Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children

Emily Kunce Stroup; Yuan Luo; L. Nelson Sanchez-Pinto

doi:10.1109/BIBM47256.2019.8983126

Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children

Emily Kunce Stroup, Yuan Luo, L. Nelson Sanchez-Pinto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Multiple organ dysfunction syndrome (MODS) is one of the most common causes of death in critically ill children. However, despite decades of clinical trials, there are no comprehensive approaches to the management of MODS or effective targeted therapies that have consistently improved outcomes. Better understanding the heterogeneity of MODS and characterizing subgroups of MODS patients could improve our understanding of the syndrome and help us develop new management strategies. We analyzed a cohort of 5,297 children with MODS from two children's hospitals and used subgraph-augmented non-negative matrix factorization (SANMF) to identify unique temporal patterns in organ dysfunction across four novel subgroups. We demonstrate that these subgroups are composed of patients with distinct clinical characteristics and are independently predictive of clinical outcomes. Our work suggests that these subgroups represent four relevant phenotypes of pediatric MODS that could be used to identify novel management strategies.

Original language	English (US)
Title of host publication	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Editors	Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	968-972
Number of pages	5
ISBN (Electronic)	9781728118673
DOIs	https://doi.org/10.1109/BIBM47256.2019.8983126
State	Published - Nov 2019
Event	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States Duration: Nov 18 2019 → Nov 21 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Country/Territory	United States
City	San Diego
Period	11/18/19 → 11/21/19

Keywords

organ dysfunction
pattern clustering
pediatric critical care
precision medicine
unsupervised learning

ASJC Scopus subject areas

Biochemistry
Biotechnology
Molecular Medicine
Modeling and Simulation
Health Informatics
Pharmacology (medical)
Public Health, Environmental and Occupational Health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BIBM47256.2019.8983126

Cite this

Stroup, E. K., Luo, Y., & Sanchez-Pinto, L. N. (2019). Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children. In I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 968-972). Article 8983126 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM47256.2019.8983126

Stroup, Emily Kunce ; Luo, Yuan ; Sanchez-Pinto, L. Nelson. / Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children. Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. editor / Illhoi Yoo ; Jinbo Bi ; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 968-972 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

@inproceedings{9db1953bb6a54452b575e18a2ea268ea,

title = "Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children",

abstract = "Multiple organ dysfunction syndrome (MODS) is one of the most common causes of death in critically ill children. However, despite decades of clinical trials, there are no comprehensive approaches to the management of MODS or effective targeted therapies that have consistently improved outcomes. Better understanding the heterogeneity of MODS and characterizing subgroups of MODS patients could improve our understanding of the syndrome and help us develop new management strategies. We analyzed a cohort of 5,297 children with MODS from two children's hospitals and used subgraph-augmented non-negative matrix factorization (SANMF) to identify unique temporal patterns in organ dysfunction across four novel subgroups. We demonstrate that these subgroups are composed of patients with distinct clinical characteristics and are independently predictive of clinical outcomes. Our work suggests that these subgroups represent four relevant phenotypes of pediatric MODS that could be used to identify novel management strategies.",

keywords = "organ dysfunction, pattern clustering, pediatric critical care, precision medicine, unsupervised learning",

author = "Stroup, {Emily Kunce} and Yuan Luo and Sanchez-Pinto, {L. Nelson}",

note = "Funding Information: ACKNOWLEDGMENTS The authors thank Neethi Pinto, MD of the University of Chicago for her support of this project. This research is partly supported by NIH/NICHD (R21HD096402, Sanchez-Pinto) and NIH/NLM (R21LM012618, Luo). Funding Information: The authors thank Neethi Pinto, MD of the University of Chicago for her support of this project. This research is partly supported by NIH/NICHD (R21HD096402, Sanchez-Pinto) and NIH/NLM (R21LM012618, Luo) Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 ; Conference date: 18-11-2019 Through 21-11-2019",

year = "2019",

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Stroup, EK, Luo, Y & Sanchez-Pinto, LN 2019, Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children. in I Yoo, J Bi & XT Hu (eds), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019., 8983126, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, Institute of Electrical and Electronics Engineers Inc., pp. 968-972, 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, San Diego, United States, 11/18/19. https://doi.org/10.1109/BIBM47256.2019.8983126

Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children. / Stroup, Emily Kunce; Luo, Yuan ; Sanchez-Pinto, L. Nelson.
Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. ed. / Illhoi Yoo; Jinbo Bi; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 968-972 8983126 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children

AU - Stroup, Emily Kunce

AU - Luo, Yuan

AU - Sanchez-Pinto, L. Nelson

N1 - Funding Information: ACKNOWLEDGMENTS The authors thank Neethi Pinto, MD of the University of Chicago for her support of this project. This research is partly supported by NIH/NICHD (R21HD096402, Sanchez-Pinto) and NIH/NLM (R21LM012618, Luo). Funding Information: The authors thank Neethi Pinto, MD of the University of Chicago for her support of this project. This research is partly supported by NIH/NICHD (R21HD096402, Sanchez-Pinto) and NIH/NLM (R21LM012618, Luo) Publisher Copyright: © 2019 IEEE.

PY - 2019/11

Y1 - 2019/11

N2 - Multiple organ dysfunction syndrome (MODS) is one of the most common causes of death in critically ill children. However, despite decades of clinical trials, there are no comprehensive approaches to the management of MODS or effective targeted therapies that have consistently improved outcomes. Better understanding the heterogeneity of MODS and characterizing subgroups of MODS patients could improve our understanding of the syndrome and help us develop new management strategies. We analyzed a cohort of 5,297 children with MODS from two children's hospitals and used subgraph-augmented non-negative matrix factorization (SANMF) to identify unique temporal patterns in organ dysfunction across four novel subgroups. We demonstrate that these subgroups are composed of patients with distinct clinical characteristics and are independently predictive of clinical outcomes. Our work suggests that these subgroups represent four relevant phenotypes of pediatric MODS that could be used to identify novel management strategies.

AB - Multiple organ dysfunction syndrome (MODS) is one of the most common causes of death in critically ill children. However, despite decades of clinical trials, there are no comprehensive approaches to the management of MODS or effective targeted therapies that have consistently improved outcomes. Better understanding the heterogeneity of MODS and characterizing subgroups of MODS patients could improve our understanding of the syndrome and help us develop new management strategies. We analyzed a cohort of 5,297 children with MODS from two children's hospitals and used subgraph-augmented non-negative matrix factorization (SANMF) to identify unique temporal patterns in organ dysfunction across four novel subgroups. We demonstrate that these subgroups are composed of patients with distinct clinical characteristics and are independently predictive of clinical outcomes. Our work suggests that these subgroups represent four relevant phenotypes of pediatric MODS that could be used to identify novel management strategies.

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KW - unsupervised learning

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M3 - Conference contribution

AN - SCOPUS:85084341649

T3 - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

SP - 968

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BT - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

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Stroup EK, Luo Y , Sanchez-Pinto LN. Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children. In Yoo I, Bi J, Hu XT, editors, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 968-972. 8983126. (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). doi: 10.1109/BIBM47256.2019.8983126

Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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