Patient-Specific Rehearsal Feasibility Before Endovascular Repair of Ruptured Abdominal Aortic Aneurysm

Purpose: To evaluate the feasibility of a patient-specific rehearsal (PsR) before emergency endovascular aneurysm repairs (eEVAR) and its influence on the operation. Materials and Methods: From February 2016 to October 2016, 10 consecutive patients (mean age 75 +/- 7.4 years; 9 men) presenting with a ruptured abdominal aortic aneurysm (rAAA) suitable for standard EVAR were enrolled in the study. A 3-dimensional (3D) model of the abdominal aorta was generated on a virtual reality simulator based on the patient's computed tomography (CT) images. Following the patient-specific simulation setup, PsR was conducted during patient admission or in parallel with the preoperative eEVAR workup. Measured outcomes were PsR feasibility only in the first 4 patients and impact on operative performance thereafter (changes in device selection, the planning process, clinical outcomes, perioperative mortality, and complication rates). Technical metrics and timing of system setup, rehearsal, interval from patient arrival to the actual procedure, and eEVAR were recorded. Results: Mean time for 3D model creation was 21.3 +/- 7.8 minutes (range 13-37); there was a significant positive relationship between aortic neck diameter and segmentation time (p=0.003). The overall mean time for simulator setup and PsR was 54 +/- 14 minutes (range 37-80); PsR alone was completed in a mean 31 +/- 40 minutes (95% confidence interval -60 to -2.2). The actual eEVAR procedure duration was 69 +/- 16 minutes (range 45-90). No delay in the actual eEVAR procedure was registered owing to the PsR pathway. In 6 patients, preprocedure rehearsal induced changes in operative strategy, including device selection, main body introduction side, and/or deployment configuration. In 4 cases, rehearsal was performed twice to achieve optimal performance. Conclusion: PsR before eEVAR was feasible in all cases and caused no time delays in the actual eEVAR procedure. PsR optimized eEVAR planning by identifying optimal strategy for stent-graft component selection and deployment.