Pediatric Sepsis Phenotyping Using Vital Sign Trajectories

Yanyi Jenny Ding, Zhidi Luo, Mindy Szeto, Yuan Luo*, L. Nelson Sanchez-Pinto*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Sepsis can be life-threatening, which highlights the need to understand the condition's diverse phenotypes to enhance treatment effectiveness. Sepsis phenotypes are derived from 12-hour vital sign trajectories of children (N=12,824) with multiple organ dysfunction syndrome from 13 U.S. hospitals. Survival analysis of the two subgroups produced by hierarchical clustering (HAC) on pairwise trajectory similarity matrix from dynamic time warping (DTW) showed a hazards ratio of 4.7 for 30-day mortality, which was better than the stratification of subgroups from group-based trajectory modeling. The higher mortality subgroup from HAC on DTW displayed higher blood pressure and pulse but lower temperature, in addition to acidosis. This comprehensive analysis of phenotypes can greatly aid in early risk evaluation, tailored treatment approaches, and improved outcomes for pediatric sepsis patients.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
EditorsXingpeng Jiang, Haiying Wang, Reda Alhajj, Xiaohua Hu, Felix Engel, Mufti Mahmud, Nadia Pisanti, Xuefeng Cui, Hong Song
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4297-4303
Number of pages7
ISBN (Electronic)9798350337488
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 - Istanbul, Turkey
Duration: Dec 5 2023Dec 8 2023

Publication series

NameProceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Conference

Conference2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Country/TerritoryTurkey
CityIstanbul
Period12/5/2312/8/23

Keywords

  • Machine Learning
  • Mortality Prediction
  • Pediatrics
  • Sepsis
  • Time-series Analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Automotive Engineering
  • Modeling and Simulation
  • Health Informatics

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