MDCTA volumetric analysis for the quantification and grading of acute non-cerebral, non-gastrointestinal hemorrhage: a feasibility study.

Purpose: To assess the feasibility of multi-detector computed tomography angiography (MDCTA) volumetry for the quantification and grading of acute non-cerebral, non-gastrointestinal bleeding.

Methods: This retrospective, single-center study investigated consecutive patients with MDCTA positive for active non-cerebral, non-gastrointestinal bleeding, between January 2020 and June 2020. Outcome measures were the quantification of active extravasation at the arterial and parenchymal phase using volumetry measurements, the calculation of active bleeding rate and bleeding grading, 30-day mortality rate, identification of independent predictors of mortality and correlation between volumetric analysis, various clinical features, and the decision to proceed with an intervention.

Results: In total 30 patients (17 females; 56.6%; mean age 70.0 ± 16.0 years) were analyzed. Volumetric analysis was feasible in all cases resulting in excellent inter-observer variability (interclass correlation coefficient 0.999 for arterial and 0.919 for venous volume measurements). Mean volume of contrast extravasation was 1.06 ± 1.09 ml and 3.07 ± 2.48 ml at the arterial and parenchymal phases, respectively. Mean bleeding rate was 6.95 ± 7.82 ml/min. High bleeding volume at arterial phase (grade 4 bleeding) was the only independent predictor of 30-day mortality (HR 1383.58; p = 0.042). There was a positive correlation between bleeding volume at arterial phase (r = 0.340; p = 0.033) and arterial bleeding rate (r = 0.381; p = 0.019) with the decision to proceed with an intervention. Bleeding volume of 0.6 ml was the cutoff value for the prediction of intervention (sensitivity 96.3%; specificity 66.7%).

Conclusions: MDCTA volumetric analysis for the quantification and grading of acute hemorrhage was feasible with excellent inter-observer agreement. The proposed bleeding grading system could optimize decision making and predict clinical outcomes.