• James H Morrill, Andrey Kormilitzin, Alejo J Nevado-Holgado, Sumanth Swaminathan, Samuel D Howison, and Terry J Lyons.
• Department of Mathematics, University of Oxford, Oxford, United Kingdom.
• Crit. Care Med. 2020 Oct 1; 48 (10): e976-e981.

ObjectivesPatients in an ICU are particularly vulnerable to sepsis. It is therefore important to detect its onset as early as possible. This study focuses on the development and validation of a new signature-based regression model, augmented with a particular choice of the handcrafted features, to identify a patient's risk of sepsis based on physiologic data streams. The model makes a positive or negative prediction of sepsis for every time interval since admission to the ICU.DesignThe data were sourced from the PhysioNet/Computing in Cardiology Challenge 2019 on the "Early Prediction of Sepsis from Clinical Data." It consisted of ICU patient data from three separate hospital systems. Algorithms were scored against a specially designed utility function that rewards early predictions in the most clinically relevant region around sepsis onset and penalizes late predictions and false positives.SettingThe work was completed as part of the PhysioNet 2019 Challenge alongside 104 other teams.PatientsPhysioNet sourced over 60,000 ICU patients with up to 40 clinical variables for each hour of a patient's ICU stay. The Sepsis-3 criteria was used to define the onset of sepsis.InterventionsNone.Measurements And Main ResultsThe algorithm yielded a utility function score which was the first placed entry in the official phase of the challenge.

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