COVID19 Clinical Predictors and Outcome

Overview

In December 2019, a novel coronavirus (SARS-CoV-2) emerged in Wuhan, Hubei, China, and now spreads across international borders. As of 11 April 2020, the total global number of confirmed SARS-CoV-2 cases reached 1,521,252 (92,798 deaths); with 65,081 (7,978 deaths) being reported in the United Kingdom. COVID-19 is the name of the disease associated with SARS-CoV-2 infection and includes a spectrum of illness that ranges from mild infection to severe pneumonia that can progress to respiratory failure and Acute Respiratory Distress Syndrome (ARDS) or septic shock. Between 8 to 15% (depending on geographical setting) of all SARS-CoV-2 positive cases can be classified as severe or necessitating intensive care unit (ICU) admission. In the early stages of the outbreak unfolding, several retrospective case studies and cases series carried out in China reported that those who died were more likely to be male, and more likely to have underlying comorbidities. Prevalence studies conducted in the US and Italy show similar trends in the distribution of comorbidities among SARS-CoV-2 severe cases; adding obesity (BMI\>30) to the list of factors potentially associated with disease severity. However, the relative importance of different underlying health conditions remains unclear owing to inadequate adjustment for important confounding factors such as age, sex, and smoking status. We propose a cohort study to evaluate predictors, clinical evolution and excess of mortality of SARS-CoV-2 in hospitalised patients, with two main workstreams- the first looking at all patients admitted to SGHFT and the second looking at patients admitted to ITU with respiratory failure.

The majority of published reports on early clinical descriptions of COVID-19 have emerged from Hubei province in China, and although these provide valuable information, the lack of standardised mortality and morbidity ratios hinders comparison of outcome experience across populations. Also, most of the data available originate from descriptive cases or series that do not account for confounding effect; so, as yet there are no specific data on how the risks associated with underlying comorbidities might vary in different population groups or settings. Recognition of risk factors for morbidity and mortality is important to determine prevention strategies as well as to target high-risk populations for potential therapeutics. So, in this study we aim to develop a predictive statistical model to identify baseline predictors of mortality including underlying health conditions and biomarker levels at admission to improve the understanding of the clinical evolution of patients with severe COVID-19. An approximative 20-30% of regular admissions in the ICU are with respiratory failure. A considerably higher number is expected in the current climate. Government policies have aimed to flatten the epidemic curve to ease the pressure on the NHS to ensure as many people as possible have access to the appropriate level of intensive care. However, even in the best-case scenario, the number of people requiring level 3 care in ICU may be ten times the current capacity. The excess of mortality due to SARS-CoV-2 in this population hasn't been fully assessed. It is unknown if the evolution of this disease shares common characteristics with other bacterial or viral infections. This information is invaluable in order to shape diagnostic protocols, prevent complications and design therapeutic strategies.