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Updated: Aug 9, 2025
A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
Refining empiric subgroups of pediatric sepsis using machine-learning techniques on observational data
Yidi Qin1, Rebecca I Caldino Bohn1, Aditya Sriram1
1Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States.
Insights
Pediatric sepsis requires personalized treatment. This review explores empiric and machine-learning phenotypes to improve precision medicine approaches, highlighting challenges in capturing sepsis heterogeneity for better outcomes.
Area of Science:
- Critical care medicine
- Pediatric infectious diseases
- Computational biology
Background:
- Sepsis causes 20% of global deaths, with 3 million child deaths annually.
- Current pediatric sepsis management often uses a "one-size-fits-all" approach, limiting treatment efficacy.
- Precision medicine offers a promising avenue to tailor treatments for individual pediatric sepsis patients.
Purpose of the Study:
- To review and compare empiric and machine-learning-based phenotyping strategies for pediatric sepsis.
- To highlight the complexities of pediatric sepsis pathobiology and heterogeneity.
- To identify methodological steps and challenges in developing precise pediatric sepsis phenotypes.
Main Methods:
- Literature review of existing phenotyping strategies in pediatric sepsis.
- Analysis of multifaceted data underlying sepsis pathobiology.
- Comparison of empiric versus machine-learning-based phenotyping approaches.
Main Results:
- Empiric and machine-learning phenotypes aid in accelerating diagnosis and treatment.
- Neither current phenotyping strategy fully captures the heterogeneity of pediatric sepsis.
- Significant challenges remain in accurately delineating pediatric sepsis phenotypes.
Conclusions:
- Advancing precision medicine in pediatric sepsis necessitates improved phenotyping strategies.
- Further research is needed to overcome challenges in capturing sepsis heterogeneity.
- Accurate phenotyping is crucial for optimizing clinical outcomes in pediatric sepsis.
Abstract:
Sepsis contributes to 1 of every 5 deaths globally with 3 million per year occurring in children. To improve clinical outcomes in pediatric sepsis, it is critical to avoid "one-size-fits-all" approaches and to employ a precision medicine approach. To advance a precision medicine approach to pediatric sepsis treatments, this review provides a summary of two phenotyping strategies, empiric and machine-learning-based phenotyping based on multifaceted data underlying the complex pediatric sepsis pathobiology. Although empiric and machine-learning-based phenotypes help clinicians accelerate the diagnosis and treatments, neither empiric nor machine-learning-based phenotypes fully encapsulate all aspects of pediatric sepsis heterogeneity. To facilitate accurate delineations of pediatric sepsis phenotypes for precision medicine approach, methodological steps and challenges are further highlighted.
