Improvement of Early Outcomes in Type A Acute Aortic Syndrome after an Aorta Code Implementation.

ANNALS OF THORACIC SURGERY(2024)

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摘要
BACKGROUND Reduction of variability through process reengineering can improve surgical results for patients with type A acute aortic syndrome. We compare short-term results before and after implementation of an Aorta Code for patients with type A acute aortic syndrome who underwent surgery. METHODS The Aorta Code was implemented in a 5 -hospital healthcare network in 2019. This critical pathway was based on a simple diagnostic algorithm, ongoing training, immediate patient transfer, and treatment by an expert multidisciplinary team. We retrospectively compared all patients operated on in our center before (2005-2018) and after (January 2019 to February 2023) its implementation. RESULTS One hundred two and 70 patients underwent surgery in the precode and code periods, respectively. In the code period the number of patients operated on per year increased (from 7.3 to 16.8), and the median elapsed time until diagnosis (6.5 hours vs 4.2 hours), transfer (4 hours vs 2.2 hours), and operating room (2.7 hours vs 1.8 hours) were significantly shorter ( P < .05). Aortic root repair and total arch replacement were more frequent (66.7% vs 82.9% [ P = .003] and 20.6% vs 40% [ P = .001]). Cardiopulmonary bypass and ischemia times were also shorter (179.7 minutes vs 148.2 minutes [ P = .001] and 105 minutes vs 91.2 minutes [ P = .022]). Incidence of prolonged mechanical ventilation (53.9% vs 34.3%, P = .011), major stroke (17.7% vs 7.1%, P = .047), and 30 -day mortality (27.5% vs 7.1%, P = .001) decreased significantly. CONCLUSIONS An Aorta Code can be successfully implemented by using a standardized protocol within a hospital network. The number of cases increased; time to diagnosis, transfer, and operating room were reduced; and 30- day mortality significantly decreased. (c) 2024 by The Society of Thoracic Surgeons. Published by Elsevier Inc.
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关键词
Aortic Root Replacement,Aortic Dissection,Cardiac Surgery Risk Models
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