Evaluating current acute aortic syndrome pathways: Collaborative Acute Aortic Syndrome Project (CAASP)

Acknowledgements: J.Z. and A.A.S. share joint authorship. S.N. and G.V share joint senior authorship. We would like to thank The Aortic Disease Charitable Trust for their support of this work.Abstract Background Diagnosis of acute aortic syndrome is challenging and associated with high perihospital mortality rates. The study aim was to evaluate current pathways and understand the chronology of acute aortic syndrome patient care. Method Consecutive patients with acute aortic syndrome imaging diagnosis between 1 January 2018 and 1 June 2021 were identified using a predetermined search strategy and followed up for 6 months through retrospective case note review. The UK National Interventional Radiology Trainee Research and Vascular and Endovascular Research Network co-ordinated the study. Results From 15 UK sites, 620 patients were enrolled. The median age was 67 (range 25–98) years, 62.0% were male and 92.9% Caucasian. Type-A dissection (41.8%) was most common, followed by type-B (34.5%); 41.2% had complicated acute aortic syndrome. Mode of presentation included emergency ambulance (80.2%), self-presentation (16.2%), and primary care referral (3.6%). Time (median (i.q.r.)) to hospital presentation was 3.1 (1.8–8.6) h and decreased by sudden onset chest pain but increased with migratory pain or hypertension. Time from hospital presentation to imaging diagnosis was 3.2 (1.3–6.5) h and increased by family history of aortic disease and decreased by concurrent ischaemic limb. Time from diagnosis to treatment was 2 (1.0–4.3) h with interhospital transfer causing delay. Management included conservative (60.2%), open surgery (32.2%), endovascular (4.8%), hybrid (1.4%) and palliative (1.4%). Factors associated with a higher mortality rate at 30 days and 6 months were acute aortic syndrome type, complicated disease, no critical care admission and age more than 70 years (P &amp;lt; 0.05). Conclusions This study presents a longitudinal data set linking time-based delays to diagnosis and treatment with clinical outcomes. It can be used to prioritize research strategies to streamline patient care. </jats:sec