Tracing COVID-19 Infection Chains Within Healthcare Institutions - Another Brick in the Wall Against SARS-CoV-2.

Early anticipation of COVID-19 infection chains within hospitals is of high importance for initiating suitable measures at the right time. Infection control specialists can be supported by application systems able of consolidating and analyzing heterogeneous, up-to-now non-standardized and distributed data needed for tracking COVID-19 infections and infected patients' hospital contacts. We developed a system, Co-Surv-SmICS, assisting in infection chain detection, in an open and standards-based way to ensure reusability of the system across institutions. Data is modelled in alignment to various national modelling initiatives and consensus data definitions, queried in a standardized way by the use of OpenEHR as information modelling standard and its associated model-based query language, analyzed and interactively visualized in the application. A first version has been published and will be enhanced with further features and evaluated in detail with regard to its potentials to support specialists during their work against SARS-CoV-2.

Development and results of an integrated platform based on emerging technologies for the evaluation of risk factors, diagnosis, treatment, prognosis and geolocation in Pandemic Covid-19 by SARS-Cov-2

An explanatory longitudinal and prospective experimental cutting study was conducted with deliberate intervention, where a platform based on emerging technologies for epidemiological and clinical approach to the Covid 19 pandemic was developed in terms of risk factors, diagnosis, treatment, prognosis and geolocation. The diagnostic efficacy of Covid 19 disease was demonstrated through evaluation by this application with a sensitivity of 56% and a specificity of 95%, with a positive likelihood ratio of 12. With regard to the assessment of the risk status in the population, there was a sensitivity of 18% with a specificity of 87%. While the positive likelihood rate was 1.5. The determination of ELISA Immunoassay Antibodies (IgM and IgG) for SARS-Cov-2 was positive in 86% of confirmed cases, the determination of antigens (Rapid Tests) showed efficacy for ruling out infection in 73.3% of patients studied. RT-qPCR was the confirming method of molecular diagnosis of virus infection in 90% of confirmed cases. 86% of confirmed cases required treatment and poor prognostic factors were detected in 18.6% of patients evaluated. There were no deaths for Covid 19. The relative risk of complications attributable to this coronavirus was twice as high before evaluation through the Emerging Technologies-based platform. 100% of the patients included were geolocated. The platform showed efficiency and effectiveness for the integral management of the Covid 19 Pandemic.

Biomarkers of severe COVID-19 pneumonia on admission using data-mining powered by common laboratory blood tests-datasets.

In the epidemiological COVID-19 research, artificial intelligence is a unique approach to make predictions about disease severity to manage COVID-19 patients. A limitation of artificial intelligence is, however, the high risk of bias. We investigated the skill of data mining and machine learning, two advanced forms of artificial intelligence, to predict severe COVID-19 pneumonia based on routine laboratory tests. A sample of 4009 COVID-19 patients was divided into Severe (PaO2< 60 mmHg, 489 cases) and Non-Severe (PaO2 ≥ 60 mmHg, 3520 cases) groups according to blood hypoxemia on admission and their laboratory datasets analyzed by the R software and WEKA workbench. After curation, data were processed for the selection of the most influential features including hemogram, pCO2, blood acid-base balance, prothrombin time, inflammation biomarkers, and glucose. The best fit of variables was successfully confirmed by either the Multilayer Perceptron, a feedforward neural network algorithm that performed machine recognition of severe COVID-19 with 96.5% precision, or by the C4.5 software, a supervised learning algorithm based on an objective-predefined variable (severity) that generated a decision tree with 89.4% precision. Finally, a complex bivariate Pearson's correlation matrix combined with advanced hierarchical clustering (dendrograms) were conducted for knowledge discovery. The hidden structure of the datasets revealed shift patterns related to the development of COVID-19-induced pneumonia that involved the lymphocyte-to-C-reactive protein and leukocyte-to-C-protein ratios, neutrophil %, pH and pCO2. The data mining approaches to the hematological fluctuations associated with severe COVID-19 pneumonia could not only anticipate adverse clinical outcomes, but also reveal putative therapeutic targets.