Related Experiment Video
Updated: Jul 13, 2025
A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
Children are small adults (when properly normalized): Transferrable/generalizable sepsis prediction
Caitlin Marassi1, Damien Socia1, Dale Larie1
1Department of Surgery, University of Vermont, 89 Beaumont Ave, Given D319, Burlington, VT 05405, United States of America.
Insights
This study developed a machine learning model to predict sepsis in children by normalizing pediatric physiological data to adult standards. The model achieved high accuracy in both pediatric and adult populations, demonstrating generalizability across age groups.
Area of Science:
- Biomedical Informatics
- Machine Learning in Healthcare
- Pediatric Critical Care
Background:
- Pediatric sepsis presents unique physiological trajectories distinct from adult cases.
- Limited pediatric sepsis datasets hinder direct model development.
- Leveraging shared biology requires normalizing pediatric data for adult comparability.
Purpose of the Study:
- To normalize pediatric physiological data for direct comparison with adult data.
- To develop machine learning classifiers for predicting pediatric sepsis onset.
- To externally validate these classifiers on an independent adult dataset.
Main Methods:
- Utilized vital signs and laboratory data from the Pediatric Intensive Care (PIC) database.
- Developed the Continuous Age-Normalized SOFA (CAN-SOFA) score for age normalization.
- Employed the XGBoost algorithm for sepsis classification and validated on MIMIC-IV adult data.
Main Results:
- The pediatric sepsis classifier achieved 0.84 accuracy and 0.867 F1-Score.
- On adult data, the classifier showed 0.80 accuracy and 0.88 F1-Score.
- Observed similar performance degradation (data drift) in both populations when tested externally.
Conclusions:
- Demonstrated the generalizability of Electronic Health Records (EHRs) between pediatric and adult populations for sepsis prediction.
- A straightforward age-normalization method enables cross-population applicability.
- This approach facilitates leveraging shared biological underpinnings for improved sepsis detection.
Background:
Though governed by the same underlying biology, the differential physiology of children causes the temporal evolution from health to a septic/diseased state to follow trajectories that are distinct from adult cases. As pediatric sepsis data sets are less readily available than for adult sepsis, we aim to leverage this shared underlying biology by normalizing pediatric physiological data such that it would be directly comparable to adult data, and then develop machine-learning (ML) based classifiers to predict the onset of sepsis in the pediatric population. We then externally validated the classifiers in an independent adult dataset.
Methods:
Vital signs and laboratory observables were obtained from the Pediatric Intensive Care (PIC) database. These data elements were normalized for age and placed on a continuous scale, termed the Continuous Age-Normalized SOFA (CAN-SOFA) score. The XGBoost algorithm was used to classify pediatric patients that are septic. We tested the trained model using adult data from the MIMIC-IV database.
Results:
On the pediatric population, the sepsis classifier has an accuracy of 0.84 and an F1-Score of 0.867. On the adult population, the sepsis classifier has an accuracy of 0.80 and an F1-score of 0.88; when tested on the adult population, the model showed similar performance degradation ("data drift") as in the pediatric population.
Conclusions:
In this work, we demonstrate that, using a straightforward age-normalization method, EHR's can be generalizable compared (at least in the context of sepsis) between the pediatric and adult populations.
