Prediction of mortality in adult patients with severe acute lung failure receiving veno-venous extracorporeal membrane oxygenation: A prospective observational study
Journal article, Peer reviewed
MetadataShow full item record
Introduction Veno-venous extracorporeal membrane oxygenation (vvECMO) can be a life-saving therapy in patients with severe acute lung failure refractory to conventional therapy. Nevertheless, vvECMO is a procedure associated with high costs and resource utilization. The aim of this study was to assess published models for prediction of mortality following vvECMO and optimize an alternative model. Methods Established mortality risk scores were validated to assess their usefulness in 304 adult patients undergoing vvECMO for refractory lung failure at the University Medical Center Regensburg from 2008 to 2013. A parsimonious prediction model was developed based on variables available before ECMO initiation using logistic regression modelling. We then assessed whether addition of variables available one day after ECMO implementation enhanced mortality prediction. Models were internally validated and calibrated by bootstrapping (400 runs). Predictive ability, goodness-of-fit and model discrimination were compared across the different models. Results In the present study population, existing mortality prediction tools for vvECMO patients showed suboptimal performance. Evaluated before vvECMO initiation, a logistic prediction model comprising age, immunocompromised state, artificial minute ventilation, pre-ECMO serum lactate and hemoglobin concentrations showed best mortality prediction in our patients (area under curve, AUC: 0.75). Additional information about norepinephrine dosage, fraction of inspired oxygen, C-reactive protein and fibrinogen concentrations the first day following ECMO initiation further improved discrimination (AUC: 0.79, P = 0.03) and predictive ability (likelihood ratio test, P < 0.001). When classifying patients as lower (<40%) or higher (>80%) risk based on their predicted mortality, the pre-ECMO and day1-on-ECMO models had negative/positive predictive values of 76%/82% and 82%/81%, respectively. Conclusions While pre-ECMO mortality prediction remains a challenge due to large patient heterogeneity, evaluation one day after ECMO initiation may improve the ability to separate lower- and higher-risk patients. Our findings support the clinical perception that chronic health condition, high comorbidity and reduced functional reserves are strongly related to survival during and following ECMO support. Renewed evaluation the first day after ECMO initiation may provide enhanced guidance for further handling of ECMO patients. Despite the usefulness of prediction models, thorough clinical evaluation should always represent the cornerstone in decision for ECMO.