Prediction of the Microbial Origin of Presumed Sepsis in PICU Encounters

Team: Team Pandas

• Program: Biomedical Engineering
• Course: Precision Care Medicine

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Project Post Summary:

Almost one million Americans are admitted to the intensive care unit with sepsis. The standard treatment for treating patients with sepsis is to administer broad-spectrum antibodies because of the lack of microbiological data available upon arrival to the ICU. This is especially problematic given that at least 50% of original “presumed sepsis” diagnoses are incorrect and in fact not of bacterial origin, resulting in the substantial overuse of antibiotics. Consequently, clinicians need a faster and more accurate method of diagnosing the microbiological origin of sepsis for children in the pediatric intensive care unit in order to deliver antibiotic-specific treatments in a timely manner, while preventing the overuse of broad-spectrum antibiotics. We aim to use machine learning to accurately predict the microbiological origin of infection in children faster than the time it takes hospital lab tests to return. We hypothesize that a predictive model analyzing a patient’s Physiological Time Series Data and electronic medical records can identify the origin of infection in the first 48 hours of PICU admission. Patient encounters were labeled as Bacterial, Nonbacterial, or Not Infected by clinical experts based on observed temperature instability and blood culture results in the PICU. Using the Random Forest Classifier in a One Vs. Rest Multi-Class Classification, we were able to predict the encounter’s microbial origin of infection using physiological time series data, all with AUC over 0.6. We were able to identify windows of physiological signals that are relevant to each classification.

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