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A comparison of existing risk prediction models in patients undergoing venoarterial extracorporeal membrane oxygenation
Authors
D. Avgerinos
I. Gambardella
+5 more
M. Gaudino
I. Gulkarov
S. Khin
K. Ramasubbu
B. Worku
Publication date
1 January 2020
Publisher
Donald and Barbara Zucker School of Medicine Academic Works
Doi
Cite
Abstract
© 2020 The Author(s) Background: Patients undergoing consideration for venoarterial extracorporeal membrane oxygenation (VA-ECMO) require an immediate risk profile assessment in the setting of incomplete information. A number of survival prediction models for critically ill patients and patients undergoing elective cardiac surgery or institution of VA-ECMO support have been designed. We assess the ability of these models to predict outcomes in a cohort of patients undergoing institution of VA-ECMO for cardiogenic shock or cardiac arrest. Methods: Fifty-one patients undergoing institution of VA-ECMO support were retrospectively analyzed. APACHE II, SOFA, SAPS II, Encourage, SAVE, and ACEF scores were calculated. Their ability to predict outcomes were assessed. Results: Indications for ECMO support included postcardiotomy shock (25%), ischemic etiologies (39%), and other etiologies (36%). Pre-ECMO arrest occurred in 73% and 41% of patients underwent cannulation during arrest. Survival to discharge was 39%. Three survival prediction model scores were significantly higher in nonsurvivors to discharge than surivors; the Encourage score (25.4 vs 20; p =.04), the APACHE II score (23.6 vs 19.2; p =.05), and the ACEF score (3.1 vs 1.8; p =.03). In ROC analysis, the ACEF score demonstrated the greatest predictive ability with an AUC of 0.7. Conclusions: A variety of survival prediction model scores designed for critically ill ICU and VA-ECMO patients demonstrated modest discriminatory ability in the current cohort of patients. The ACEF score, while not designed to predict survival in critically ill patients, demonstrated the best discriminatory ability. Furthermore, it is the simplest to calculate, an advantage in the emergent setting
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Last time updated on 16/06/2023