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Related Concept Videos

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Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health

John Karlsson Valik1,2, Logan Ward3,4, Hideyuki Tanushi5

  • 1Division of Infectious Diseases, Department of Medicine, Karolinska Institutet, Solna, Stockholm, Sweden. john.karlsson.valik@ki.se.

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|July 20, 2023
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Summary
This summary is machine-generated.

A new machine learning model, SepsisFinder, accurately predicts sepsis onset using electronic health records outside the ICU. This early warning system identifies patients at high risk, potentially improving survival rates and guiding interventions.

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Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Critical Care Medicine

Background:

  • Sepsis is a major cause of hospital mortality, and early detection is crucial for survival.
  • Current sepsis prediction models often focus on Intensive Care Unit (ICU) patients, missing opportunities for earlier intervention.
  • Digitalization of healthcare data enables the development of automated prediction tools for prompt sepsis recognition.

Purpose of the Study:

  • To develop and validate a machine learning model for early sepsis prediction using routine electronic health record (EHR) data outside the ICU.
  • To compare the performance of the developed model against established methods like the National Early Warning Score 2 (NEWS2).
  • To assess the potential of the model to facilitate timely clinical interventions and improve patient outcomes.

Main Methods:

  • A cohort of 82,852 hospital admissions and 8038 sepsis episodes (Sepsis-3 criteria) was analyzed.
  • A causal probabilistic network model, SepsisFinder, was developed to predict sepsis onset within 48 hours using hourly updated EHR data.
  • Model performance was evaluated using Area Under the Receiver Operating Characteristic Curve (AUROC) and Area Under the Precision-Recall Curve (APR), and compared against NEWS2 and a gradient-boosting model.

Main Results:

  • SepsisFinder demonstrated high predictive accuracy (AUROC 0.950) using sparse EHR data outside the ICU.
  • The model triggered earlier than NEWS2 and a gradient-boosting machine learning model.
  • SepsisFinder signaled sepsis onset a median of 5.5 hours before antibiotic administration, with increased precision in specific patient subgroups.

Conclusions:

  • Machine learning models like SepsisFinder can effectively predict sepsis onset using routine EHR data outside the ICU.
  • Early sepsis detection via SepsisFinder offers a significant clinical benefit by enabling timely interventions.
  • This approach holds promise for identifying high-risk populations and tailoring clinical management to improve sepsis care.