Validation of Prognostic Clinical Risk Scores in Predicting Outcomes for Patients Diagnosed With COVID-19 During Initial Triage Assessment

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.