A bacteraemia risk prediction model: development and validation in an emergency medicine population.

OBJECTIVE: Design a risk model to predict bacteraemia in patients attended in emergency departments (ED) for an episode of infection. METHODS: This was a national, prospective, multicentre, observational cohort study of blood cultures (BC) collected from adult patients (≥ 18 years) attended in 71 Spanish EDs from October 1 2019 to March 31, 2020. Variables with a p value < 0.05 were introduced in the univariate analysis together with those of clinical significance. The final selection of variables for the scoring scale was made by logistic regression with selection by introduction. The results obtained were internally validated by dividing the sample in a derivation and a validation cohort. RESULTS: A total of 4,439 infectious episodes were included. Of these, 899 (20.25%) were considered as true bacteraemia. A predictive model for bacteraemia was defined with seven variables according to the Bacteraemia Prediction Model of the INFURG-SEMES group (MPB-INFURG-SEMES). The model achieved an area under the curve-receiver operating curve of 0.924 (CI 95%:0.914-0.934) in the derivation cohort, and 0.926 (CI 95%: 0.910-0.942) in the validation cohort. Patients were then split into ten risk categories, and had the following rates of risk: 0.2%(0 points), 0.4%(1 point), 0.9%(2 points), 1.8%(3 points), 4.7%(4 points), 19.1% (5 points), 39.1% (6 points), 56.8% (7 points), 71.1% (8 points), 82.7% (9 points) and 90.1% (10 points). Findings were similar in the validation cohort. The cut-off point of five points provided the best precision with a sensitivity of 95.94%, specificity of 76.28%, positive predictive value of 53.63% and negative predictive value of 98.50%. CONCLUSION: The MPB-INFURG-SEMES model may be useful for the stratification of risk of bacteraemia in adult patients with infection in EDs, together with clinical judgement and other variables independent of the process and the patient.

Video Compression for Screen Recorded Sequences Following Eye Movements.

With the advent of smartphones and tablets, video traffic on the Internet has increased enormously. With this in mind, in 2013 the High Efficiency Video Coding (HEVC) standard was released with the aim of reducing the bit rate (at the same quality) by 50% with respect to its predecessor. However, new contents with greater resolutions and requirements appear every day, making it necessary to further reduce the bit rate. Perceptual video coding has recently been recognized as a promising approach to achieving high-performance video compression and eye tracking data can be used to create and verify these models. In this paper, we present a new algorithm for the bit rate reduction of screen recorded sequences based on the visual perception of videos. An eye tracking system is used during the recording to locate the fixation point of the viewer. Then, the area around that point is encoded with the base quantization parameter (QP) value, which increases when moving away from it. The results show that up to 31.3% of the bit rate may be saved when compared with the original HEVC-encoded sequence, without a significant impact on the perceived quality.

Towards the Representation of Network Assets in Health Care Environments Using Ontologies.

OBJECTIVES: The aim of the study is to design an ontology model for the representation of assets and its features in distributed health care environments. Allow the interchange of information about these assets through the use of specific vocabularies based on the use of ontologies. METHODS: Ontologies are a formal way to represent knowledge by means of triples composed of a subject, a predicate, and an object. Given the sensitivity of network assets in health care institutions, this work by using an ontology-based representation of information complies with the FAIR principles. Federated queries to the ontology systems, allow users to obtain data from multiple sources (i.e., several hospitals belonging to the same public body). Therefore, this representation makes it possible for network administrators in health care institutions to have a clear understanding of possible threats that may emerge in the network. RESULTS: As a result of this work, the "Software Defined Networking Description Language-CUREX Asset Discovery Tool Ontology" (SDNDL-CAO) has been developed. This ontology uses the main concepts in network assets to represent the knowledge extracted from the distributed health care environments: interface, device, port, service, etc. CONCLUSION: The developed SDNDL-CAO ontology allows to represent the aforementioned knowledge about the distributed health care environments. Network administrators of these institutions will benefit as they will be able to monitor emerging threats in real-time, something critical when managing personal medical information.