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.