Early Sepsis Detection with Deep Learning on EHR Event Sequences

Background: Sepsis is a clinical condition involving an extreme inflammatory response to an infection, and is associated with high morbidity and mortality. Without intervention, this response can progress to septic shock, organ failure and death. Every hour that treatment is delayed mortality increases. Early identification of sepsis is therefore important for a positive outcome. Methods: We constructed predictive models for sepsis detection and performed a register-based cohort study on patients from four Danish municipalities. We used event-sequences of raw electronic health record (EHR) data from 2013 to 2017, where each event consists of three elements: a timestamp, an event category (e.g. medication code), and a value. In total, we consider 25.622 positive (SIRS criteria) sequences and 25.622 negative sequences with a total of 112 million events distributed across 64 different hospital units. The number of potential predictor variables in raw EHR data easily exceeds 10.000 and can be challenging for predictive modeling due to this large volume of sparse, heterogeneous events. Traditional approaches have dealt with this complexity by curating a limited number of variables of importance; a labor-intensive process that may discard a vast majority of information. In contrast, we consider a deep learning system constructed as a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) network. Importantly, our system learns representations of the key factors and interactions from the raw event sequence data itself. Results: Our model predicts sepsis with an AUROC score of 0.8678, at 11 hours before actual treatment was started, outperforming all currently deployed approaches. At other prediction times, the model yields following AUROC scores. 15 min: 0.9058, 3 hours: 0.8803, 24 hours: 0.8073. Conclusion: We have presented a novel approach for early detection of sepsis that has more true positives and fewer false negatives than existing alarm systems without introducing domain knowledge into the model. Importantly, the model does not require changes in the daily workflow of healthcare professionals at hospitals, as the model is based on data that is routinely captured in the EHR. This also enables real-time prediction, as healthcare professionals enters the raw events in the EHR

Dermatan Sulfate Epimerase 1-Deficient Mice Have Reduced Content and Changed Distribution of Iduronic Acids in Dermatan Sulfate and an Altered Collagen Structure in Skin▿

Dermatan sulfate epimerase 1 (DS-epi1) and DS-epi2 convert glucuronic acid to iduronic acid in chondroitin/dermatan sulfate biosynthesis. Here we report on the generation of DS-epi1-null mice and the resulting alterations in the chondroitin/dermatan polysaccharide chains. The numbers of long blocks of adjacent iduronic acids are greatly decreased in skin decorin and biglycan chondroitin/dermatan sulfate, along with a parallel decrease in iduronic-2-O-sulfated-galactosamine-4-O-sulfated structures. Both iduronic acid blocks and iduronic acids surrounded by glucuronic acids are also decreased in versican-derived chains. DS-epi1-deficient mice are smaller than their wild-type littermates but otherwise have no gross macroscopic alterations. The lack of DS-epi1 affects the chondroitin/dermatan sulfate in many proteoglycans, and the consequences for skin collagen structure were initially analyzed. We found that the skin collagen architecture was altered, and electron microscopy showed that the DS-epi1-null fibrils have a larger diameter than the wild-type fibrils. The altered chondroitin/dermatan sulfate chains carried by decorin in skin are likely to affect collagen fibril formation and reduce the tensile strength of DS-epi1-null skin