Abstract TP26: System-wide Implementation Of Computed Tomography Perfusion (CTP) Imaging

Abstract

Background: Mechanical thrombectomy (MT) is effective for reducing morbidity in patients presenting within 24 hours of symptom onset with large vessel occlusion (LVO). Perfusion imaging is helpful in selecting patients in this extended time window. We report our experience of implementing CTP imaging in all our spokes and its impact on quality metrics and outcomes. Methods: Acute Stroke Imaging Protocol was updated to include CTP in addition to CT and CTA for all suspected stroke patients presenting within 24 hours of symptoms in April 2021 processed by RAPID AI (iSchema View inc, Menlo Park, CA, USA). Consecutive patients aged ≥18 years with suspected stroke presenting to regional spokes between Jan 2021 to Dec 2021 were included in the analysis. Demographics, NIHSS, quality metrics at regional facility (Door-to-thrombolysis, door-in-door-out times) and Comprehensive Stroke Center-CSC (door-to-arterial access, reperfusion rates (TICI IIb/III), hemorrhagic transformation) and functional outcomes (modified Rankin Scale, mRS) at 90 days were compared between the groups. Results: CTP studies were performed on 1113 patients between April 2021 and December 2021. RAPID AI processing identified LVO in 203 (18.7%) patients, of which 29 (14%) patients were included in this analysis; 14 patients were transferred to CSC for possible MT without CTP at regional facility and 15 were transferred with CTP at regional. Age (p=0.44), NIHSS (p=0.08) and onset-arrival time (p=0.54) were similar in both groups at the regional facility. Door to thrombolysis (p=0.13) and door-in-door-out (p=0.17) times were similar in both groups at the regional facility. CSC metrics of door-to-arterial access times (p=0.84), reperfusion (TICI IIB/III) (p=0.83) and hemorrhagic transformation (p=0.49) rates were similar between groups. 90 day good functional outcomes (mRS 0-2) were similar in patients undergoing thrombectomy (p=0.86). Conclusion: Our study suggests that AI supported perfusion imaging is feasible in a large stroke network and does not appear to have a significant impact on process times, quality metrics and outcomes. A larger study would need to be conducted to validate the observed results.