Extracorporeal membrane oxygenation (ECMO) has matured considerably from its initial development by John H. Gibbon, MD, in Philadelphia, PA, in 1937.1 Since then, the evolution of perfusion technology (e.g., centrifugal flow pumps and polymethylpentene membrane oxygenators) and cardiac critical care medicine2 have allowed circuits to be miniaturized and have enabled their role in higher acuity of critically ill patients to expand without a decriment in outcomes.
Mendiratta and colleagues3 from the University of Arkansas have performed an excellent retrospective review of the Extracorporeal Life Support Organization (ELSO) Registry.4 In their review of 99 elderly (>65 years of age) patients who underwent ECMO as part of cardiopulmonary resuscitation (E-CPR), they were able to elucidate two key findings: 1) preexisting renal insufficiency is a harbinger of a poor outcome and 2) while E-CPR outcomes in the elderly are not stellar, they appear to be better than conventional CPR. These results bring the question of which patient can most optimally benefit from this advanced resuscitative technology to the forefront.
In the current study, Mendiratta et al.3 were able to demonstrate a 22.2% survival to discharge in the elderly population. Given the inherent comorbidities of this population, the practitioners who enroll data in the ELSO Registry are to be commended. Conventional survival to discharge with E-CPR has been reported to be 27%.5 In evaluating our own data in the E-CPR population at The Ohio State University, we have been able to achieve 57% successful decannulation in the E-CPR setting, with a 29% survival to discharge (unpublished data). From these data, one could crudely predict a 25–30% survival to discharge for E-CPR. This demonstrates room for improvement while patients are supported with ECMO or the need for development of predictive models which would enable more optimum patient selection. We would argue the latter.
Other areas of critical care medicine have benefitted from predictive models (e.g., Sepsis-related Organ Failure Assessment).6 Researchers in the ECMO/E-CPR arena have identified risk factors which can foretell a poor outcome, such as age, preexisting renal insufficiency, disease process, and the need for anticoagulation.3,5,7–9 Although these risk factors may aid in our prognostication to some degree, the initiation of ECMO or its continuation does not currently have a beneficial predictive model (such as that potentially provided by a back propagating artificial neural network).10
With an ever-expanding eye on process improvement, quality, and resource utilization that has evolved with the National Surgical Quality Improvement Program, it is imperative that those practitioners with expertise and belief in the benefits of ECMO develop expanded predictive models of efficacy and survival. As these models evolve, we should call for the expansion of the ELSO Registry to include the data elements to enable their validation nationally and internationally and further improve the outcomes being obtained by ECMO practitioners.
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