Background Improved mortality prediction for patients in intensive care units (ICU) remains an important challenge. Many severity scores have been proposed but validation studies have concluded that they are not adequately calibrated. Many flexible algorithms are available, yet none of these individually outperform all others regardless of context. In contrast, the Super Learner (SL), an ensemble machine learning technique that leverages on multiple learning algorithms to obtain better prediction performance, has been shown to perform at least as well as the optimal member of its library. It might provide an ideal opportunity to construct a novel severity score with an improved performance profile. The aim of the present study was to provide a new mortality prediction algorithm for ICU patients using an implementation of the Super Learner, and to assess its performance relative to prediction based on the SAPS II, APACHE II and SOFA scores. Methods We used the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database (v26) including all patients admitted to an ICU at Boston’s Beth Israel Deaconess Medical Center from 2001 to 2008. The calibration, discrimination and risk classification of predicted hospital mortality based on SAPS II, on APACHE II, on SOFA and on our Super Learned-based proposal were evaluated. Performance measures were calculated using cross-validation to avoid making biased assessments. Our proposed score was then externally validated on a dataset of 200 randomly selected patients admitted at the ICU of Hôpital Européen Georges-Pompidou in Paris, France between September 2013 and June 2014. The primary outcome was hospital mortality. The explanatory variables were the same as those included in the SAPS II score. Results 24,508 patients were included, with median SAPS II 38 (IQR: 27–51), median SOFA 5 (IQR: 2–8). A total of 3,002/24,508(12.2%) patients died in the hospital. The two versions of our Super Learner-based proposal yielded average predicted probabilities of death of 0.12 (IQR: 0.02–0.16) and 0.13 (IQR: 0.01–0.19), whereas the corresponding values for the SOFA and SAPS II scores were, respectively, 0.12 (IQR: 0.05–0.15) and 0.30 (IQR: 0.08–0.48). The cross-validated area under the receiver operating characteristics curve (AUROC) for SAPS II and SOFA were 0.78(95%CI: 0.77–0.78) and 0.71 (95%CI: 0.71–0.72), respectively. Our proposal reached an AUROC of 0.85 (95%CI: 0.84–0.85) when the explanatory variables were categorized as in SAPS II, and of 0.88 (95%CI: 0.87–0.89) when the same explanatory variables were included without any transformation. In addition, it exhibited better calibration properties than previous score systems. On the external validation dataset, the AUROC was 0.94 (95%CI: 0.90–0.98) and calibration properties were good. Interpretation As compared to conventional severity scores, our Super Learner-based proposal offers improved performance for predicting hospital mortality in ICU patients. A user-friendly implementation is available online an...

For a given severity, mHLA-DR proved not to a predictive marker of outcome, but a weak trend of mHLA-DR recovery was associated with an increased risk of secondary infection. Monitoring immune functions through mHLA-DR in intensive care unit patients therefore could be useful to identify a high risk of secondary infection.