A scoping review on pediatric sepsis prediction technologies in healthcare

Abstract

This scoping review evaluates recent advancements in data-driven technologies for predicting non-neonatal pediatric sepsis, including artificial intelligence, machine learning, and other methodologies. Of the 27 included studies, 23 (85%) were single-center investigations, and 16 (59%) used logistic regression. Notably, 20 (74%) studies used datasets with a low prevalence of sepsis-related outcomes, with area under the receiver operating characteristic scores ranging from 0.56 to 0.99. Prediction time points varied widely, and development characteristics, performance metrics, implementation outcomes, and considerations for human factors-especially workflow integration and clinical judgment-were inconsistently reported. The variations in endpoint definitions highlight the potential significance of the 2024 consensus criteria in future development. Future research should strengthen the involvement of clinical users to enhance the understanding and integration of human factors in designing and evaluating these technologies, ultimately aiming for safe and effective integration in pediatric healthcare.

Fig. 1. Frequency-weighted feature group rankings based on the top 20 extracted features.

The normalized rankings of combined feature groups used in models for pediatric a sepsis, b severe sepsis, and c septic shock, weighted by how often each feature group appeared within the three endpoints and listed from highest (top) to lowest (bottom) median value. The individual features included within the developed categorizations are available in Supplementary Data 2. The data contained in this graph is from 17 articles and 29 models reporting ranked features for pediatric sepsis, severe sepsis, and septic shock endpoints, with some models including more than one sepsis-related endpoint.

Fig. 2. Overall frequency of categorized features for pediatric sepsis, severe sepsis, and septic shock endpoints.

The frequency of each feature category is listed in descending order from highest to lowest occurrence. The individual features included within the developed categorizations are available in Supplementary Data 2. All articles with 20 or fewer features are included. For articles with more than 20 features, only the top 20 are included. Articles with a ranking for Kawasaki Disease vs Sepsis (n = 1) and articles with more than 20 non-ranked features (n = 1) are excluded from this figure. The “Biomarkers: Genes” category was only used in two articles, accounting for 100% of the model features when used, and so is only counted once for septic shock and twice for sepsis in this figure.