Machine learning derivation of four computable 24-h pediatric sepsis phenotypes to facilitate enrollment in early personalized anti-inflammatory clinical trials

Machine learning derivation of four computable 24-h pediatric sepsis phenotypes to facilitate enrollment in early personalized anti-inflammatory clinical trials

Abstract Background Thrombotic microangiopathy-induced thrombocytopenia-associated multiple organ failure and hyperinflammatory macrophage activation syndrome are important causes of late pediatric sepsis mortality that are often missed or have delayed diagnosis. The National Institutes of General M...

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Bibliographic Details
Main Authors: Yidi Qin, Kate F. Kernan, Zhenjiang Fan, Hyun-Jung Park, Soyeon Kim, Scott W. Canna, John A. Kellum, Robert A. Berg, David Wessel, Murray M. Pollack, Kathleen Meert, Mark Hall, Christopher Newth, John C. Lin, Allan Doctor, Tom Shanley, Tim Cornell, Rick E. Harrison, Athena F. Zuppa, Russell Banks, Ron W. Reeder, Richard Holubkov, Daniel A. Notterman, J. Michael Dean, Joseph A. Carcillo
Format: Article
Language:English
Published: BMC 2022-05-01
Series:Critical Care
Subjects:
Severe sepsis
Multiple organ failure
Immunoparalysis-associated multiple organ failure
Thrombocytopenia-associated multiple organ failure
Macrophage activation syndrome
Sequential multiple organ failure
Online Access:https://doi.org/10.1186/s13054-022-03977-3

Internet

https://doi.org/10.1186/s13054-022-03977-3

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