Effect of Caring of Critically Sick Patients with COVID-19 Pneumonia at Undesignated ICU Wards on Secondary Infections

<b><i>Introduction:</i></b> COVID-19 has caused high rates of mortality. During pandemic peak, a significant number of patients were admitted to undesignated ICU areas before transferring to designated ICU, owing to unavailability of ICU beds. We aimed to record the effect of care of critically sick patients with COVID-19 on prevalence of secondary bacterial infection. <b><i>Methods:</i></b> We retrospectively studied all critically ill patients with COVID-19 pneumonia meeting ICU admission criteria who were admitted to Dubai hospital between January 1, 2020, and June 30, 2020. All the patients who transferred to wards other than designated ICU constitute category as cases. All patients who directly admitted to the designated ICU ward from emergency department constitute controls. The demographics, clinical parameters, and treatment profile of these patients were recorded and compared. Prevalence of secondary bacterial infection was calculated. <b><i>Results:</i></b> Patients with COVID-19 had high prevalence of secondary bacterial infection. Patients who stayed at undesignated ICU wards had higher occurrence of inpatient fever, hypoxemia, and they were more likely to be sedated and paralyzed than patients who stayed in designated ICU wards. Multiple logistic regression analysis showed care outside designated ICU ward does not predict increase in secondary nonviral microbial infections. <b><i>Conclusion:</i></b> Care of patients at undesignated ICU wards prior to admission to designated ICU does not impact prevalence of secondary bacterial infection.

Cognitive Impairment and Dementia Data Modelling

Recently, a huge amount of data is available for clinical research on cognitive diseases. A lot of challenges arise when data from different repositories should be integrated. Since data entities are stored with different names at different levels of granularity, a common data model is needed, providing a unified description of different factors and indicators of cognitive diseases. This paper proposes a common hierarchical data model of patients with cognitive disorders, which keeps the semantics of the data in a human-readable format and accelerates interoperability of clinical datasets. It defines data entities, their attributes and relationships related to diagnosis and treatment. The data model covers four main aspects of the patient’s profile: (1) personal profile; (2) anamnestic profile, including social status, everyday habits, and head trauma history; (3) clinical profile, describing medical investigations and assessments, comorbidities and the most likely diagnose; and (4) treatment profile with prescribed medications. It provides a native vocabulary, improving data availability, saving efforts, accelerating clinical data interoperability and standardizing data to minimize risk of rework and misunderstandings. The data model enables the application of machine learning algorithms by helping scientists to understand the semantics of information through a holistic view of patient.