• Bernhard Wernly, Behrooz Mamandipoor, Philipp Baldia, Christian Jung, and Venet Osmani.
• Department of Cardiology, Paracelsus Medical University of Salzburg, Austria; Division of Cardiology, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden. Electronic address: bernhard@wernly.at.
• Int J Med Inform. 2021 Jan 1; 145: 104312.

PurposeTo evaluate the application of machine learning methods, specifically Deep Neural Networks (DNN) models for intensive care (ICU) mortality prediction. The aim was to predict mortality within 96 hours after admission to mirror the clinical situation of patient evaluation after an ICU trial, which consists of 24-48 hours of ICU treatment and then "re-triage". The input variables were deliberately restricted to ABG values to maximise real-world practicability.MethodsWe retrospectively evaluated septic patients in the multi-centre eICU dataset as well as single centre MIMIC-III dataset. Included were all patients alive after 48 hours with available data on ABG (n = 3979 and n = 9655 ICU stays for the multi-centre and single centre respectively). The primary endpoint was 96 -h-mortality.ResultsThe model was developed using long short-term memory (LSTM), a type of DNN designed to learn temporal dependencies between variables. Input variables were all ABG values within the first 48 hours. The SOFA score (AUC of 0.72) was moderately predictive. Logistic regression showed good performance (AUC of 0.82). The best performance was achieved by the LSTM-based model with AUC of 0.88 in the multi-centre study and AUC of 0.85 in the single centre study.ConclusionsAn LSTM-based model could help physicians with the "re-triage" and the decision to restrict treatment in patients with a poor prognosis.Copyright © 2020 Elsevier B.V. All rights reserved.

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