Continuous Detection of Stimulus Brightness Differences Using Visual Evoked Potentials in Healthy Volunteers with Closed Eyes.

Background/Objectives: We defined the value of a machine learning algorithm to distinguish between the EEG response to no light or any light stimulations, and between light stimulations with different brightnesses in awake volunteers with closed eyelids. This new method utilizing EEG analysis is visionary in the understanding of visual signal processing and will facilitate the deepening of our knowledge concerning anesthetic research. Methods: X-gradient boosting models were used to classify the cortical response to visual stimulation (no light vs. light stimulations and two lights with different brightnesses). For each of the two classifications, three scenarios were tested: training and prediction in all participants (all), training and prediction in one participant (individual), and training across all but one participant with prediction performed in the participant left out (one out). Results: Ninety-four Caucasian adults were included. The machine learning algorithm had a very high predictive value and accuracy in differentiating between no light and any light stimulations (AUCROCall: 0.96; accuracyall: 0.94; AUCROCindividual: 0.96 ± 0.05, accuracyindividual: 0.94 ± 0.05; AUCROConeout: 0.98 ± 0.04; accuracyoneout: 0.96 ± 0.04). The machine learning algorithm was highly predictive and accurate in distinguishing between light stimulations with different brightnesses (AUCROCall: 0.97; accuracyall: 0.91; AUCROCindividual: 0.98 ± 0.04, accuracyindividual: 0.96 ± 0.04; AUCROConeout: 0.96 ± 0.05; accuracyoneout: 0.93 ± 0.06). The predictive value and accuracy of both classification tasks was comparable between males and females. Conclusions: Machine learning algorithms could almost continuously and reliably differentiate between the cortical EEG responses to no light or light stimulations using visual evoked potentials in awake female and male volunteers with eyes closed. Our findings may open new possibilities for the use of visual evoked potentials in the clinical and intraoperative setting.

Emergency critical care: closing the gap between onset of critical illness and intensive care unit admission.

Critical illness is an exquisitely time-sensitive condition and follows a disease continuum, which always starts before admission to the intensive care unit (ICU), in the majority of cases even before hospital admission. Reflecting the common practice in many healthcare systems that critical care is mainly provided in the confined areas of an ICU, any delay in ICU admission of critically ill patients is associated with increased morbidity and mortality. However, if appropriate critical care interventions are provided before ICU admission, this association is not observed. Emergency critical care refers to critical care provided outside of the ICU. It encompasses the delivery of critical care interventions to and monitoring of patients at the place and time closest to the onset of critical illness as well as during transfer to the ICU. Thus, emergency critical care covers the most time-sensitive phase of critical illness and constitutes one missing link in the chain of survival of the critically ill patient. Emergency critical care is delivered whenever and wherever critical illness occurs such as in the pre-hospital setting, before and during inter-hospital transfers of critically ill patients, in the emergency department, in the operating theatres, and on hospital wards. By closing the management gap between onset of critical illness and ICU admission, emergency critical care improves patient safety and can avoid early deaths, reverse mild-to-moderate critical illness, avoid ICU admission, attenuate the severity of organ dysfunction, shorten ICU length of stay, and reduce short- and long-term mortality of critically ill patients. Future research is needed to identify effective models to implement emergency critical care systems in different healthcare systems.