Development and Evaluation of a Machine Learning Model for the Early Identification of Patients at Risk for Sepsis.

Journal: Annals of emergency medicine
Published Date:

Abstract

STUDY OBJECTIVE: The Third International Consensus Definitions (Sepsis-3) Task Force recommended the use of the quick Sequential [Sepsis-related] Organ Failure Assessment (qSOFA) score to screen patients for sepsis outside of the ICU. However, subsequent studies raise concerns about the sensitivity of qSOFA as a screening tool. We aim to use machine learning to develop a new sepsis screening tool, the Risk of Sepsis (RoS) score, and compare it with a slate of benchmark sepsis-screening tools, including the Systemic Inflammatory Response Syndrome, Sequential Organ Failure Assessment (SOFA), qSOFA, Modified Early Warning Score, and National Early Warning Score.

Authors

  • Ryan J Delahanty
    Tenet Healthcare, Nashville, TN.
  • JoAnn Alvarez
    Tenet Healthcare Corporation, Nashville, TN.
  • Lisa M Flynn
    Tenet Healthcare Corporation, Nashville, TN.
  • Robert L Sherwin
    Department of Emergency Medicine, Wayne State University, Detroit, MI.
  • Spencer S Jones
    Tenet Healthcare, Nashville, TN.