Article

Machine learning driven identification of gene-expression signatures correlated with multiple organ dysfunction trajectories and complex sub-endotypes of pediatric septic shock

2022-09-29

Abstract excerpt

<h4>Background: </h4> Multiple organ dysfunction syndrome (MODS) disproportionately drives sepsis morbidity and mortality among children. The biology of this heterogeneous syndrome is complex, dynamic, and incompletely understood. Gene expression signatures correlated with MODS trajectories may facilitate identification of molecular targets and predictive enrichment. Methods Secondary analyses of publicly availab...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: fe2da02d-1f67-5827-b8ed-a5cfb29f5d63
DOI: 10.21203/rs.3.rs-2093663/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Machine learning driven identification of gene-expression signatures correlated with multiple organ dysfunction trajectories and complex sub-endotypes of pediatric septic shockDOI 10.21203/rs.3.rs-2093663/v1Read

Machine learning-driven identification of the gene-expression signature associated with a persistent multiple organ dysfunction trajectory in critical illnessSemantic proximity Read

Derivation, validation, and transcriptomic assessment of pediatric septic shock phenotypes identified through latent profile analyses: Results from a prospective multi-center observational cohortSemantic proximity Read

Gene expression signatures identify pediatric patients with multiple organ dysfunction who require advanced life support in the intensive care unitSemantic proximity Read

Derivation, validation, and clinical relevance of a pediatric sepsis phenotype with persistent hypoxemia and shockSemantic proximity Read

External validation and biomarker assessment of a high-risk, data-driven pediatric sepsis phenotype characterized by persistent hypoxemia, encephalopathy, and shock.Semantic proximity Read

Identification and transcriptomic assessment of latent profile pediatric septic shock phenotypes.Semantic proximity Read

Biomarker Assessment of a High-Risk, Data-Driven Pediatric Sepsis Phenotype Characterized by Persistent Hypoxemia, Encephalopathy, and Shock.Semantic proximity Read

Derivation, Validation, and Clinical Relevance of a Pediatric Sepsis Phenotype With Persistent Hypoxemia, Encephalopathy, and Shock.Semantic proximity Read

Microvascular phenotypes in pediatric sepsis identified by machine learning: prognostic implications for organ dysfunction and mortality.Semantic proximity Read

Whole Blood Transcriptomics Identifies Subclasses of Pediatric Septic ShockSemantic proximity Read

Comparative immuno-biology at clinical recognition of early multiple organ dysfunction syndrome in pediatric and adult patients using single-cell transcriptomicsSemantic proximity Read

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 Hours post ICU admissionSemantic proximity Read