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In pediatric care, understanding the nuances of hepatic drug metabolism is crucial, as it significantly differs from that of adults. This divergence is primarily due to the developmental stage of drug-metabolizing enzymes, which affects how medications are processed in the body. In neonates, for instance, the activity of Phase I enzymes—critical for the initial breakdown of drugs—is markedly reduced, functioning at just 20–40% of the levels seen in adults. This reduction poses...
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Related Experiment Video

Updated: Nov 9, 2025

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
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Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children.

Emily Kunce Stroup1, Yuan Luo2, L Nelson Sanchez-Pinto3

  • 1Driskill Graduate Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, U.S.A.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
|April 12, 2021
PubMed
Summary

Identifying distinct subgroups in pediatric Multiple Organ Dysfunction Syndrome (MODS) offers new hope for targeted treatments. This study reveals four unique patient phenotypes, paving the way for improved management strategies in critically ill children.

Keywords:
organ dysfunctionpattern clusteringpediatric critical careprecision medicineunsupervised learning

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Area of Science:

  • Pediatric critical care medicine
  • Computational biology
  • Systems medicine

Background:

  • Multiple organ dysfunction syndrome (MODS) is a leading cause of mortality in critically ill children.
  • Current management strategies and targeted therapies for pediatric MODS have shown limited success in improving outcomes.
  • Understanding the heterogeneity of MODS is crucial for developing effective treatment approaches.

Purpose of the Study:

  • To identify distinct subgroups within pediatric MODS patients.
  • To characterize the clinical heterogeneity and temporal patterns of organ dysfunction in pediatric MODS.
  • To determine if identified subgroups are predictive of clinical outcomes.

Main Methods:

  • Analysis of a large cohort (5,297 children) with MODS from two children's hospitals.
  • Application of subgraph-augmented non-negative matrix factorization (SANMF) to identify temporal patterns.
  • Characterization of patient subgroups based on clinical features and outcomes.

Main Results:

  • Identification of four novel subgroups within the pediatric MODS cohort.
  • Demonstration that these subgroups exhibit distinct clinical characteristics.
  • Validation that the identified subgroups are independently predictive of clinical outcomes.

Conclusions:

  • The four identified subgroups represent distinct phenotypes of pediatric MODS.
  • These phenotypes can aid in a better understanding of MODS heterogeneity.
  • The findings suggest potential for developing novel, targeted management strategies for pediatric MODS.