A Machine Learning Model Derived From Analysis of Time-Course Gene-Expression Datasets Reveals Temporally Stable Gene Markers Predictive of Sepsis Mortality

Abstract

Sepsis is associated with significant mortality and morbidity among critically ill patients admitted to intensive care units (ICU) and represents a major health challenge globally. Given the significant clinical and biological heterogeneity among patients and the dynamic nature of the host immune response, identifying those at high risk of poor outcomes remains a critical challenge. Here, we performed secondary analysis of publicly available time-series gene-expression datasets from peripheral blood of patients admitted to the ICU to elucidate temporally stable gene expression markers between sepsis survivors and non-survivors. Using a limited set of genes that were determined to be temporally stable, we derived a dynamical model using a Support Vector Machine (SVM) classifier to accurately predict the mortality of sepsis patients. Our model had robust performance in a test dataset, where patients’ transcriptome was sampled at alternate time points, with an area under the curve (AUC) of 0.89 (95% CI: 0.82-0.96) upon 5-fold cross-validation. We also identified 7 potential biomarkers of sepsis mortality (STAT5A, CX3CR1, LCP1, SNRPG, RPS27L, LSM5, SHCBP1 that require future validation. Pending prospective testing, our model may be used to identify sepsis patients with high risk of mortality accounting for the dynamic nature of the disease and with potential therapeutic implications.

Development and Internal-External Validation of the Acci-Sofa Model for Predicting in-Hospital Mortality of Patients With Sepsis-3 in the Icu: A Multicenter Retrospective Cohort Study

“…At the genetic level, there have also been some breakthroughs in explaining different sepsis prognoses. Huang et al (23) revealed seven potential genetic biomarkers for predicting sepsis mortality through a machine learning model. Kapp et al (24) discovered information regarding molecular factors that may help explain the differences in sepsis survival outcomes among African American/Black and non-Hispanic White patients with primary urinary tract infection.…”

Integrated multi-omics and artificial intelligence to explore new neutrophils clusters and potential biomarkers in sepsis with experimental validation

“…ML has shown great promise in enhancing the prediction, diagnosis, and management of sepsis. Min Huang et al conducted the Support Vector Machine classifier and identified mortality biomarkers of sepsis (16). Moreover, another study applied a new machine learning method in predicting 20 differentially expressed genes for sepsis outcomes (17).…”