ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2019
Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Longitudinal clustering of Life's Essential 8 health metrics: application of a novel unsupervised learning method in the CARDIA study.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Clinical Sepsis Phenotypes in Critically Ill Patients.Microorganisms · 2023Review
- Unsupervised phenotyping of sepsis using nonnegative matrix factorization of temporal trends from a multivariate panel of physiological measurements.BMC medical informatics and decision making · 2021Article
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Authors and funding
3 authors.
Funding
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.
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Registered trials
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