A new tool for predicting COVID-19 severity and prognosis


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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, emerged in December 2019 in Wuhan, China and is the cause of the coronavirus disease (COVID-19). The virus has overwhelmed healthcare systems and at times put the world on pause as we reflect and find new ways to battle the pandemic.

Currently there seems to be a significant association with COVID-19 and anxiety. The reason being that we do not know how the disease will progress in terms of severity. Several factors, besides clinical risk factors, play a role in disease severity including: diverse immunological, metabolic and muscle abnormalities. As COVID-19 clinically presents in many different ways, there has been a lack of a comprehensive prognostic and explanatory model, all seemingly limited by their use of limited immunological variables, with clinical, radiographic and laboratory features, without the incorporation of a comprehensive assessment of the wide COVID-19 spectrum of abnormalities. In this present study researchers from Mexico, Torres-Ruiz, et al., created and validated a compound explanatory model including diverse clinical, immunological, metabolomic, and muscle atrophy variables to classify COVID-19 patients according to their disease severity and to predict adverse outcomes. In short, their aim was to investigate novel biological features with the hope of explaining COVID-19 severity and prognosis (death and disease progression).

Of the 121 patients recruited, they were divided into groups according to their disease severity as follows:

  1. Mild/moderate disease: Fever, upper respiratory infection symptoms, with or without pneumonia.
  1. Severe: Any of the following: respiratory failure, respiratory rate ≥ 30 breaths per minute, oxygen saturation at rest ≤ 93%, PaO2/FiO2 ≤ 300 mmHg.
  1. Critical: Any of the following: requirement of invasive mechanical ventilation (IMV), shock, multiple organ failure.

The researchers showed that a model comprising Body Mass Index (BMI), hemoglobin, albumin, 3-Hydroxyisovaleric acid, absolute numbers of effector memory CD8+ T cells, T-Helper (Th)-1, Low density granulocytes (LDG)s and the serum/plasma concentrations of monocyte chemoattractant protein 1 (MCP-1), TRIM63, and Neutrophil Extracellular Traps (NETs) is a useful tool for the stratification of patients according to their disease severity. Overall their model displayed good discriminatory capacity and unveiled explanatory features for COVID-19 disease severity as summarised in Figure 1. In addition, inclusive of a distinct metabolic profile, the model may be used as good predictor for disease progression in COVID-19. The model also highlighted novel immunological and metabolomic features associated with COVID-19 severity and prognosis, illustrating interaction between innate and adaptive immunity, inflammation-induced muscle atrophy and hypoxia as the main drivers of COVID-19 severity. This may be the first model that includes clinical features in addition to biological variables that represent varying abnormalities, varying clinical presentations and pathophysiology’s related to disease severity in COVID-19. It must be said that this study does have its limitations including: sample size, genetic background of patients, viral load and viral genotype (associated with differences in disease severity).

Figure 1: A summary as taken directly from the article: Diverse clinical and biological traits are able to accurately predict COVID-19 severity. Severe/critical disease is characterized by increased BMI, LDGs, circulating NETs, plasma TRIM63, and serum 3-Hydroxyisovaleric acid, as well as diminished levels of Hb, albumin, and lymphopenia of Th1 and CD8+ effector memory T cells (Torres-Ruiz, et al., 2021).

In summary and in their own words,

“The CLICOVID-19 SRS showed the best performance to accurately stratify the COVID-19 severity in both ambulatory and hospitalized patients in an indirect comparison with other predictive indexes…After external validation in different settings, the CLICOVID-19 SRS could be a useful tool to optimize the healthcare resources allocated to manage COVID-19 by identifying subjects that could be safely managed as outpatients and those in whom hospital admission and intensive care is imperative.”

This predictive/risk model is available online as the researchers decided to construct an online risk digital calculator based on the predictive index, which is publicly available (https://rai-unam-mx.shinyapps.io/CLICOVID-19-SRS/)

 

Journal Article: Torres-Ruiz, et al., 2021. Redefining COVID-19 Severity and Prognosis: The Role of Clinical and Immunobiotypes. Frontiers in Immunology.

Summary by Stefan Botha

 
 
 
 
 
 
International Union of Immunological SocietiesUniversity of South AfricaInstitute of Infectious Disease and Molecular MedicineElizabeth Glazer Pediatric Aids FoundationStellenbosch University