). The lowest Bland-Altman limit of agreement was observed with GLM-3 (mean difference, -0.0 ± 5.1 Hounsfield units/grams of iodine [HU/gI]; 95% confidence interval [CI], -10.1, 10.1), followed by ΔHUCCTA (-0.0 ± 5.9 HU/gI; 95% CI, -11.9, 11.9) and TBW (1.1 ± 6.2 HU/gI; 95% CI, -11.2, 13.4).

We demonstrated that the patient's TBW and ΔHUTEST significantly affected contrast enhancement on CCTA images and that the combined use of clinical information and test bolus results is useful for predicting aortic enhancement.