Early prediction of ECMO-eligibility for severe ARDS in adults

Abstract Purpose Appropriately identifying and triaging patients with newly diagnosed ARDS who may progress to severe ARDS is a common clinical challenge without any existing tools for assistance. Materials and Methods Using a retrospective cohort, a simple prediction score was developed to improve early identification of acute respiratory distress syndrome (ARDS) patients who were likely to progress to severe ARDS within 7 days. A broad array of comorbidities and physiologic variables were collected for the 12-hour period starting from intubation for ARDS. ECMO eligibility was determined based on published criteria from recent ECMO guidelines and clinical trials. Separate data-driven and expert opinion approaches to prediction score creation were completed. Results The study included 767 patients with moderate or severe ARDS who were admitted to the intensive care unit between January 1, 2005 and December 31, 2010. In the data-driven approach, incorporating the ARDS index (a novel variable incorporating oxygenation index and estimated dead space), aspiration and change of PaO2/FiO2 ratio into a simple prediction model yielded a c-statistic (ROC-AUC) of 0.71 in the validation cohort. The expert opinion based prediction score (including oxygenation index, change of PaO2/FiO2 ratio, obesity, aspiration and immunocompromised state) yielded a c-statistic of 0.61 in the validation cohort. Conclusions The data-driven e arly p rediction E CMO-eligibility for s evere A RDS (EPESA) score uses commonly measured variables of ARDS patients within 12 hours of intubation, and could be used to identify those patients who may merit early transfer to an ECMO-capable medical center.