• Angelo M Dell'Aquila, Konrad Wisniewski, Adrian-Iustin Georgevici, Gábor Szabó, Francesco Onorati, Cecilia Rossetti, Lenard Conradi, Till Demal, Andreas Rukosujew, Sven Peterss, Radner Caroline, Joscha Buech, Antonio Fiore, Thierry Folliguet, Andrea Perrotti, Amélie Hervé, Francesco Nappi, Angel G Pinto, Javier Rodriguez Lega, Marek Pol, Petr Kacer, Enzo Mazzaro, Giuseppe Gatti, Igor Vendramin, Daniela Piani, Luisa Ferrante, Mauro Rinaldi, Eduard Quintana, Robert Pruna-Guillen, Sebastien Gerelli, Di PernaDarioDCentre Hospitalier Annecy Genevois, France., Metesh Acharya, Hiwa Sherzad, Giovanni Mariscalco, Mark Field, Amer Harky, Manoj Kuduvalli, Matteo Pettinari, Stefano Rosato, Tatu Juvonen, Jormalainen Mikko, Timo Mäkikallio, Caius Mustonen, and Fausto Biancari.
• Department of Cardiac Surgery, University Hospital Halle, Germany; Department of Cardiothoracic Surgery, University Hospital Muenster, Muenster, Germany. Electronic address: am.dellaquila@gmail.com.
• J. Thorac. Cardiovasc. Surg. 2024 Nov 8.

BackgroundThe current study aims to report the presentation of the malperfusion syndrome in patients with acute Type A aortic dissection admitted to surgery and its impact on mortality.MethodsData were retrieved from the multicenter European Registry of Type A Aortic Dissection (ERTAAD). The Penn classification was utilized to categorize malperfusion syndromes. A machine learning algorithm was applied to assess the multivariate interaction's importance regarding in-hospital mortality.ResultsA total of 3,902 consecutive patients underwent repair for Acute Type A Aortic Dissection. Local malperfusion syndrome occurred in 1,584 (40.58%) patients. Multi-organ involvement occurred in 582 patients (36.74%) whereas 1,002 patients (63.26%) had single-organ malperfusion. The prevalence was the highest for cerebral (21.27%) followed by peripheral (13.94%), myocardial (9.7%), renal (9.33%), mesenteric (4.15%), and spinal malperfusion (2.10%). Multi-organ involvement predominantly occurred in organs perfused by the downstream aorta. Malperfusion significantly increased mortality risk (p < 0.001, OR 1.95 ± 0.29). The Boruta machine learning algorithm identified the Penn classification as significantly associated with in-hospital mortality (p< 0.0001, variable importance = 7.91), however, 8 other variables yielded higher prediction importance. According to the Penn classification mortality rates were for Penn A = 12.38%, Penn B = 20.71% Penn C = 28.90%, and Penn BC = 31.84% respectively.ConclusionsNearly half of the examined cohort presented with signs of malperfusion syndrome predominantly due to local involvement. More than one-third of patients with local malperfusion syndrome had a multivessel involvement. Furthermore, different levels of Penn classification can be used only as a first tool for preliminary stratification of early mortality risk.Copyright © 2024. Published by Elsevier Inc.

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