Abstract
We have previously developed a bispectral electroencephalography (BSEEG) device, which was shown to be effective in detecting delirium and predicting patient outcomes. In this study we aimed to apply the BSEEG approach for a sepsis. This was a retrospective cohort study conducted at a single center. Sepsis-positive cases were identified based on retrospective chart review. EEG raw data and calculated BSEEG scores were obtained in the previous studies. The relationship between BSEEG scores and sepsis was analyzed, as well as the relationship among sepsis, BSEEG score, and mortality. Data were analyzed from 628 patients. The BSEEG score from the first encounter (1st BSEEG) showed a significant difference between patients with and without sepsis (p = 0.0062), although AUC was very small indicating that it is not suitable for detection purpose. Sepsis patients with high BSEEG scores showed the highest mortality, and non-sepsis patients with low BSEEG scores showed the lowest mortality. Mortality of non-sepsis patients with high BSEEG scores was as bad as that of sepsis patients with low BSEEG scores. Even adjusting for age, gender, comorbidity, and sepsis status, BSEEG remained a significant predictor of mortality (p = 0.008). These data are demonstrating its usefulness as a potential tool for identification of patients at high risk and management of sepsis.
Highlights
Sepsis is one of the most common causes of in-hospital death; one in three patients die from this c ondition[1, 2]
As we have previously developed a point-of-care bispectral electroencephalography (BSEEG) method, which was shown to be effective in detecting delirium and predicting patient o utcomes[13,14,15], in this study, we aimed to apply the BSEEG approach to see if the same EEG-based method can be useful for the identification of patients with sepsis at high risk for poor outcomes
365-day mortality was compared between the two groups to confirm that sepsis categorization based on our definition is reasonably accurate by showing that sepsis is associated with high mortality
Summary
Sepsis is one of the most common causes of in-hospital death; one in three patients die from this c ondition[1, 2]. The national figures closely mimic this ratio, with 270,000 deaths out of 1.7 million cases of sepsis each year[2] Given how frightening these numbers are, early detection of sepsis patients at risk for poor outcome is pivotal for prompt intervention and better outcomes, especially because a septic patient’s condition can deteriorate rapidly. As we have previously developed a point-of-care BSEEG method, which was shown to be effective in detecting delirium and predicting patient o utcomes[13,14,15], in this study, we aimed to apply the BSEEG approach to see if the same EEG-based method can be useful for the identification of patients with sepsis at high risk for poor outcomes
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
