RATIONALE: Increasing use of extracorporeal membrane oxygenation (ECMO) for acute respiratory failure may increase resource requirements and hospital costs. Better prediction of survival in these patients may improve resource use, allow risk-adjusted comparison of center-specific outcomes, and help clinicians to target patients most likely to benefit from ECMO. OBJECTIVES: To create a model for predicting hospital survival at initiation of ECMO for respiratory failure. METHODS: Adult patients with severe acute respiratory failure treated by ECMO from 2000 to 2012 were extracted from the Extracorporeal Life Support Organization (ELSO) international registry. Multivariable logistic regression was used to create the Respiratory ECMO Survival Prediction (RESP) score using bootstrapping methodology with internal and external validation. MEASUREMENTS AND MAIN RESULTS: Of the 2,355 patients included in the study, 1,338 patients (57%) were discharged alive from hospital. The RESP score was developed using pre-ECMO variables independently associated with hospital survival on logistic regression, which included age, immunocompromised status, duration of mechanical ventilation before ECMO, diagnosis, central nervous system dysfunction, acute associated nonpulmonary infection, neuromuscular blockade agents or nitric oxide use, bicarbonate infusion, cardiac arrest, PaCO2, and peak inspiratory pressure. The receiver operating characteristics curve analysis of the RESP score was c = 0.74 (95% confidence interval, 0.72-0.76). External validation, performed on 140 patients, exhibited excellent discrimination (c = 0.92; 95% confidence interval, 0.89-0.97). CONCLUSIONS: The RESP score is a relevant and validated tool to predict survival for patients receiving ECMO for respiratory failure.
RATIONALE: Increasing use of extracorporeal membrane oxygenation (ECMO) for acute respiratory failure may increase resource requirements and hospital costs. Better prediction of survival in these patients may improve resource use, allow risk-adjusted comparison of center-specific outcomes, and help clinicians to target patients most likely to benefit from ECMO. OBJECTIVES: To create a model for predicting hospital survival at initiation of ECMO for respiratory failure. METHODS: Adult patients with severe acute respiratory failure treated by ECMO from 2000 to 2012 were extracted from the Extracorporeal Life Support Organization (ELSO) international registry. Multivariable logistic regression was used to create the Respiratory ECMO Survival Prediction (RESP) score using bootstrapping methodology with internal and external validation. MEASUREMENTS AND MAIN RESULTS: Of the 2,355 patients included in the study, 1,338 patients (57%) were discharged alive from hospital. The RESP score was developed using pre-ECMO variables independently associated with hospital survival on logistic regression, which included age, immunocompromised status, duration of mechanical ventilation before ECMO, diagnosis, central nervous system dysfunction, acute associated nonpulmonary infection, neuromuscular blockade agents or nitric oxide use, bicarbonate infusion, cardiac arrest, PaCO2, and peak inspiratory pressure. The receiver operating characteristics curve analysis of the RESP score was c = 0.74 (95% confidence interval, 0.72-0.76). External validation, performed on 140 patients, exhibited excellent discrimination (c = 0.92; 95% confidence interval, 0.89-0.97). CONCLUSIONS: The RESP score is a relevant and validated tool to predict survival for patients receiving ECMO for respiratory failure.
Authors: Nicolas Nesseler; Yoann Launey; Sonia Isslame; Erwan Flécher; Thomas Lebouvier; Yannick Mallédant; Philippe Seguin Journal: Intensive Care Med Date: 2015-03-24 Impact factor: 17.440
Authors: Jeffrey DellaVolpe; Ryan P Barbaro; Jeremy W Cannon; Eddy Fan; Wendy R Greene; Kyle J Gunnerson; Lena M Napolitano; Ace Ovil; Jeremy C Pamplin; Matthieu Schmidt; Lauren R Sorce; Daniel Brodie Journal: Crit Care Med Date: 2020-06 Impact factor: 7.598