Sepsis, a life-threatening condition resulting from the body's extreme response to an infection, remains a significant challenge in acute care. Despite medical advancements, sepsis continues to have high morbidity and mortality rates. Consequently, sepsis research is crucial in developing effective treatments and improving patient outcomes. Researchers in this field focus on understanding the pathophysiology, early diagnosis, and innovative treatments to combat this severe condition.
Recognition of early sepsis is critical to allow timely initiation of adequate treatment: antibiotics and supportive care. We use big data to develop novel algorithms to improve early recognition of sepsis and identify which patients benefits the most from which therapy (personalized medicine) using deep learning. To facilitate this kind of research, we have set up the Acutelines data-biobank at the ED of the UMCG. The purpose of the Acutelines data-biobank is to improve prevention, recognition and treatment of acute conditions. A trained team of researchers screens all patients entering the ED, followed by data/biomaterial collection depending on broad selection criteria. In addition to demographic and medical data from the electronic patient file, we collect and store biomaterials (blood, urine, stool) for biomarker discovery, take a photograph of the face to predict deterioration using computer vision techniques and record continuous electrophysiological waveforms (i.e. ECG, PPG, EMG) to identify features predictive of deterioration. In the current project, we will focus on developing machine learning model to improve sepsis prediction by integrating biomarkers and clinical data.