A comparison of existing risk prediction models in patients undergoing venoarterial extracorporeal membrane oxygenation. Academic Article uri icon

Overview

abstract

  • 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.

publication date

  • March 29, 2020

Research

keywords

  • Extracorporeal Membrane Oxygenation

Identity

Scopus Document Identifier

  • 85082532422

Digital Object Identifier (DOI)

  • 10.1016/j.hrtlng.2020.03.004

PubMed ID

  • 32234259

Additional Document Info

volume

  • 49

issue

  • 5