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

Background 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 available datasets. (1) Supervised machine learning (ML) was used to identify genes correlated with persistent MODS relative to those without in the derivation cohort. Model performances were tested across 4 validation cohorts, among children and adults with differing inciting cause for organ dysfunctions, to identify a stable set of genes and fixed classification model to reliably estimate the risk of MODS. Clinical propensity scores, where available, were used to enhance model performance. (2) We identified organ-specific dysfunction signatures by eliminating redundancies between the shared MODS signature and those of individual organ dysfunctions. (3) Finally, novel patient subclasses were identified through unsupervised hierarchical clustering of genes correlated with persistent MODS and compared with previously established pediatric septic shock endotypes. Results 568 genes were differentially expressed, among which ML identified 109 genes that were consistently correlated with persistent MODS. The AUROC of a model that incorporated the stable features chosen from repeated cross-validation experiments to estimate risk of MODS was 0.87 (95% CI: 0.85–0.88). Model performance using the top 20 genes and an ExtraTree classification model yielded AUROCs ranging 0.77–0.96 among validation cohorts. Genes correlated with day 3 and 7 cardiovascular, respiratory, and renal dysfunctions were identified. Finally, the top 50 genes were used to discover four novel subclasses, of which patients belonging to M1 and M2 had the worst clinical outcomes. Reactome pathway analyses revealed a potential role of transcription factor RUNX1 in distinguishing subclasses. Interaction with receipt of adjuvant steroids suggested that newly derived M1 and M2 endotypes were biologically distinct relative to established endotypes. Conclusions Our data suggest the existence of complex sub-endotypes among children with septic shock wherein overlapping biological pathways may be linked to differential response to therapies. Future studies in cohorts enriched for patients with MODS may facilitate discovery and development of disease modifying therapies for subsets of critically ill children with sepsis.