Construction of the Metabolomics-Based Prognosis-Prediction Models for ICU Septic Patients

Abstract Background: Global mortality related to sepsis remains unacceptably high in intensive care units (ICUs). Accurate prognostic evaluation of sepsis could effectively reduce the mortality of septic patients. Our goal is to present an effective and rapid method to assess the prognosis of sepsis.Methods: We included 96 septic patients according to the sepsis 3.0 in ICU, who were grouped into survival and death groups according to 28-day, hospital, and 90-day prognosis. Liquid chromatography/mass spectrometry was performed to detect the metabolite changes in plasma. Multivariate logistic regression models, using differential metabolites and clinical indicators within 24 h after diagnosis of sepsis, were used to construct the prediction models for 28-day, hospital, and 90-day prognosis in sepsis.Results: Metabolic profiles related to 28-day, hospital, and 90-day prognosis were significantly different between the survival and the death group. Specifically, 13, 4, and 29 primary differential metabolites related to amino acid metabolism and fatty acid metabolism were identified between the survival and death group at 28-day, hospital, and 90-day prognosis, respectively. Further, we found that model 1 including indoleacetic acid, 3-methylene-indolenine, heart rate, respiratory support, and application of pressure drugs; model 2 including lymphocyte count, alkaline phosphatase, SOFA, and L-alpha-amino-1H-pyrrole-1-hexanoic acid; model 3 including pyrrolidine, dopamine, heart rate, respiratory support, and application of pressure drugs, could predict 28-day, hospital, and 90-day prognosis of sepsis with a sensitivity of 75.51%, 73.58%, and 83.33%, specificity of 78.72%, 72.09%, and 76.19%, the area under the receiver operating characteristics curve of 0.881, 0.830, 0.892, respectively.Conclusions: This research could be used to predict the 28-day, hospital, and 90-day prognosis of septic patients based on differential metabolites and clinical parameters, and could also be used to develop novel sepsis-treatment methods.