Background: The 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.
Methods: Data were retrieved from the multicenter European Registry of Type A Aortic Dissection. The Penn classification was used to categorize malperfusion syndromes. A machine-learning algorithm was applied to assess the multivariate interaction's importance regarding in-hospital mortality.
Results: A total of 3902 consecutive patients underwent repair for acute type A aortic dissection. Local malperfusion syndrome occurred in 1584 (40.59%) patients. Multiorgan involvement occurred in 582 patients (36.74%) whereas 1002 patients (63.26%) had single-organ malperfusion. The prevalence was the greatest for cerebral (21.27%) followed by peripheral (13.94%), myocardial (9.7%), renal (9.33%), mesenteric (4.15%), and spinal malperfusion (2.10%). Multiorgan involvement predominantly occurred in organs perfused by the downstream aorta. Malperfusion significantly increased the risk of mortality (P < .001; odds ratio, 1.94 ± 0.29). The Boruta machine-learning algorithm identified the Penn classification as significantly associated with in-hospital mortality (P < .0001, variable importance = 7.91); however, 8 other variables yielded greater prediction importance. According to the Penn classification, mortality rates were 12.38% for Penn A, 20.71% for Penn B, 28.90% for Penn C, and 31.84% for Penn BC, respectively.
Conclusions: Nearly one half of the examined cohort presented with signs of malperfusion syndrome predominantly attributable 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.
Keywords: aortic dissection; machine learning; malperfusion.
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