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. 2023 Sep 22:16:77-81.
doi: 10.1016/j.sopen.2023.09.013. eCollection 2023 Dec.

Children are small adults (when properly normalized): Transferrable/generalizable sepsis prediction

Caitlin Marassi  1 , Damien Socia  1 , Dale Larie  1 , Gary An  1 , R Chase Cockrell  1
Affiliations

Children are small adults (when properly normalized): Transferrable/generalizable sepsis prediction

Caitlin Marassi et al. Surg Open Sci. .

Abstract

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.

Keywords: Machine learning; Pediatric sepsis; Sepsis.

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Conflict of interest statement

The authors report no proprietary or commercial interest in any product mentioned or concept discussed in this article.

Figures

Fig. 1
Fig. 1
Explanatory diagram of the XGBoost algorithm. Each node of the decision tree contains a mathematical operation, or ‘decision,’ labeled DXy, in which X represents the sequence in which the decision is operated upon and y represents an individual tree model in the ensemble. Results are indicated by the A's.
Fig. 2
Fig. 2
Panel on Left: Confusion matrix showing performance of model on internal validation on PIC (Accuracy 0.84). Panel on Right: Confusion matrix showing performance of external validation of model trained on pediatric data on adult data/MIMIC (Accuracy 0.80).
See this image and copyright information in PMC

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