PR001985 (Project)

Description:Septic shock is a life-threatening condition that arises from a dysregulated host response to infection, resulting in the failure of multiple organ systems and the rapid decline of the patient’s health status. As a medical emergency, early recognition and timely interventions are crucial for improving patient outcomes, particularly in the emergency department setting where septic patients are initially treated. Despite the importance of early identification, current diagnostic tools remain challenges in predicting septic shock at the time of admission. To address these limitations, this study aimed to develop a highly sensitive and specific platform for predicting septic shock in the emergency department using machine learning techniques applied to the metabolic profile. This approach has the potential to optimize the clinical management of septic shock.