Volumetric analysis of pulmonary CTA for the assessment of right ventricular dysfunction in patients with acute pulmonary embolism.

To retrospectively determine the value of a volumetric ventricle analysis for the assessment of right ventricular dysfunction in patients with suspected pulmonary embolism (PE) by using image data from non-electrocardiographically (ECG)-gated multidetector computed tomography angiography (CTA).
Hypothesizing that the presence of PE and the embolus location correlated with right ventricular dysfunction, we retrospectively analyzed 100 non-ECG-gated pulmonary CTA datasets of patients with central, peripheral, and without PE. Right ventricle/left ventricle (RV/LV) diameter ratio measured in transverse sections (RV/LV(trans)), four-chamber view (RV/LV(4ch)), and RV/LV volume ratio (RV/LV(vol)) were assessed on CT images. The results were correlated with the embolus location, the 30-day mortality rate, and the necessity of intensive care treatment.
All CT parameters showed statistically significant differences between all patients groups depended on embolus location. The receiver operating characteristic analysis RV/LV(vol) showed the strongest discriminatory power to differ between patients with central and without PE and between patients with central and peripheral PE (central PE vs. no PE: RV/LV(vol) = 0.932, RV/LV(trans) = 0.880, and RV/LV(4ch) = 0.811, central PE vs. peripheral PE: RV/LV(vol) = 0.950, RV/LV(trans) = 0.849, and RV/LV(4ch) = 0.881), indicating a correlation with embolus location predisposing for RVD. For the identification of high-risk patients with PE all three CT parameters showed statistically significant values (P < .0001), whereas in the receiver operating characteristic analysis, RV/LV(vol) had the strongest discriminatory power (RV/LV(vol) = 0.819, RV/LV(trans) = 0.799, and RV/LV(4ch) = 0.758).
Ventricle volumetry of non-ECG-gated CTA allows the assessment of right ventricular dysfunction in patients with acute PE. Compared to unidimensional measurements, a volumetric analysis seems to be slightly superior to identify high-risk patients with adverse clinical outcome. However, the method is more time consuming and requires dedicated software tools compared to unidimensional parameters, which is disadvantageous in an emergency setting.