Background:

Since December 2019, when severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)
causing COVID -19 disease emerged in Wuhan city and on 11 March 2020 rapidly spread into
the rest of the world including UAE as a pandemic. COVID-19 continues to be a global
health threat with a massive burden on health care systems resulting in more than six
million deaths in 188 countries (1).

COVID-19 infection is characterized by a wide clinical spectrum of disease severity
ranging from asymptomatic illness to severe disease that may progress to life-threatening
complications such as shock and acute respiratory distress syndrome (2). Thus, having
clinically applicable prognostic tools for early identification of symptomatic patients
at high risk of progression to severe / critical illness is essential to guide allocating
limited healthcare resources (3). So far, clinical prognostic tools have focused on host
factors, but more recent data indicated a significant association between SARS-CoV-2
variants and the development of complications such as long COVID (4).

Currently, the clinical assessment for patients with COVID-19 infection is based on
patient's age, number of comorbidities, subjective symptoms, and extent of pulmonary
infiltrate on radiological examination which makes early prediction of severe / critical
illness rather difficult (5-7). A recently published prognostic prediction tools (ALA &
ALKA) were proposed to aid triaging patients with COVID-19 infection on initial diagnosis
(8). These prediction tools are based on simple readily available laboratory tests and
therefore may offer a clear advantage over other tools to guide discharge and admission
decisions in triage assessment centers Nevertheless, external validation of these simple
tools using another cohort of patients would provide a stronger evidence to support their
utility in triaging patients on initial diagnosis. In addition, it will also allow
further optimization of these tools to improve their utility as clinical decision support
tools to triage patients on initial diagnosis. Patients deemed to be high risk based on
these predictive tools could be triaged to hospital admission where intensive care unit
(ICU) is available in anticipation of worse outcome. Therefore, these patients may
benefit from earlier initiation of the required level of care and support including
specific therapy.

The aim of this study is to validate and compare the ALA & ALKA prediction tools with the
currently clinical risk assessment scoring system proposed for initial evaluation of
patients with COVID-19 infection.

Methodology:

An observational longitudinal follow up of all consecutive patients with positive
SARS-CoV-2 testing on nasopharyngeal swabs per WHO definitions presenting to the
emergency department . Furthermore, population level SARS-CoV-2 RNA sequence data will
also be examined and correlated with COVID-19 severity and the performance of prediction
tools.

Data will be abstracted from electronic medical records using a data collection tool.
This includes demographics, clinical manifestation, number of comorbidities, initial
laboratory and radiological examination results and their final outcomes as detailed
below.

The risk assessment score at initial presentation will be calculated for each patient
using clinical assessment scoring of ALA & ALKA and compared with the currently proposed
clinical risk assessment scoring system

The utility of the risk score in triaging patients on their initial visits to emergency
department (ED) will be validated against the following measured outcomes:

1. Hospital admission on the first encounter to ED

2. Admission to ICU for the duration of the COVID-19 hospitalization

3. In hospital and out of hospital mortality

4. Return to ED following initial discharge (within the current covid illness period,
Maximum 30 days from the initial diagnosis)

Sample Collection Process:

Data will be abstracted from electronic medical records using a data collection tool. The
data would include demographics, clinical manifestation, comorbidities, laboratory and
radiological results, and final outcomes.

The assessment risk score at initial presentation will be calculated using a free
web-based online calculator.

Data Handling & Analysis:

Descriptive statistics will be generated for all variables. Multivariate logistic
regression models to fit for outcomes. Variables incorporated in the COVID-19 risk of
score will be included in the regression analysis to predict the outcomes. Multivariate
logistic regression results will be presented in terms of adjusted Odds Ratios with
corresponding 95% confidence intervals and p-values.

Discrimination will be evaluated using C-Statistic, along with its corresponding 95%
Confidence Intervals and Receiver Operating Characteristic (ROC) curve. C-Statistics ≥
0.7 will be considered good and ≥ 0.8 will be considered excellent (9). Calibration will
be assessed based on the predicted probability for the outcome as predicted from the
regressions. Calibration curves will be generated. P-values <0.05 is considered
statistically significant. All analysis will be performed using SPSS software (version
28, IBM Corp, NY, USA).