Machine learning-driven identification of the gene-expression signature associated with a persistent multiple organ dysfunction trajectory in critical illnessResearch in context
Mihir R. Atreya,
Shayantan Banerjee,
Andrew J. Lautz,
Matthew N. Alder,
Brian M. Varisco,
Hector R. Wong,
Jennifer A. Muszynski,
Mark W. Hall,
L. Nelson Sanchez-Pinto,
Rishikesan Kamaleswaran,
Natalie Z. Cvijanovich,
Julie C. Fitzgerald,
Scott L. Weiss,
Michael T. Bigham,
Parag N. Jain,
Adam J. Schwarz,
Riad Lutfi,
Jeffrey Nowak,
Geoffrey L. Allen,
Neal J. Thomas,
Jocelyn R. Grunwell,
Torrey Baines,
Michael Quasney,
Bereketeab Haileselassie,
Chris J. Lindsell
Affiliations
Mihir R. Atreya
Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, Cincinnati, 45229, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA; Corresponding author. Cincinnati Children's Hospital Medical Center, Division of Critical Care Medicine, MLC2005, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.
Shayantan Banerjee
Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600 036, India
Andrew J. Lautz
Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, Cincinnati, 45229, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA
Matthew N. Alder
Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, Cincinnati, 45229, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA
Brian M. Varisco
Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, Cincinnati, 45229, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA
Hector R. Wong
Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, Cincinnati, 45229, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA
Jennifer A. Muszynski
Division of Critical Care Medicine, Nationwide Children's Hospital, Columbus, 43205, OH, USA; Department of Pediatrics, Ohio State University, Columbus, 43205, OH, USA
Mark W. Hall
Division of Critical Care Medicine, Nationwide Children's Hospital, Columbus, 43205, OH, USA; Department of Pediatrics, Ohio State University, Columbus, 43205, OH, USA
L. Nelson Sanchez-Pinto
Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, 60611, IL, USA; Department of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, 60611, IL, USA
Rishikesan Kamaleswaran
Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, 30322, GA, United States; Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, 30322, GA, United States
Summary: Background: Multiple organ dysfunction syndrome (MODS) disproportionately drives morbidity and mortality among critically ill patients. However, we lack a comprehensive understanding of its pathobiology. Identification of genes associated with a persistent MODS trajectory may shed light on underlying biology and allow for accurate prediction of those at-risk. Methods: Secondary analyses of publicly available gene-expression datasets. Supervised machine learning (ML) was used to identify a parsimonious set of genes associated with a persistent MODS trajectory in a training set of pediatric septic shock. We optimized model parameters and tested risk-prediction capabilities in independent validation and test datasets, respectively. We compared model performance relative to an established gene-set predictive of sepsis mortality. Findings: Patients with a persistent MODS trajectory had 568 differentially expressed genes and characterized by a dysregulated innate immune response. Supervised ML identified 111 genes associated with the outcome of interest on repeated cross-validation, with an AUROC of 0.87 (95% CI: 0.85–0.88) in the training set. The optimized model, limited to 20 genes, achieved AUROCs ranging from 0.74 to 0.79 in the validation and test sets to predict those with persistent MODS, regardless of host age and cause of organ dysfunction. Our classifier demonstrated reproducibility in identifying those with persistent MODS in comparison with a published gene-set predictive of sepsis mortality. Interpretation: We demonstrate the utility of supervised ML driven identification of the genes associated with persistent MODS. Pending validation in enriched cohorts with a high burden of organ dysfunction, such an approach may inform targeted delivery of interventions among at-risk patients. Funding: H.R.W.′s NIH R35GM126943 award supported the work detailed in this manuscript. Upon his death, the award was transferred to M.N.A. M.R.A., N.S.P, and R.K were supported by NIH R21GM151703. R.K. was supported by R01GM139967.
