Korean J Radiol.  2018 Dec;19(6):1021-1030. 10.3348/kjr.2018.19.6.1021.

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

• Masuda T
• Nakaura T
• Funama Y
• Sato T
• Higaki T
• Kiguchi M
• Matsumoto
• Yamashita
• Imada N
• Awai K

• KMID: 2424842
• DOI: http://doi.org/10.3348/kjr.2018.19.6.1021

Abstract

OBJECTIVE
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.
MATERIALS AND METHODS
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.
RESULTS
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).
CONCLUSION
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.

Keyword

MDCT; Computed tomography; Heart; Aorta contrast medium; Aortic enhancement; Cardiac output

MeSH Terms

Angiography*
Body Weight
Cardiac Output
Heart
Humans
Iodine
Linear Models*
Multivariate Analysis
Iodine

Figure

• 

  Fig. 1 Scattergrams of relationship between aortic enhancement and scan protocols using TBW for selecting iodinated contrast material dose and patient age (A), height (B), TBW (C), cardiac output (D), peak time (E), and ΔHUTEST (F).There was significant positive correlation between ΔHUCCTA and age (r = 0.34). Inverse correlation was seen between ΔHUCCTA and TBW (r = 0.67), height (r = 0.43), CO (r = 0.34), and ΔHUTEST (r = 0.75) by linear regression analysis (p < 0.01 for all). There was no significant correlation between peak time of test bolus and ΔHUCCTA (r = 0.14, p = 0.142). gI = grams of iodine, HU = Hounsfield units, TBW = total body weight, ΔHUCCTA = per gram of iodine on coronary computed tomography angiography, ΔHUTEST = per gram of iodine on test bolus

• 

  Fig. 2 Scattergrams of relationship between ΔHUCCTA and GLMs using TBW (A), TDC (B), and GLMs (C).By validation analysis, GLMs manifested highest correlation coefficient with prediction values (r = 0.75), followed by TDC (r = 0.69) and TBW (r = 0.62). GLMs = generalized linear regression models, TDC = time-density curve

• 

  Fig. 3 Bland–Altman limit of relationship between difference in measured value and predicted value, and mean of measured value and predicted value obtained for GLMs using TBW (A), TDC (B), and GLMs (C).Lowest Bland–Altman limit of agreement observed with GLMs (mean difference −0.0 ± 5.0 HU/gI, 95% CI: −10.1, 10.1 HU/gI), ΔHUCCTA (−0.0 ± 5.9 HU/gI, 95% CI: −11.9, 11.9 HU/gI), and TBW (1.1 ± 6.1 HU/gI, 95% CI: −11.1, 13.3 HU/gI). CI = confidence interval

• 

  Fig. 4 67-year-old woman with chest pain.Axial images (A–C) and TDC (D) are shown. ΔHUTEST was 60.2 HU/mgI and TBW was 67 kg. Predicted ΔHUCCTA was calculated with GLM-1 (ΔHUTEST) as 43.7 HU/gI, with GLM-2 (TBW) as 27.9 HU/gI, and with GLM-3 as 35.6 HU/gI. Actual ΔHUCCTA was 35.3 HU/gI.

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