An explainable prognostic prediction panel for sepsis based on serum amino acid profiles

BackgroundSepsis is a life-threatening syndrome requiring aggressive management, and novel noninvasive biomarkers to enable high-confidence risk stratification of sepsis and to predict sepsis-related outcomes are urgently needed.MethodsQuantitative (liquid chromatography) mass spectrometry was used to compare the abundance of serum amino acids between patients with sepsis and healthy controls (HC) to characterize alterations in amino acid profiles associated with sepsis. In addition, multiple machine learning (ML) methods were applied to construct a prognostic prediction model for patients with sepsis.

The predictive performance was assessed, and the feature contributions were screened, followed by the development of an explainable prognostic prediction panel for sepsis.ResultsSixty participants in the HC group and 172 patients in the sepsis group (82 patients with septic shock) were included in this study. A discernible difference in the amino acid profiles between the HC and sepsis groups was detected, and the abundance of amino acids differed significantly among the HC, septic shock, and non-septic shock groups, indicating that amino acids could differentiate patients with sepsis from the HC group with good diagnostic performance.