Machine Learning-Based Discovery of a Gene Expression Signature in Pediatric Acute Respiratory Distress Syndrome.

Abstract

To identify differentially expressed genes and networks from the airway cells within 72 hours of intubation of children with and without pediatric acute respiratory distress syndrome. To test the use of a neutrophil transcription reporter assay to identify immunogenic responses to airway fluid from children with and without pediatric acute respiratory distress syndrome. Prospective cohort study. Thirty-six bed academic PICU. Fifty-four immunocompetent children, 28 with pediatric acute respiratory distress syndrome, who were between 2 days to 18 years old within 72 hours of intubation for acute hypoxemic respiratory failure. None. We applied machine learning methods to a Nanostring transcriptomics on primary airway cells and a neutrophil reporter assay to discover gene networks differentiating pediatric acute respiratory distress syndrome from no pediatric acute respiratory distress syndrome. An analysis of moderate or severe pediatric acute respiratory distress syndrome versus no or mild pediatric acute respiratory distress syndrome was performed. Pathway network visualization was used to map pathways from 62 genes selected by ElasticNet associated with pediatric acute respiratory distress syndrome. The Janus kinase/signal transducer and activator of transcription pathway emerged. Support vector machine performed best for the primary airway cells and the neutrophil reporter assay using a leave-one-out cross-validation with an area under the operating curve and 95% CI of 0.75 (0.63-0.87) and 0.80 (0.70-1.0), respectively. We identified gene networks important to the pediatric acute respiratory distress syndrome airway immune response using semitargeted transcriptomics from primary airway cells and a neutrophil reporter assay. These pathways will drive mechanistic investigations into pediatric acute respiratory distress syndrome. Further studies are needed to validate our findings and to test our models.