Background
This study aims to describe the epidemiology of COVID-19 patients in a Swiss university hospital.
Methods
This retrospective observational study included all adult patients hospitalized with a laboratory confirmed SARS-CoV-2 infection from March 1 to March 25, 2020. We extracted data from electronic health records. The primary outcome was the need to mechanical ventilation at day 14. We used multivariate logistic regression to identify risk factors for mechanical ventilation. Follow-up was of at least 14 days.
Results
145 patients were included in the multivariate model, of whom 36 (24.8%) needed mechanical ventilation at 14 days. The median time from symptoms onset to mechanical ventilation was 9·5 days (IQR 7.00, 12.75). Multivariable regression showed increased odds of mechanical ventilation with age (OR 1.09 per year, 95% CI 1.03–1.16, p = 0.002), in males (OR 6.99, 95% CI 1.68–29.03, p = 0.007), in patients who presented with a qSOFA score ≥2 (OR 7.24, 95% CI 1.64–32.03, p = 0.009), with bilateral infiltrate (OR 18.92, 3.94–98.23, p<0.001) or with a CRP of 40 mg/l or greater (OR 5.44, 1.18–25.25; p = 0.030) on admission. Patients with more than seven days of symptoms on admission had decreased odds of mechanical ventilation (0.087, 95% CI 0.02–0.38, p = 0.001).
Conclusions
This study gives some insight in the epidemiology and clinical course of patients admitted in a European tertiary hospital with SARS-CoV-2 infection. Age, male sex, high qSOFA score, CRP of 40 mg/l or greater and a bilateral radiological infiltrate could help clinicians identify patients at high risk for mechanical ventilation.
Traditionally, data used in OLAP (online analytical processing) have been limited to the contents of the data warehouse of a company. However, the needs for analysis are often more demanding and data are needed from different sources. In this article, we study how the semantics of data sources can be described to allow combining data from several sources into an OLAP cube. We apply Semantic Web technologies for defining an OWL/RDF ontology for OLAP data sources and OLAP cubes. These definitions are then utilised in OLAP cube formation by posing an OWL/RDF ontology-based query against them. We use Grid technologies to enhance the efficiency of processing and ensuring security. Our primary interest is in the cube construction (i.e., ETL process), and we assume that standard OLAP methods can be used for the actual analysis. Our tests show that the proposed approach can speed up the construction of an OLAP cube for ad hoc queries by supporting a high-level query language and reducing the amount of required data.
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