Discussion
To control the worldwide pandemic of SARS-CoV-2 infection and
efficiently allocate medical resources, the screening and management of
asymptomatic or presymptomatic COVID-19 patients in the population are
urgent challenges. This retrospective study systematically characterized
the asymptomatic, presymptomatic, and symptomatic COVID-19 patients who
were admitted to Wuhan Huoshenshan Hospital and developed a
risk-stratification calculator that will help clinicians worldwide to
make appropriate clinical decisions at the time of admission.
Asymptomatic and presymptomatic COVID-19 patients have similar CT
findings, which indicates the urgent need for a risk-stratification
model to differentiate these patients. Despite the absence of validation
cohorts to confirm the performance accuracy of the differential
diagnosis model, the performance of this risk-stratification model is
satisfactory and accurate (based on AUCs >0.82 in both the
development and validation cohorts). The package-based prediction
calculator can be utilized by clinicians to perform risk stratification
of asymptomatic, non-severe presymptomatic, and severe presymptomatic
COVID-19 patients based on 5–10 commonly evaluated indicators. This
makes the package-based calculator easy to utilize, facilitating
clinical resource reallocation, such as for ICU beds and ventilators.
Furthermore, the risk-stratification model can be utilized for
symptomatic and presymptomatic COVID-19 patients, which implies that the
indicators identified in the analysis possibly have intrinsic
associations with the development of COVID-19. A high BNP level is
related to acute respiratory distress syndrome, sepsis, and congestive
heart failure and contributes to higher mortality in patients with
pneumonia (21, 22). PCT, as an indicator for bacterial coinfection, is
approved by the US Food and Drug Administration to guide antibiotic
treatment for suspected lower respiratory tract infections (23).
Previously published literature reviews and meta-analyses have indicated
that the results of laboratory tests for PCT could indicate progression
of COVID-19 (8, 24, 25), which greatly supports our results. There is
limited information about the frequency and bacterial spectrum of
pulmonary coinfections and superinfections in COVID-19 patients (26);
however, our analysis strengthens the vital role of BNP and PCT
evaluation in the clinic to verify the possibility of coinfections
associated with SARS-CoV-2 infection.
Functional CD8+ T-cell exhaustion contributes to the progression of
SARS-CoV-2 infection (27, 28). As indicated in a previous report, loss
of function occurs hierarchically in exhausted CD8+ T-cells (29). In the
initial stage, CD8+ T-cells are unable to produce IL-2. During the
intermediate stage, CD8+ T-cells may lose the ability to produce tumor
necrosis factor (TNF). In the severe stage, CD8+ T-cells completely lose
the ability to produce large amounts of IFNγ or there may be a physical
deletion of virus-specific CD8+ T-cells. Based on the single-cell
sequencing data, our re-analysis indicates that SARS-CoV-2 can induce
intermediate-stage CD8+ T-cell exhaustion involving a lack of IL-2 and
TNFα production but with limited IFNγ production (Fig. 5F), which may be
mediated by LAG-3. Because LAG-3 cross-linking can inhibit IL-2, TNFα,
and IFNγ production and T-cell proliferation (30). Therefore,
LAG3+IFNγ+CD8+ T-cells may be the predominant phenotype showing
exhaustion and may need to be further investigated to elucidate the
pathogenic clinical severity of COVID-19.
As the retrieved and analyzed clinical data were based on all data from
the clinical practice and were not derived from random selection, the
data are deemed to be descriptive to some extent. For a comparable data
analysis, baseline normalization by the propensity score matching method
was performed, and this strategy helped reveal more information from
these data. We were unable to verify the accuracy of the model due to
lack of data from another research center, resulting in only partial
validation of our risk-stratification model.