Клинические инфекционные заболевания и практика

Абстрактный

Quantitative Modelling of COVID-19 Severity

Layla Khaled Rafeei, Mohamed Hameed Alrahim, Maryam Fuad Ali, Haitham Jahrami, Jameela Alsalman, Zahra Alaradi

Background: Coronavirus Disease 2019 (COVID-19), a new group of RNA viruses that appeared in Wuhan in the Republic of China in December 2019 and declared a pandemic by the World Health Organization (WHO) in March 2020. Since its emergence, it has been linked to a number of physiological factors that can help predict the severity of the illness. This study aims to explore some of these factors and their effect on the illness clinical course.

Materials and methods: This is a retrospective cross-sectional study of 416 COVID positive patients, aged between 5 months and 92 years, who were admitted to COVID facilities of the Ministry of Health of the Kingdom of Bahrain, over the period April to August 2020. Physiological factors that were studied among those patients included both vital signs and laboratory values.

Results and discussion: The study established a correlation between patients’ hemoglobin levels and their ages, pulse rates and blood pressure readings, with age being the highest influencing factor. Henceforth, a Generalized Linear Model (GLM) was established to predict patients’ hemoglobin level and thereafter the severity of their illnesses. The correlation between actual and predicted patient hemoglobin levels were found to be statistically significant with a P value of <0.05.

Conclusion: With many factors contributing to the clinical course of COVID disease, establishing a model to predict one of those factors, such as patients’ hemoglobin levels as in the index study, is critical for the understanding of the disease and hence, establishing better disease outcomes.