Discussion
As SARS-CoV-2 continues to evolve, the lung pathogenicity of the
emerging VOCs continues to decline3-5,15. As the
primary screening test for pneumonia, CT scans play an essential role in
the early stages of the epidemic of SARS-CoV-218-19.
However, with the reduced lung pathogenicity of the new mutant strain, a
system is required to evaluate the risk of pneumonia in recently
infected individuals to ensure the effective use of healthcare resources
and minimize unnecessary exposure to electromagnetic radiation.
The present study was designed to develop a model for pneumonia risk
prediction in patients with SRAS-CoV-2 infection, for classifying the
risk of pneumonia in SARS-CoV-2 infected patients, to provide
clinicians with an appropriate reference for selecting CT scans by
predicting the risk of pneumonia in subjects before chest CT scans, to
reduce non-essential medical ionizing radiation and reduce the financial
burden on patients.
The pneumonia risk model constructed in this study shows good
discrimination, calibration, and clinical validity. In addition, the
predictors used in the model are ”age” and ”blood routine indicators”,
which are very common, readily available, and inexpensive. This provides
a reasonable basis for promoting the use of the model.
To reduce irrelevant and redundant information, we used both ”univariate
and multivariate logistic regression” and ”Lasso regression” to screen
for predictor variables; the variables selected for both options were
taken as the final predictors, namely: age, InCRP, %Mon.
Previous reports have shown that the severity and fatality rates of
COVID-19 significantly vary with age group, and they rise sharply in the
elderly20-22; this supports the age predictor’s
inclusion in the pneumonia risk prediction model.
As a general indicator of inflammation, CRP is associated with the
clinical severity of COVID-1922-24. CRP may indicate
COVID-19 changes earlier than chest CT — CRP was significantly
elevated before CT findings in severe COVID-19
patients25.
In our study, %Mon was partially associated with the risk of pneumonia,
which is in accord with recent studies26. Monocytes
are innate immune system cells that participate in several immune
function events, including phagocytosis, antigen presentation, and
inflammatory responses27; circulating monocytes
extravasate into peripheral tissues during sterile and non-sterile
inflammation and undergo differentiation into macrophages or dendritic
cells. A previous review article discussed the buildup of
monocyte/macrophage cells in the lungs. These cells are likely sources
of the proinflammatory cytokines and chemokines linked to deadly
diseases brought on by human coronavirus infections, such as
COVID-1928, suggesting that the migration of monocytes
into lung tissue may be the cause of the monocyte reduction in
peripheral blood.
In previous related studies, additional factors such as cardiovascular
disease, chronic respiratory disease, diabetes, obesity, hypertension,
and high serum ferritin levels, were found to be associated with the
progression of COVID-1929-31. Since our study was
retrospective, it is limited by missing information, and some of the
valuable indicators reported by related studies were not included in
this study. In addition, some of the indicators were not included in our
study because they were derived from patients’ complaints rather than
standard medical diagnoses and had low credibility.
From the standpoint of model promotion, the more streamlined model
predictions are less expensive, easier to use, and more suited to wider
use, but they also result in a decline in model prediction performance.
This is a matter of balance, depending on the application scenario of
the model being constructed: whether it should be applied primarily for
primary screening of high-risk cases or whether it prefers higher
predictive accuracy.
In our study, the pneumonia risk prediction model we constructed was
mainly applied to the primary screening of people at high risk of
pneumonia in SARS-CoV-2 infected individuals, so we chose a more
streamlined modeling strategy.
One unexpected finding was that the model performed better in the
validation cohort than in the training cohort. This result may be
explained by the relatively small sample size of the validation cohort
and a certain degree of homology with the training cohort.
Limitations of this study
Our study has several limitations.
First, despite applying the inclusion criteria strictly, we could not
completely rule out cases with potential lesions in body parts other
than the lungs at study entry from influencing the predictors. This
created some confusion during the model’s development and some
difficulty in evaluating its predictive performance.
Second, even though external validation was carried out, the cohort for
it came from just one center, and the sample size was somewhat tiny.
In later research, a larger sample size would be required to calibrate
and validate the model in a multicenter population.