Early Prediction of Septic Shock in Emergency Department Using Serum Metabolites

Early recognition of septic shock is crucial for improving clinical management and patient outcomes, especially in the emergency department (ED). This study conducted serum metabolomic profiling on ED patients diagnosed with septic shock (n = 32) and those without septic shock (n = 92) using a high-resolution mass spectrometer. By implementing a supervised machine learning algorithm, a prediction model based on a panel of metabolites achieved an accuracy of 87.8%. Notably, when employed on a low-resolution instrument, the model maintained its predictive performance with an accuracy of 84.2%. These results demonstrate the potential of metabolite-based algorithms to identify patients at high risk of septic shock. Our proposed workflow aims to optimize risk assessment and streamline clinical management processes in the ED, holding promise as an efficient routine test to promote timely intensive interventions and reduce septic shock mortality.