DISCUSSION
We have evaluated multiple regression analysis for mathematically
modeling the course of COVID-19 to predict clinical outcome. The premise
of this model is that quantitatively measured clinical and laboratory
parameters involved in the pathogenesis of disease progression can be
mathematically mapped to a multiple-regression model. COVID-19 is
initiated by infection of the subject with SARS-CoV-2 with subsequent
replication in the epithelial cells of the lung. The factors that
contribute to the viral load include number of cells that express the
ACE2 and other receptors, and inflammatory cytokines. Comorbidities
contribute towards a more serious disease progression. Virus infection
of antigen presenting cells, such as dendritic cells, macrophages, and
other cell types including endothelial cells, results in activation of
biochemical signals, which lead to secretion of a battery of cytokines
that include IL1β and IL-6. The viral infection as well as inflammatory
cytokines cause fever and an increase in serum inflammatory factors such
as D-Dimer and Ferritin. Induction of an inflammatory response
contributes to reduction of the total numbers of lymphocytes from
circulation. The inflammation results in a loss of lung function (e.g.,
reduction in blood-oxygen levels), cardiac function (blood pressure) and
can culminate in multi-organ failure.
Subjects with a normal immune response can generally mount an adequate
innate and adaptive response to the virus. These individuals clear the
virus by generating adaptive T cell responses and neutralizing
antibodies. Subjects with comorbid conditions can have compromised
immune function which could result in dysfunctional activation of
inflammatory responses, leading to worse clinical outcomes.
Selection of the parameters that were included in the model building
process was influenced by their perceived significance from current
research reports. This list of factors is by no means complete and it is
expected that in due course a more comprehensive list will emerge. This
report provides a basis for creating a tool, independent of the number
and type of parameters, that could find utility in predicting the
disease outcome using those parameters.
Viral Load. Association of viral load and progression of
diseases has been reported for several viral infections [42-44].
Viral load in COVID-19 is measured by qRT-PCR of SARS-CoV-2 using
primers for the spike gene [43]. The correlation of high viral load
with severity of disease progression has been extensively demonstrated.
The systemic dissemination of the virus has been associated with
expression of the ACE2 receptor on endothelial cells [21]].
Comorbid conditions could enhance the expression of receptors and enable
distribution of virus, thereby enhancing the viral load, which can
result in progression of disease.
IFNα. The critical role of Type I interferons in innate and
adaptive immunity, leading to both protective and pathogenic responses,
has been reported in the case of several viral and bacterial infections
[45]. SARS-CoV-2 infection has been shown to result in a diverse
range of effects on Type I immune responses. Most patients elicit a
strong IFNα response along with a battery of inflammatory cytokines,
some of which progress to a cytokine storm [46, 47]. Specific
blocking of the type I mediated signal transduction by various proteins
of SARS-CoV-2 has been demonstrated [48]. A remarkably high
proportion of male subjects experiencing severe or critical COVID-19
disease expressed an inability to produce sufficient levels of IFNα due
to various types of errors in the IFN genes. Curiously, majority of the
male subjects possessed circulating IFNα autoantibodies that had the
ability to neutralize the endogenously produced cytokine, thereby
effectively reducing the available IFNα. The discovery of these two
mechanisms for lowering IFNα levels underscores its relevance in
controlling the progression of disease in individuals infected with the
SARS-CoV-2 [49].
D-Dimer. D-Dimer is routinely measured in clinical situations
because its levels correlate with serious underlying conditions
including venous thromboembolism, cancer and sepsis [48]. In the
case of COVID-19 patients, introduction of the virus brings about
infection-induced inflammatory alterations leading to coagulopathy.
Lungs being the target of SARS-CoV-2, acute injury to the lung as well
as multi organ failure have been caused by the virus-induced cascade of
the inflammatory pathway. In an early study on 41 COVID-19 patients,
those with severe disease had higher levels of D-Dimer along with high
levels of IL-8, TNFα and IL-2R [31]. Male patients were found to
have higher levels of IL-6, IL-2R, Ferritin and other markers of
inflammation compared to female. High levels of IL-6 showed a
statistically significant correlation with severe disease in a
retrospective study as well [27]. One can hypothesize that such
patients would likely benefit from anticoagulation therapy.
Ferritin. A high level of ferritin, measure of stored iron, was
found to be associated with severe disease in COVID-19 patients and was
linked to high fatality rates in a 72 patient prospective study [33,
50, 51]. In another study on 39 patients, those with mild COVID-19
symptoms had lower levels of ferritin while those with moderate or
severe symptoms expressed higher levels of ferritin [50].
Lymphopenia. Loss of lymphocytes after viral infections has
been associated with severe disease. The mechanisms involved in
lymphodepletion can been implicated to be due to cell death, cytokine
storm and/or redistribution of lymphocyte populations [3, 33, 37].
In this model, we have utilized lymphopenia as a measure of severity of
disease progression. Loss of immune function could result in several
potential mechanisms of pathogenesis including autoimmunity,
hyperactivation, increased susceptibility to infections and organ
dysfunction.
Neutralizing Antibodies. Induction of neutralizing antibodies
directed to the receptor-binding domain of the spike protein is critical
for restricting entry of the virus into the cells and has been one of
the central tenets of a protective immune response. In this model, we
have used a range of IgG titers to spike protein for the simulated data
set [52]. However, the role of neutralizing antibodies induced in a
large proportion of subjects following natural infection is still being
studied [53]. Some subjects do not elicit strong antibody responses.
Sub-optimal levels of antibodies may catalyze generation of virus
mutants [54]. Neutralizing antibodies to the virus have generally
not correlated with reduced severity of disease in the primary
infection. In addition, it will be interesting to decipher the role of
pre-existing antibodies reported recently in the modulation of disease
and its impact on vaccination regimens. Thus, the mechanisms involved in
the induction of antibodies, the repertoire and diversity of responses,
and effects on protection versus progression, remains to be clearly
established.
The predictive model can have multiple applications, such as forecasting
the percentage of the population that will progress to severe disease in
each geography, enabling logistics planning for hospital beds, health
care providers and personal-protective safety equipment. Analysis of the
coefficient of correlations of parameters with outcome of disease may
provide clues to a better understanding of the mechanism of action of
disease pathogenesis. The model can predict the probability of disease
progression at an individual level, based on parameter data, and can be
used to understand the effect and impact of therapeutic interventions.
The predictive model can be utilized to analyze large amounts of data to
develop algorithms for personalized treatment regimens.
In summary, we have developed a probabilistic model that can be utilized
to predict progression of disease following infection with SARS-CoV-2.
This model was developed using simulated data based on published levels
of COVID-19 related clinical and laboratory parameters and provides an
approach to predicting the outcome of disease. Validation of the model
will require existing data and the clinical outcomes of patients.
Prediction of disease progression can be highly valuable at an
individual as well as population level.