Utilisation of the signature method to identify the early onset of sepsis from multivariate physiological time series in critical care monitoring

Journal article

Objectives: Patients 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. Design: The 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. Setting: The work was completed as part of the PhysioNet 2019 Challenge alongside 104 other teams. Patients: PhysioNet 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. Interventions: None. Measurements and Main Results: The algorithm yielded a utility function score which was the first placed entry in the official phase of the challenge.

Authors: Morrill, JH; Kormilitzin, A; Nevado-Holgado, A; Swaminathan, S; Howison, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Lippincott, Williams & Wilkins
• Journal: Critical Care Medicine
• Volume: 48
• Issue: 10
• Pages: e976-e981
• Publication date: 2020-08-03
• Acceptance date: 2020-04-16
• DOI: 10.1097/CCM.0000000000004510
• EISSN: 1530-0293
• ISSN: 0090-3493
• Language: English
• Keywords: FFR