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 Medical Science sepsis research working group recommendations call for application of new research approaches in extant clinical data sets to improve efficiency of early trials of new sepsis therapies. Our objective is to apply machine learning approaches to derive computable 24-h sepsis phenotypes to facilitate personalized enrollment in early anti-inflammatory trials targeting these conditions.

Methods

We applied consensus, k-means clustering analysis to our extant PHENOtyping sepsis-indu

SUBMITTER: Qin Y 

PROVIDER: S-EPMC9077858 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature