Discussion
In this study, we aimed to evaluate the diagnostic performance of
Standard ™Q COVID-19 Ag test, a recently commercialized RAT in Egypt and
to investigate the factors that could influence test performance. With
an overall accuracy of 75.9%, this RAT showed low performance as
compared to the RT-qPCR. Combining laboratory parameters with RAT did
not enhance RAT predictive accuracy. To the best of our knowledge, this
study deems the first one in Egypt that provides detailed evaluation of
the diagnostic performance of a RAT against RT-qPCR on Egyptian
subjects.
Given that the ideal RAT should have a sensitivity > 95%
and a specificity of 100% (Nalumansi et
al., 2020), The Standard ™Q COVID-19 Ag studied here showed less than
optimal performance. The observed 78.2% sensitivity means that this RAT
test has falsely considered 21.7% (15/69) of the COVID-19 true positive
cases as non-infected. Similarly, a specificity of 64.2% means that
this RAT has falsely considered 35.7% (5/14) of the COVID-19 negative
subjects as positive. The lack of sensitivity of the RAT could lead to
disease dissemination among population if the missed patients are
infectious. Actually, an RT-qPCR-positive subject does not necessarily
means that he/she is infectious. Our data indicated the majority of the
15 false negative patients by RAT had low viral load, although being
symptomatic (Table S1) . Since we did not isolate live viruses
from those patients, their infectiousness remains unknown and the
presence of symptoms does not imply that the person is infectious as
shown previously for COVID-19 patients with low viral load
(Singanayagam et al., 2020). Symptoms in
those groups could be attributed to virus-induced end-organ damage,
which was obvious in their radiological findings, rather than presence
of replicating virus. On the other side, the lack of specificity could
lead to extra cost due to wrong decision of isolation or advising
needless therapy. At the time of writing this paper, we are analyzing
clinical data from big Egyptian cohort, which might solidify some of
these conclusions. The current RAT showed higher sensitivity and lower
specificity when it was applied in 262 Ugandan subjects
(Nalumansi et al., 2020). This RAT had
higher sensitivity (98.3%) and higher specificity (98.7%) than our
results when applied on 454 subjects from Thailand
(Chaimayo et al., 2020). This indicates
that test results might be race/ethnicity- dependent. Our data added to
the already known diversity in RATs result. The sensitivity of the
current RAT was higher than that obtained by BIOCREDIT COVID-19 Ag test
(43.1%) applied on nasal swabs in Egypt
(A. M. Abdelrazik et al., 2020). In two
independent studies, Ag Respi-Strip (Coris Bioconcept, Gembloux,
Nelgium) exhibited specificity of 100% and sensitivity ranged from
30-50% (Lambert-Niclot et al., 2020;
Scohy et al., 2020). Fluorescence RAT
done on 239 participants in China showed low sensitivity of 68% and
maximum specificity (100%) (Diao et al.,
2020). The fluorescence immunochromatographic assay produced 93.9%
sensitivity and 100% specificity when used on 127 subjects from Chile
(Porte et al., 2020). The differences in
test performance could be due to variabilities in the participant’s
clinical features, sample type and processing, PCR protocol and viral
load in samples. When evaluating any RAT performance, it is worth noting
that misdiagnosis of COVID-19 patients could be due to the difference
between the virus strain contained in the sample and the one against
which the antibodies coated in the RAT were raised. This is highlighted
knowing that Standard ™ Q COVID-19 Ag was designed to detect the
original WUHAN-01 strain and that mutation rate is high in the
antibody-target SARS-CoV-2 N protein
(Rahman et al., 2020). It is therefore
recommended to continuously evaluate and update the validity of this and
other RAT when applied in different communities that might experience
other SARS-CoV-2 strains especially with the beginning of second wave.
Our data showed that the Standard ™ Q COVID-19 Ag was more sensitive and
more accurate in patients with high viral load than those with low viral
load. Similar results were shown for the same assay in Uganda
(Nalumansi et al., 2020) and for other
qualitative (Abeer Mohamed Abdelrazik,
Shahira Morsy Elshafie, & Hossam M Abdelaziz, 2020;
Lambert-Niclot et al., 2020;
Porte et al., 2020) and quantitative
(Akashi et al., 2019) RATs. In parallel,
RAT showed the highest sensitivity and accuracy in the samples collected
during the first week post-symptoms and sampling time was the top
important feature that determines the results of both RT-qPCR and RAT as
revealed by the our random forest classification (Figure S1) .
These findings support previous reports that showed a 14% decrease in
sensitivity of fluorescence immunochromatographic assay when performed
on samples collected between 8-12 days post-symptoms relative to earlier
samples (Porte et al., 2020). It is
already known that SARS-CoV-2 load in upper respiratory tract samples
often peak few days after symptom onset
(Wölfel et al., 2020;
Zou et al., 2020). This complement our
results since 17 out of the 28 subjects with RAT positive and strong
RT-qPCR were sampled between 0-7 days post-symptoms. Taken together,
this suggests a triple relationship between high diagnostic performance
of RAT, high viral load in the sample and the early time of sampling
post-symptoms and highlights the clinical utility of this RAT in
severely affected patients with high viral load and at early stages of
COVID-19 infection.
Many studies are there that analyzed the performance of RAT, yet limited
studies correlate patient’s clinical and radiological features to the
RAT performance. Our observation that Standard™ Q COVID-19 Ag test has
higher sensitivity and accuracy in symptomatic than in asymptomatic
subjects is in line with previous study done on 3410 Italian patients
using the same assay, where the RAT’s sensitivity declined from 89.9%
in symptomatic subjects to 50% in the asymptomatic ones. As evidenced
by one patient in our study, our analysis suggests that Standard™ Q
COVID-19 Ag test could detect, with very faint line, RT-qPCR negative
subjects who are asymptomatic and had no radiological alteration. This
highlights the importance of subjecting asymptomatic suspected
individuals to the test and that this RAT might be sensitive enough to
truly detect asymptomatic carriers, who likely account for significant
portion of disease transmission events among humans
(Cloutier et al., 2021). Our data indicate
the low clinical value of radiological analyses in determining COVID-19
patients relative to RT-qPCR or even the RAT since all participants who
had no radiological alteration proved positive by RT-qPCR (4 of them
have high Ct value > 25) and five of them were also
positive by RAT. Obviously, additional analyses are needed to generalize
these observations.
From a diagnosis point of view, it might be useful to combine RAT
results with laboratory measurements in patient’s blood in pursuit of
enhancing RAT performance, particularly when RAT is the only assay
available. The machine learning approach employed here enabled us to
test this hypothesis. The best-obtained and validated model (formed of
RAT plus HB and urea) gave a predictive accuracy of 59.3% and other
models with more features, that are COVID-19 related, gave even lower
accuracy that this one. This analysis scheme suggests that using
laboratory parameter might not afford the desired improvement in
diagnostic performance of the RAT studied here, and possibly other RAT.
Another point to consider for clinicians is the parameters that should
be taken into consideration when performing the test given the
differences in the results between RT-qPCR and RAT. The vast difference
between determinants of both assays (as shown by random forest
classification model) suggests that the differences between the results
of both assay have reflected on the parameters to be considered as
determinants for the assay.
We acknowledge that this analysis is limited by some factors that should
be taken into account in upcoming studies: the small sample size and the
unavailability of some participant’s data were due to logistic hurdles
during the pandemic time. Obviously, additional samples are required for
evaluating this RAT. The limited fund at the time of the study and
accelerated pressure for obtaining results precluded us from evaluating
the influence of sample processing procedures on the RAT accuracy, such
an important factor that might alter test results. We do believe that
the strength of this study lies in its performance in real-life
settings. We were able to link viral load, sampling time, clinical
symptoms and laboratory parameters to the assay results and to test, by
machine learning approach, the effect of measuring blood parameters on
enhancing RAT performance.